TY  - JOUR
AU  - Saqr, Mohammed
AU  - Jovanović, Jelena
AU  - Viberg, Olga
AU  - Gašević, Dragan
PY  - 2022
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/2302
AB  - Predictors of student academic success do not always replicate well across different learning designs, subject areas, or educational institutions. This suggests that characteristics of a particular discipline and learning design have to be carefully considered when creating predictive models in order to scale up learning analytics. This study aimed to examine if and to what extent frequently used predictors of study success are portable across a homogenous set of courses. The research was conducted in an integrated blended problem-based curriculum with trace data (n = 2,385 students) from 50 different course offerings across four academic years. We applied the statistical method of single paper meta-analysis to combine correlations of several indicators with students' success. Total activity and the forum indicators exhibited the highest prediction intervals, where the former represented proxies of the overall engagement with online tasks, and the latter with online collaborative learning activities. Indicators of lecture reading (frequency of lecture view) showed statistically insignificant prediction intervals and, therefore, are less likely to be portable across course offerings. The findings show moderate amounts of variability both within iterations of the same course and across courses. The results suggest that the use of the meta-analytic statistical method for the examination of study success indicators across courses with similar learning design and subject area can offer valuable quantitative means for the identification of predictors that reasonably well replicate and consequently can be reliably portable in the future.
PB  - Routledge Journals, Taylor & Francis Ltd, Abingdon
T2  - Studies in Higher Education
T1  - Is there order in the mess? A single paper meta-analysis approach to identification of predictors of success in learning analytics
EP  - 2391
IS  - 12
SP  - 2370
VL  - 47
DO  - 10.1080/03075079.2022.2061450
UR  - conv_2648
ER  - 

@article{
author = "Saqr, Mohammed and Jovanović, Jelena and Viberg, Olga and Gašević, Dragan",
year = "2022",
abstract = "Predictors of student academic success do not always replicate well across different learning designs, subject areas, or educational institutions. This suggests that characteristics of a particular discipline and learning design have to be carefully considered when creating predictive models in order to scale up learning analytics. This study aimed to examine if and to what extent frequently used predictors of study success are portable across a homogenous set of courses. The research was conducted in an integrated blended problem-based curriculum with trace data (n = 2,385 students) from 50 different course offerings across four academic years. We applied the statistical method of single paper meta-analysis to combine correlations of several indicators with students' success. Total activity and the forum indicators exhibited the highest prediction intervals, where the former represented proxies of the overall engagement with online tasks, and the latter with online collaborative learning activities. Indicators of lecture reading (frequency of lecture view) showed statistically insignificant prediction intervals and, therefore, are less likely to be portable across course offerings. The findings show moderate amounts of variability both within iterations of the same course and across courses. The results suggest that the use of the meta-analytic statistical method for the examination of study success indicators across courses with similar learning design and subject area can offer valuable quantitative means for the identification of predictors that reasonably well replicate and consequently can be reliably portable in the future.",
publisher = "Routledge Journals, Taylor & Francis Ltd, Abingdon",
journal = "Studies in Higher Education",
title = "Is there order in the mess? A single paper meta-analysis approach to identification of predictors of success in learning analytics",
pages = "2391-2370",
number = "12",
volume = "47",
doi = "10.1080/03075079.2022.2061450",
url = "conv_2648"
}

Saqr, M., Jovanović, J., Viberg, O.,& Gašević, D.. (2022). Is there order in the mess? A single paper meta-analysis approach to identification of predictors of success in learning analytics. in Studies in Higher Education
Routledge Journals, Taylor & Francis Ltd, Abingdon., 47(12), 2370-2391.
https://doi.org/10.1080/03075079.2022.2061450
conv_2648

Saqr M, Jovanović J, Viberg O, Gašević D. Is there order in the mess? A single paper meta-analysis approach to identification of predictors of success in learning analytics. in Studies in Higher Education. 2022;47(12):2370-2391.
doi:10.1080/03075079.2022.2061450
conv_2648 .

Saqr, Mohammed, Jovanović, Jelena, Viberg, Olga, Gašević, Dragan, "Is there order in the mess? A single paper meta-analysis approach to identification of predictors of success in learning analytics" in Studies in Higher Education, 47, no. 12 (2022):2370-2391,
https://doi.org/10.1080/03075079.2022.2061450 .,
conv_2648 .

TY  - JOUR
AU  - Fan, Yizhou
AU  - Jovanović, Jelena
AU  - Saint, John
AU  - Jiang, Yuhang
AU  - Wang, Qiong
AU  - Gašević, Dragan
PY  - 2022
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/2299
AB  - Massive Open Online Courses (MOOCs) have already shown a great potential to be used as an alternative model for teacher professional development (TPD). Not only do MOOCs offer relevant content and activities, but they also provide opportunities for strengthening the skills for self regulated learning of teachers as life-long learners. In this study, we focused on a unique subgroup of MOOC learners - retakers who take the same TPD MOOC multiple times. In the empirical study reported in this paper, we examined what learning strategies they choose when taking a TPD MOOC and the extent to which they are able to adapt their learning strategies to improve their performance in the subsequent attempts. By using learning analytic methods, we detected five learning strategies and eight major forms of strategy change adapted by MOOC retakers. We found that two strategy changes (from Content-Oriented Strategy to Intensive-Thorough Strategy or Balanced Strategy) with associated with significantly higher performance in the subsequent attempts compared to no change in strategy. Our findings have implications for MOOC instructors and providers about the ways they can support MOOC retakers. Our findings also suggest that MOOCs are not only valuable TPD resources for teachers, but they also have unique opportunities for strengthening the self-regulation skills of teacher learners as the "by-products of learning".
PB  - Pergamon-Elsevier Science Ltd, Oxford
T2  - Computers & Education
T1  - Revealing the regulation of learning strategies of MOOC retakers: A learning analytic study
VL  - 178
DO  - 10.1016/j.compedu.2021.104404
UR  - conv_2651
ER  - 

@article{
author = "Fan, Yizhou and Jovanović, Jelena and Saint, John and Jiang, Yuhang and Wang, Qiong and Gašević, Dragan",
year = "2022",
abstract = "Massive Open Online Courses (MOOCs) have already shown a great potential to be used as an alternative model for teacher professional development (TPD). Not only do MOOCs offer relevant content and activities, but they also provide opportunities for strengthening the skills for self regulated learning of teachers as life-long learners. In this study, we focused on a unique subgroup of MOOC learners - retakers who take the same TPD MOOC multiple times. In the empirical study reported in this paper, we examined what learning strategies they choose when taking a TPD MOOC and the extent to which they are able to adapt their learning strategies to improve their performance in the subsequent attempts. By using learning analytic methods, we detected five learning strategies and eight major forms of strategy change adapted by MOOC retakers. We found that two strategy changes (from Content-Oriented Strategy to Intensive-Thorough Strategy or Balanced Strategy) with associated with significantly higher performance in the subsequent attempts compared to no change in strategy. Our findings have implications for MOOC instructors and providers about the ways they can support MOOC retakers. Our findings also suggest that MOOCs are not only valuable TPD resources for teachers, but they also have unique opportunities for strengthening the self-regulation skills of teacher learners as the "by-products of learning".",
publisher = "Pergamon-Elsevier Science Ltd, Oxford",
journal = "Computers & Education",
title = "Revealing the regulation of learning strategies of MOOC retakers: A learning analytic study",
volume = "178",
doi = "10.1016/j.compedu.2021.104404",
url = "conv_2651"
}

Fan, Y., Jovanović, J., Saint, J., Jiang, Y., Wang, Q.,& Gašević, D.. (2022). Revealing the regulation of learning strategies of MOOC retakers: A learning analytic study. in Computers & Education
Pergamon-Elsevier Science Ltd, Oxford., 178.
https://doi.org/10.1016/j.compedu.2021.104404
conv_2651

Fan Y, Jovanović J, Saint J, Jiang Y, Wang Q, Gašević D. Revealing the regulation of learning strategies of MOOC retakers: A learning analytic study. in Computers & Education. 2022;178.
doi:10.1016/j.compedu.2021.104404
conv_2651 .

Fan, Yizhou, Jovanović, Jelena, Saint, John, Jiang, Yuhang, Wang, Qiong, Gašević, Dragan, "Revealing the regulation of learning strategies of MOOC retakers: A learning analytic study" in Computers & Education, 178 (2022),
https://doi.org/10.1016/j.compedu.2021.104404 .,
conv_2651 .

TY  - JOUR
AU  - Jovanović, Jelena
AU  - Saqr, Mohammed
AU  - Joksimović, Srećko
AU  - Gašević, Dragan
PY  - 2021
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/2168
AB  - Predictive modelling of academic success and retention has been a key research theme in Learning Analytics. While the initial work on predictive modelling was focused on the development of general predictive models, portable across different learning settings, later studies demonstrated the drawbacks of not considering the specificities of course design and disciplinary context. This study builds on the methods and findings of related earlier studies to further explore factors predictive of learners' academic success in blended learning. In doing so, it differentiates itself by (i) relying on a larger and homogeneous course sample (15 courses, 50 course offerings in total), and (ii) considering both internal and external conditions as factors affecting the learning process. We apply mixed effect linear regression models, to examine: i) to what extent indicators of students' online learning behaviour can explain the variability in the final grades, and ii) to what extent that variability is attributable to the course and students' internal conditions, not captured by the logged data. Having examined different types of behaviour indicators (e.g., indicators of the overall activity level, those indicative of regularity of study, etc), we found little difference, if any, in their predictive power. Our results further indicate that a low proportion of variance is explained by the behaviour-based indicators, while a significant portion of variability stems from the learners' internal conditions. Hence, when variability in external conditions is largely controlled for (the same institution, discipline, and nominal pedagogical model), students' internal state is the key predictor of their course performance.
PB  - Pergamon-Elsevier Science Ltd, Oxford
T2  - Computers & Education
T1  - Students matter the most in learning analytics: The effects of internal and instructional conditions in predicting academic success
VL  - 172
DO  - 10.1016/j.compedu.2021.104251
UR  - conv_2524
ER  - 

@article{
author = "Jovanović, Jelena and Saqr, Mohammed and Joksimović, Srećko and Gašević, Dragan",
year = "2021",
abstract = "Predictive modelling of academic success and retention has been a key research theme in Learning Analytics. While the initial work on predictive modelling was focused on the development of general predictive models, portable across different learning settings, later studies demonstrated the drawbacks of not considering the specificities of course design and disciplinary context. This study builds on the methods and findings of related earlier studies to further explore factors predictive of learners' academic success in blended learning. In doing so, it differentiates itself by (i) relying on a larger and homogeneous course sample (15 courses, 50 course offerings in total), and (ii) considering both internal and external conditions as factors affecting the learning process. We apply mixed effect linear regression models, to examine: i) to what extent indicators of students' online learning behaviour can explain the variability in the final grades, and ii) to what extent that variability is attributable to the course and students' internal conditions, not captured by the logged data. Having examined different types of behaviour indicators (e.g., indicators of the overall activity level, those indicative of regularity of study, etc), we found little difference, if any, in their predictive power. Our results further indicate that a low proportion of variance is explained by the behaviour-based indicators, while a significant portion of variability stems from the learners' internal conditions. Hence, when variability in external conditions is largely controlled for (the same institution, discipline, and nominal pedagogical model), students' internal state is the key predictor of their course performance.",
publisher = "Pergamon-Elsevier Science Ltd, Oxford",
journal = "Computers & Education",
title = "Students matter the most in learning analytics: The effects of internal and instructional conditions in predicting academic success",
volume = "172",
doi = "10.1016/j.compedu.2021.104251",
url = "conv_2524"
}

Jovanović, J., Saqr, M., Joksimović, S.,& Gašević, D.. (2021). Students matter the most in learning analytics: The effects of internal and instructional conditions in predicting academic success. in Computers & Education
Pergamon-Elsevier Science Ltd, Oxford., 172.
https://doi.org/10.1016/j.compedu.2021.104251
conv_2524

Jovanović J, Saqr M, Joksimović S, Gašević D. Students matter the most in learning analytics: The effects of internal and instructional conditions in predicting academic success. in Computers & Education. 2021;172.
doi:10.1016/j.compedu.2021.104251
conv_2524 .

Jovanović, Jelena, Saqr, Mohammed, Joksimović, Srećko, Gašević, Dragan, "Students matter the most in learning analytics: The effects of internal and instructional conditions in predicting academic success" in Computers & Education, 172 (2021),
https://doi.org/10.1016/j.compedu.2021.104251 .,
conv_2524 .

TY  - JOUR
AU  - Fincham, Ed
AU  - Rozemberczki, Benedek
AU  - Kovanović, Vitomir
AU  - Joksimović, Srećko
AU  - Jovanović, Jelena
AU  - Gašević, Dragan
PY  - 2021
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/2228
AB  - In this article, we empirically validate Tinto's Student Integration model, in particular, the predictions the model makes regarding both students' academic outcomes and their dropout decisions. In doing so, we analyze three decades' worth of student enrollments at an Australian university and present a novel methodological approach using graph embedding techniques to capture both structural and neighborhood-based features of the co-enrollment network. In keeping with Tinto's model, we find that not only do these embedded representations of students' social network predict their final grade point average (GPA), but also are able to successfully classify students who dropout. Our results show that these embedded representations of a student's social network can achieve F1-scores of up to 0.79 when classifying dropout and explain up to 10% of the variance in student's final GPA. When controlling for a small set of covariates and variables common to the literature, this performance increases to 0.83 and 24%, respectively. Furthermore, the performance of these methods is robust to both changes in their parameterization and to corruption of the underlying social networks. Importantly, this implies that hyperparameters may be selected to reduce the computational demands of this method without loss of predictive power. The novelty of this method, and its ability to identify student dropout, merits further investigation to preemptively identify at-risk students.
PB  - IEEE Computer Soc, Los Alamitos
T2  - IEEE Transactions on Learning Technologies
T1  - Persistence and Performance in Co-Enrollment Network Embeddings: An Empirical Validation of Tinto's Student Integration Model
EP  - 121
IS  - 1
SP  - 106
VL  - 14
DO  - 10.1109/TLT.2021.3059362
UR  - conv_2471
ER  - 

@article{
author = "Fincham, Ed and Rozemberczki, Benedek and Kovanović, Vitomir and Joksimović, Srećko and Jovanović, Jelena and Gašević, Dragan",
year = "2021",
abstract = "In this article, we empirically validate Tinto's Student Integration model, in particular, the predictions the model makes regarding both students' academic outcomes and their dropout decisions. In doing so, we analyze three decades' worth of student enrollments at an Australian university and present a novel methodological approach using graph embedding techniques to capture both structural and neighborhood-based features of the co-enrollment network. In keeping with Tinto's model, we find that not only do these embedded representations of students' social network predict their final grade point average (GPA), but also are able to successfully classify students who dropout. Our results show that these embedded representations of a student's social network can achieve F1-scores of up to 0.79 when classifying dropout and explain up to 10% of the variance in student's final GPA. When controlling for a small set of covariates and variables common to the literature, this performance increases to 0.83 and 24%, respectively. Furthermore, the performance of these methods is robust to both changes in their parameterization and to corruption of the underlying social networks. Importantly, this implies that hyperparameters may be selected to reduce the computational demands of this method without loss of predictive power. The novelty of this method, and its ability to identify student dropout, merits further investigation to preemptively identify at-risk students.",
publisher = "IEEE Computer Soc, Los Alamitos",
journal = "IEEE Transactions on Learning Technologies",
title = "Persistence and Performance in Co-Enrollment Network Embeddings: An Empirical Validation of Tinto's Student Integration Model",
pages = "121-106",
number = "1",
volume = "14",
doi = "10.1109/TLT.2021.3059362",
url = "conv_2471"
}

Fincham, E., Rozemberczki, B., Kovanović, V., Joksimović, S., Jovanović, J.,& Gašević, D.. (2021). Persistence and Performance in Co-Enrollment Network Embeddings: An Empirical Validation of Tinto's Student Integration Model. in IEEE Transactions on Learning Technologies
IEEE Computer Soc, Los Alamitos., 14(1), 106-121.
https://doi.org/10.1109/TLT.2021.3059362
conv_2471

Fincham E, Rozemberczki B, Kovanović V, Joksimović S, Jovanović J, Gašević D. Persistence and Performance in Co-Enrollment Network Embeddings: An Empirical Validation of Tinto's Student Integration Model. in IEEE Transactions on Learning Technologies. 2021;14(1):106-121.
doi:10.1109/TLT.2021.3059362
conv_2471 .

Fincham, Ed, Rozemberczki, Benedek, Kovanović, Vitomir, Joksimović, Srećko, Jovanović, Jelena, Gašević, Dragan, "Persistence and Performance in Co-Enrollment Network Embeddings: An Empirical Validation of Tinto's Student Integration Model" in IEEE Transactions on Learning Technologies, 14, no. 1 (2021):106-121,
https://doi.org/10.1109/TLT.2021.3059362 .,
conv_2471 .

TY  - JOUR
AU  - Matcha, Wannisa
AU  - Gašević, Dragan
AU  - Uzir, Nora'ayu Ahmad
AU  - Jovanović, Jelena
AU  - Pardo, Abelardo
AU  - Lim, Lisa
AU  - Maldonado-Mahauad, Jorge
AU  - Gentili, Sheridan
AU  - Perez-Sanagustin, Mar
AU  - Tsai, Yi-Shan
PY  - 2020
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/2084
AB  - Generalizability of the value of methods based on learning analytics remains one of the big challenges in the field of learning analytics. One approach to testing generalizability of a method is to apply it consistently in different learning contexts. This study extends a previously published work by examining the generalizability of a learning analytics method proposed for detecting learning tactics and strategies from trace data. The method was applied to the datasets collected in three different course designs and delivery modalities, including flipped classroom, blended learning, and massive open online course. The proposed method combines process mining and sequence analysis. The detected learning strategies are explored in terms of their association with academic performance. The results indicate the applicability of the proposed method across different learning contexts. Moreover, the findings contribute to the understanding of the learning tactics and strategies identified in the trace data: learning tactics proved to be responsive to the course design, whereas learning strategies were found to be more sensitive to the delivery modalities than to the course design. These findings, well aligned with self-regulated learning theory, highlight the association of learning contexts with the choice of learning tactics and strategies.
PB  - Soc Learning Analytics Research-Solar, Beaumont
T2  - Journal of Learning Analytics
T1  - Analytics of Learning Strategies: Role of Course Design and Delivery Modality
EP  - 71
IS  - 2
SP  - 45
VL  - 7
DO  - 10.18608/jla.2020.72.3
UR  - conv_2382
ER  - 

@article{
author = "Matcha, Wannisa and Gašević, Dragan and Uzir, Nora'ayu Ahmad and Jovanović, Jelena and Pardo, Abelardo and Lim, Lisa and Maldonado-Mahauad, Jorge and Gentili, Sheridan and Perez-Sanagustin, Mar and Tsai, Yi-Shan",
year = "2020",
abstract = "Generalizability of the value of methods based on learning analytics remains one of the big challenges in the field of learning analytics. One approach to testing generalizability of a method is to apply it consistently in different learning contexts. This study extends a previously published work by examining the generalizability of a learning analytics method proposed for detecting learning tactics and strategies from trace data. The method was applied to the datasets collected in three different course designs and delivery modalities, including flipped classroom, blended learning, and massive open online course. The proposed method combines process mining and sequence analysis. The detected learning strategies are explored in terms of their association with academic performance. The results indicate the applicability of the proposed method across different learning contexts. Moreover, the findings contribute to the understanding of the learning tactics and strategies identified in the trace data: learning tactics proved to be responsive to the course design, whereas learning strategies were found to be more sensitive to the delivery modalities than to the course design. These findings, well aligned with self-regulated learning theory, highlight the association of learning contexts with the choice of learning tactics and strategies.",
publisher = "Soc Learning Analytics Research-Solar, Beaumont",
journal = "Journal of Learning Analytics",
title = "Analytics of Learning Strategies: Role of Course Design and Delivery Modality",
pages = "71-45",
number = "2",
volume = "7",
doi = "10.18608/jla.2020.72.3",
url = "conv_2382"
}

Matcha, W., Gašević, D., Uzir, N. A., Jovanović, J., Pardo, A., Lim, L., Maldonado-Mahauad, J., Gentili, S., Perez-Sanagustin, M.,& Tsai, Y.. (2020). Analytics of Learning Strategies: Role of Course Design and Delivery Modality. in Journal of Learning Analytics
Soc Learning Analytics Research-Solar, Beaumont., 7(2), 45-71.
https://doi.org/10.18608/jla.2020.72.3
conv_2382

Matcha W, Gašević D, Uzir NA, Jovanović J, Pardo A, Lim L, Maldonado-Mahauad J, Gentili S, Perez-Sanagustin M, Tsai Y. Analytics of Learning Strategies: Role of Course Design and Delivery Modality. in Journal of Learning Analytics. 2020;7(2):45-71.
doi:10.18608/jla.2020.72.3
conv_2382 .

Matcha, Wannisa, Gašević, Dragan, Uzir, Nora'ayu Ahmad, Jovanović, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan, "Analytics of Learning Strategies: Role of Course Design and Delivery Modality" in Journal of Learning Analytics, 7, no. 2 (2020):45-71,
https://doi.org/10.18608/jla.2020.72.3 .,
conv_2382 .

TY  - CONF
AU  - Uzir, Nora'ayu Ahmad
AU  - Gašević, Dragan
AU  - Jovanović, Jelena
AU  - Matcha, Wannisa
AU  - Lim, Lisa-Angelique
AU  - Fudge, Anthea
PY  - 2020
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/2087
AB  - This paper reports on the findings of a study that proposed a novel learning analytics methodology that combines three complimentary techniques - agglomerative hierarchical clustering, epistemic network analysis, and process mining. The methodology allows for identification and interpretation of self-regulated learning in terms of the use of learning strategies. The main advantage of the new technique over the existing ones is that it combines the time management and learning tactic dimensions of learning strategies, which are typically studied in isolation. The new technique allows for novel insights into learning strategies by studying the frequency of, strength of connections between, and ordering and time of execution of time management and learning tactics. The technique was validated in a study that was conducted on the trace data of first-year undergraduate students who were enrolled into two consecutive offerings (N-2017 = 250 and N-2018 = 232) of a course at an Australian university. The application of the proposed technique identified four strategy groups derived from three distinct time management tactics and five learning tactics. The tactics and strategies identified with the technique were correlated with academic performance and were interpreted according to the established theories and practices of self-regulated learning.
PB  - Assoc Computing Machinery, New York
C3  - LAK 20: the Tenth International Conference on Learning Analytics & Knowledge
T1  - Analytics of Time Management and Learning Strategies for Effective Online Learning in Blended Environments
EP  - 401
SP  - 392
DO  - 10.1145/3375462.3375493
UR  - conv_2369
ER  - 

@conference{
author = "Uzir, Nora'ayu Ahmad and Gašević, Dragan and Jovanović, Jelena and Matcha, Wannisa and Lim, Lisa-Angelique and Fudge, Anthea",
year = "2020",
abstract = "This paper reports on the findings of a study that proposed a novel learning analytics methodology that combines three complimentary techniques - agglomerative hierarchical clustering, epistemic network analysis, and process mining. The methodology allows for identification and interpretation of self-regulated learning in terms of the use of learning strategies. The main advantage of the new technique over the existing ones is that it combines the time management and learning tactic dimensions of learning strategies, which are typically studied in isolation. The new technique allows for novel insights into learning strategies by studying the frequency of, strength of connections between, and ordering and time of execution of time management and learning tactics. The technique was validated in a study that was conducted on the trace data of first-year undergraduate students who were enrolled into two consecutive offerings (N-2017 = 250 and N-2018 = 232) of a course at an Australian university. The application of the proposed technique identified four strategy groups derived from three distinct time management tactics and five learning tactics. The tactics and strategies identified with the technique were correlated with academic performance and were interpreted according to the established theories and practices of self-regulated learning.",
publisher = "Assoc Computing Machinery, New York",
journal = "LAK 20: the Tenth International Conference on Learning Analytics & Knowledge",
title = "Analytics of Time Management and Learning Strategies for Effective Online Learning in Blended Environments",
pages = "401-392",
doi = "10.1145/3375462.3375493",
url = "conv_2369"
}

Uzir, N. A., Gašević, D., Jovanović, J., Matcha, W., Lim, L.,& Fudge, A.. (2020). Analytics of Time Management and Learning Strategies for Effective Online Learning in Blended Environments. in LAK 20: the Tenth International Conference on Learning Analytics & Knowledge
Assoc Computing Machinery, New York., 392-401.
https://doi.org/10.1145/3375462.3375493
conv_2369

Uzir NA, Gašević D, Jovanović J, Matcha W, Lim L, Fudge A. Analytics of Time Management and Learning Strategies for Effective Online Learning in Blended Environments. in LAK 20: the Tenth International Conference on Learning Analytics & Knowledge. 2020;:392-401.
doi:10.1145/3375462.3375493
conv_2369 .

Uzir, Nora'ayu Ahmad, Gašević, Dragan, Jovanović, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea, "Analytics of Time Management and Learning Strategies for Effective Online Learning in Blended Environments" in LAK 20: the Tenth International Conference on Learning Analytics & Knowledge (2020):392-401,
https://doi.org/10.1145/3375462.3375493 .,
conv_2369 .

TY  - JOUR
AU  - Joksimović, Srećko
AU  - Jovanović, Jelena
AU  - Kovanović, Vitomir
AU  - Gašević, Dragan
AU  - Milikić, Nikola
AU  - Zouaq, Amal
AU  - Van Staalduinen, Jan Paul
PY  - 2020
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/2056
AB  - Learning in computer-mediated setting represents a complex, multidimensional process. This complexity calls for a comprehensive analytical approach that would allow for understanding of various dimensions of learner generated discourse and the structure of the underlying social interactions. Current research, however, primarily focuses on manual or, more recently, supervised methods for discourse analysis. Moreover, discourse and social structures are typically analyzed separately without the use of computational methods that can offer a holistic perspective. This paper proposes an approach that addresses these two challenges, first, by using an unsupervised machine learning approach to extract speech acts as representations of knowledge construction processes and finds transition probabilities between speech acts across different messages, and second, by integrating the use of discovered speech acts to explain the formation of social ties and predicting course outcomes. We extracted six categories of speech acts from messages exchanged in discussion forums of two MOOCs and each category corresponded to knowledge construction processes from well-established theoretical models. We further showed how measures derived from discourse analysis explained the ways how social ties were created that framed emerging social networks. Multiple regression models showed that the combined use of measures derived from discourse analysis and social ties predicted learning outcomes.
PB  - IEEE Computer Soc, Los Alamitos
T2  - IEEE Transactions on Learning Technologies
T1  - Comprehensive Analysis of Discussion Forum Participation: From Speech Acts to Discussion Dynamics and Course Outcomes
EP  - 51
IS  - 1
SP  - 38
VL  - 13
DO  - 10.1109/TLT.2019.2916808
UR  - conv_2290
ER  - 

@article{
author = "Joksimović, Srećko and Jovanović, Jelena and Kovanović, Vitomir and Gašević, Dragan and Milikić, Nikola and Zouaq, Amal and Van Staalduinen, Jan Paul",
year = "2020",
abstract = "Learning in computer-mediated setting represents a complex, multidimensional process. This complexity calls for a comprehensive analytical approach that would allow for understanding of various dimensions of learner generated discourse and the structure of the underlying social interactions. Current research, however, primarily focuses on manual or, more recently, supervised methods for discourse analysis. Moreover, discourse and social structures are typically analyzed separately without the use of computational methods that can offer a holistic perspective. This paper proposes an approach that addresses these two challenges, first, by using an unsupervised machine learning approach to extract speech acts as representations of knowledge construction processes and finds transition probabilities between speech acts across different messages, and second, by integrating the use of discovered speech acts to explain the formation of social ties and predicting course outcomes. We extracted six categories of speech acts from messages exchanged in discussion forums of two MOOCs and each category corresponded to knowledge construction processes from well-established theoretical models. We further showed how measures derived from discourse analysis explained the ways how social ties were created that framed emerging social networks. Multiple regression models showed that the combined use of measures derived from discourse analysis and social ties predicted learning outcomes.",
publisher = "IEEE Computer Soc, Los Alamitos",
journal = "IEEE Transactions on Learning Technologies",
title = "Comprehensive Analysis of Discussion Forum Participation: From Speech Acts to Discussion Dynamics and Course Outcomes",
pages = "51-38",
number = "1",
volume = "13",
doi = "10.1109/TLT.2019.2916808",
url = "conv_2290"
}

Joksimović, S., Jovanović, J., Kovanović, V., Gašević, D., Milikić, N., Zouaq, A.,& Van Staalduinen, J. P.. (2020). Comprehensive Analysis of Discussion Forum Participation: From Speech Acts to Discussion Dynamics and Course Outcomes. in IEEE Transactions on Learning Technologies
IEEE Computer Soc, Los Alamitos., 13(1), 38-51.
https://doi.org/10.1109/TLT.2019.2916808
conv_2290

Joksimović S, Jovanović J, Kovanović V, Gašević D, Milikić N, Zouaq A, Van Staalduinen JP. Comprehensive Analysis of Discussion Forum Participation: From Speech Acts to Discussion Dynamics and Course Outcomes. in IEEE Transactions on Learning Technologies. 2020;13(1):38-51.
doi:10.1109/TLT.2019.2916808
conv_2290 .

Joksimović, Srećko, Jovanović, Jelena, Kovanović, Vitomir, Gašević, Dragan, Milikić, Nikola, Zouaq, Amal, Van Staalduinen, Jan Paul, "Comprehensive Analysis of Discussion Forum Participation: From Speech Acts to Discussion Dynamics and Course Outcomes" in IEEE Transactions on Learning Technologies, 13, no. 1 (2020):38-51,
https://doi.org/10.1109/TLT.2019.2916808 .,
conv_2290 .

TY  - CONF
AU  - Matcha, Wannisa
AU  - Gašević, Dragan
AU  - Jovanović, Jelena
AU  - Uzir, Nora'ayu Ahmad
AU  - Oliver, Chris W.
AU  - Murray, Andrew
AU  - Gašević, Danijela
PY  - 2020
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/2051
AB  - Studying online requires well-developed self-regulated learning skills to properly manage one's learning strategies. Learning analytics research has proposed novel methods for extracting theoretically meaningful learning strategies from trace data originating from formal learning settings (online, blended, or flipped classroom). Thus identified strategies proved to be associated with academic achievement. However, automated extraction of theoretically meaningful learning strategies from trace data in the context of massive open online courses (MOOCs) is still under-explored. Moreover, there is a lacuna in research on the relations between automatically detected strategies and the established psychological constructs. The paper reports on a study that (a) applied a state-of-the-art analytic method that combines process and sequence mining techniques to detect learning strategies from the trace data collected in a MOOC (N=1,397), and (b) explored associations of the detected strategies with academic performance and personality traits (Big Five). Four learning strategies detected with the adopted analytics method were shown to be theoretically interpretable as the wellknown approaches to learning. The results also revealed that the four detected learning strategies were predicted by conscientiousness, emotional instability, and agreeableness and were associated with academic performance. Implications for theoretical validity and practical application of analytics-detected learning strategies are also provided.
PB  - Assoc Computing Machinery, New York
C3  - LAK 20: the Tenth International Conference on Learning Analytics & Knowledge
T1  - Analytics of Learning Strategies: the Association with the Personality Traits
EP  - 160
SP  - 151
DO  - 10.1145/3375462.3375534
UR  - conv_2366
ER  - 

@conference{
author = "Matcha, Wannisa and Gašević, Dragan and Jovanović, Jelena and Uzir, Nora'ayu Ahmad and Oliver, Chris W. and Murray, Andrew and Gašević, Danijela",
year = "2020",
abstract = "Studying online requires well-developed self-regulated learning skills to properly manage one's learning strategies. Learning analytics research has proposed novel methods for extracting theoretically meaningful learning strategies from trace data originating from formal learning settings (online, blended, or flipped classroom). Thus identified strategies proved to be associated with academic achievement. However, automated extraction of theoretically meaningful learning strategies from trace data in the context of massive open online courses (MOOCs) is still under-explored. Moreover, there is a lacuna in research on the relations between automatically detected strategies and the established psychological constructs. The paper reports on a study that (a) applied a state-of-the-art analytic method that combines process and sequence mining techniques to detect learning strategies from the trace data collected in a MOOC (N=1,397), and (b) explored associations of the detected strategies with academic performance and personality traits (Big Five). Four learning strategies detected with the adopted analytics method were shown to be theoretically interpretable as the wellknown approaches to learning. The results also revealed that the four detected learning strategies were predicted by conscientiousness, emotional instability, and agreeableness and were associated with academic performance. Implications for theoretical validity and practical application of analytics-detected learning strategies are also provided.",
publisher = "Assoc Computing Machinery, New York",
journal = "LAK 20: the Tenth International Conference on Learning Analytics & Knowledge",
title = "Analytics of Learning Strategies: the Association with the Personality Traits",
pages = "160-151",
doi = "10.1145/3375462.3375534",
url = "conv_2366"
}

Matcha, W., Gašević, D., Jovanović, J., Uzir, N. A., Oliver, C. W., Murray, A.,& Gašević, D.. (2020). Analytics of Learning Strategies: the Association with the Personality Traits. in LAK 20: the Tenth International Conference on Learning Analytics & Knowledge
Assoc Computing Machinery, New York., 151-160.
https://doi.org/10.1145/3375462.3375534
conv_2366

Matcha W, Gašević D, Jovanović J, Uzir NA, Oliver CW, Murray A, Gašević D. Analytics of Learning Strategies: the Association with the Personality Traits. in LAK 20: the Tenth International Conference on Learning Analytics & Knowledge. 2020;:151-160.
doi:10.1145/3375462.3375534
conv_2366 .

Matcha, Wannisa, Gašević, Dragan, Jovanović, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W., Murray, Andrew, Gašević, Danijela, "Analytics of Learning Strategies: the Association with the Personality Traits" in LAK 20: the Tenth International Conference on Learning Analytics & Knowledge (2020):151-160,
https://doi.org/10.1145/3375462.3375534 .,
conv_2366 .

TY  - JOUR
AU  - Uzir, Nora'ayu Ahmad
AU  - Gašević, Dragan
AU  - Matcha, Wannisa
AU  - Jovanović, Jelena
AU  - Pardo, Abelardo
PY  - 2020
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/2046
AB  - This paper aims to explore time management strategies followed by students in a flipped classroom through the analysis of trace data. Specifically, an exploratory study was conducted on the dataset collected in three consecutive offerings of an undergraduate computer engineering course (N = 1,134). Trace data about activities were initially coded for the timeliness of activity completion. Such data were then analysed using agglomerative hierarchical clustering based on Ward's algorithm, first order Markov chains, and inferential statistics to (a) detect time management tactics and strategies from students' learning activities and (b) analyse the effects of personalized analytics-based feedback on time management. The results indicate that meaningful and theoretically relevant time management patterns can be detected from trace data as manifestations of students' tactics and strategies. The study also showed that time management tactics had significant associations with academic performance and were associated with different interventions in personalized analytics-based feedback.
PB  - Wiley, Hoboken
T2  - Journal of Computer Assisted Learning
T1  - Analytics of time management strategies in a flipped classroom
EP  - 88
IS  - 1
SP  - 70
VL  - 36
DO  - 10.1111/jcal.12392
UR  - conv_2257
ER  - 

@article{
author = "Uzir, Nora'ayu Ahmad and Gašević, Dragan and Matcha, Wannisa and Jovanović, Jelena and Pardo, Abelardo",
year = "2020",
abstract = "This paper aims to explore time management strategies followed by students in a flipped classroom through the analysis of trace data. Specifically, an exploratory study was conducted on the dataset collected in three consecutive offerings of an undergraduate computer engineering course (N = 1,134). Trace data about activities were initially coded for the timeliness of activity completion. Such data were then analysed using agglomerative hierarchical clustering based on Ward's algorithm, first order Markov chains, and inferential statistics to (a) detect time management tactics and strategies from students' learning activities and (b) analyse the effects of personalized analytics-based feedback on time management. The results indicate that meaningful and theoretically relevant time management patterns can be detected from trace data as manifestations of students' tactics and strategies. The study also showed that time management tactics had significant associations with academic performance and were associated with different interventions in personalized analytics-based feedback.",
publisher = "Wiley, Hoboken",
journal = "Journal of Computer Assisted Learning",
title = "Analytics of time management strategies in a flipped classroom",
pages = "88-70",
number = "1",
volume = "36",
doi = "10.1111/jcal.12392",
url = "conv_2257"
}

Uzir, N. A., Gašević, D., Matcha, W., Jovanović, J.,& Pardo, A.. (2020). Analytics of time management strategies in a flipped classroom. in Journal of Computer Assisted Learning
Wiley, Hoboken., 36(1), 70-88.
https://doi.org/10.1111/jcal.12392
conv_2257

Uzir NA, Gašević D, Matcha W, Jovanović J, Pardo A. Analytics of time management strategies in a flipped classroom. in Journal of Computer Assisted Learning. 2020;36(1):70-88.
doi:10.1111/jcal.12392
conv_2257 .

Uzir, Nora'ayu Ahmad, Gašević, Dragan, Matcha, Wannisa, Jovanović, Jelena, Pardo, Abelardo, "Analytics of time management strategies in a flipped classroom" in Journal of Computer Assisted Learning, 36, no. 1 (2020):70-88,
https://doi.org/10.1111/jcal.12392 .,
conv_2257 .

TY  - JOUR
AU  - Milikić, Nikola
AU  - Gašević, Dragan
AU  - Jovanović, Jelena
PY  - 2020
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/2024
AB  - Learning design in a massive open online course (MOOC) intends to promote creativity, autonomy, and social networked learning, amongst other things. Students in a MOOC are required to self-regulate their learning to properly self-monitor their learning process and effectiveness of the adopted learning strategies. This paper presents the results of a study among 279 students enrolled in a MOOC that was enriched with a set of scaffolding interventions for social mirroring. The mirroring interventions supported social awareness and social embeddedness of learners. Associations between the use of the interventions and microlevel self-regulated learning processes were measured and analyzed. The extent to which those associations are affected by learner demographics and motivational characteristics was also investigated. Findings show that interventions that provide students, throughout the course, with learning updates and progress of peers are associated with the students' engagement with learning tasks and applying changes in strategies for completing those tasks. Social awareness scaffold influenced more students low in need for cognition, with a higher education degree, high in performance-approach orientation and low in grit, to engage with their learning tasks, while its effect on the change in learning strategies was higher with those early and towards the end of their careers and high in performance-approach strategy. The social comparison scaffold affected more students low in mastery goal orientation and high in grit to work on their learning tasks.
PB  - IEEE Computer Soc, Los Alamitos
T2  - IEEE Transactions on Learning Technologies
T1  - Measuring Effects of Technology-Enabled Mirroring Scaffolds on Self-Regulated Learning
EP  - 163
IS  - 1
SP  - 150
VL  - 13
DO  - 10.1109/TLT.2018.2885743
UR  - conv_2291
ER  - 

@article{
author = "Milikić, Nikola and Gašević, Dragan and Jovanović, Jelena",
year = "2020",
abstract = "Learning design in a massive open online course (MOOC) intends to promote creativity, autonomy, and social networked learning, amongst other things. Students in a MOOC are required to self-regulate their learning to properly self-monitor their learning process and effectiveness of the adopted learning strategies. This paper presents the results of a study among 279 students enrolled in a MOOC that was enriched with a set of scaffolding interventions for social mirroring. The mirroring interventions supported social awareness and social embeddedness of learners. Associations between the use of the interventions and microlevel self-regulated learning processes were measured and analyzed. The extent to which those associations are affected by learner demographics and motivational characteristics was also investigated. Findings show that interventions that provide students, throughout the course, with learning updates and progress of peers are associated with the students' engagement with learning tasks and applying changes in strategies for completing those tasks. Social awareness scaffold influenced more students low in need for cognition, with a higher education degree, high in performance-approach orientation and low in grit, to engage with their learning tasks, while its effect on the change in learning strategies was higher with those early and towards the end of their careers and high in performance-approach strategy. The social comparison scaffold affected more students low in mastery goal orientation and high in grit to work on their learning tasks.",
publisher = "IEEE Computer Soc, Los Alamitos",
journal = "IEEE Transactions on Learning Technologies",
title = "Measuring Effects of Technology-Enabled Mirroring Scaffolds on Self-Regulated Learning",
pages = "163-150",
number = "1",
volume = "13",
doi = "10.1109/TLT.2018.2885743",
url = "conv_2291"
}

Milikić, N., Gašević, D.,& Jovanović, J.. (2020). Measuring Effects of Technology-Enabled Mirroring Scaffolds on Self-Regulated Learning. in IEEE Transactions on Learning Technologies
IEEE Computer Soc, Los Alamitos., 13(1), 150-163.
https://doi.org/10.1109/TLT.2018.2885743
conv_2291

Milikić N, Gašević D, Jovanović J. Measuring Effects of Technology-Enabled Mirroring Scaffolds on Self-Regulated Learning. in IEEE Transactions on Learning Technologies. 2020;13(1):150-163.
doi:10.1109/TLT.2018.2885743
conv_2291 .

Milikić, Nikola, Gašević, Dragan, Jovanović, Jelena, "Measuring Effects of Technology-Enabled Mirroring Scaffolds on Self-Regulated Learning" in IEEE Transactions on Learning Technologies, 13, no. 1 (2020):150-163,
https://doi.org/10.1109/TLT.2018.2885743 .,
conv_2291 .

TY  - CONF
AU  - Uzir, Nora'ayu Ahmad
AU  - Gašević, Dragan
AU  - Matcha, Wannisa
AU  - Jovanović, Jelena
AU  - Pardo, Abelardo
AU  - Lim, Lisa-Angelique
AU  - Gentili, Sheridan
PY  - 2019
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/1891
AB  - This paper reports the findings of a study that proposed a novel learning analytic methodology that combines process mining with cluster analysis to study time management in the context of blended and online learning. The study was conducted with first-year students (N = 241) who were enrolled in blended learning of a health science course. The study identified four distinct time management tactics and three strategies. The tactics and strategies were interpreted according to the established theoretical framework of self-regulated learning in terms of student decisions about what to study, how long to study, and how to study. The study also identified significant differences in academic performance among students who followed different time management strategies.
PB  - Springer International Publishing Ag, Cham
C3  - Transforming Learning with Meaningful Technologies, Ec-Tel 2019
T1  - Discovering Time Management Strategies in Learning Processes Using Process Mining Techniques
EP  - 569
SP  - 555
VL  - 11722
DO  - 10.1007/978-3-030-29736-7_41
UR  - conv_2377
ER  - 

@conference{
author = "Uzir, Nora'ayu Ahmad and Gašević, Dragan and Matcha, Wannisa and Jovanović, Jelena and Pardo, Abelardo and Lim, Lisa-Angelique and Gentili, Sheridan",
year = "2019",
abstract = "This paper reports the findings of a study that proposed a novel learning analytic methodology that combines process mining with cluster analysis to study time management in the context of blended and online learning. The study was conducted with first-year students (N = 241) who were enrolled in blended learning of a health science course. The study identified four distinct time management tactics and three strategies. The tactics and strategies were interpreted according to the established theoretical framework of self-regulated learning in terms of student decisions about what to study, how long to study, and how to study. The study also identified significant differences in academic performance among students who followed different time management strategies.",
publisher = "Springer International Publishing Ag, Cham",
journal = "Transforming Learning with Meaningful Technologies, Ec-Tel 2019",
title = "Discovering Time Management Strategies in Learning Processes Using Process Mining Techniques",
pages = "569-555",
volume = "11722",
doi = "10.1007/978-3-030-29736-7_41",
url = "conv_2377"
}

Uzir, N. A., Gašević, D., Matcha, W., Jovanović, J., Pardo, A., Lim, L.,& Gentili, S.. (2019). Discovering Time Management Strategies in Learning Processes Using Process Mining Techniques. in Transforming Learning with Meaningful Technologies, Ec-Tel 2019
Springer International Publishing Ag, Cham., 11722, 555-569.
https://doi.org/10.1007/978-3-030-29736-7_41
conv_2377

Uzir NA, Gašević D, Matcha W, Jovanović J, Pardo A, Lim L, Gentili S. Discovering Time Management Strategies in Learning Processes Using Process Mining Techniques. in Transforming Learning with Meaningful Technologies, Ec-Tel 2019. 2019;11722:555-569.
doi:10.1007/978-3-030-29736-7_41
conv_2377 .

Uzir, Nora'ayu Ahmad, Gašević, Dragan, Matcha, Wannisa, Jovanović, Jelena, Pardo, Abelardo, Lim, Lisa-Angelique, Gentili, Sheridan, "Discovering Time Management Strategies in Learning Processes Using Process Mining Techniques" in Transforming Learning with Meaningful Technologies, Ec-Tel 2019, 11722 (2019):555-569,
https://doi.org/10.1007/978-3-030-29736-7_41 .,
conv_2377 .

TY  - JOUR
AU  - Jovanović, Jelena
AU  - Mirriahi, Negin
AU  - Gašević, Dragan
AU  - Dawson, Shane
AU  - Pardo, Abelardo
PY  - 2019
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/1886
AB  - Flipped classroom (FC) is an active learning design requiring students to complete assigned pre-class learning activities in preparation for face-to-face sessions. Students' timely, regular, and productive engagement with the pre-class activities is considered critical for the success of the overall FC design, as these activities serve to prepare students for effective participation in face-to-face sessions. However, there is limited empirical evidence on the strength of association between students' regularity of engagement with the pre-class activities and their learning performance in a FC course. Hence, the current study uses learning trace data from three consecutive offerings of a FC course to examine students' regularity of pre-class learning activities and its association with the students' course performance. In particular, the study derives several indicators of regularity from the trace data, including indicators related to time management and those reflecting regularity in the pattern of engagement with pre-class learning activities. The association with course performance is examined by building predictive regression models with the defined indicators as features. To examine the relevance of incorporating the specificities of the instructional design in predictive models, we designed and compared two kinds of indicators: generic (i.e. course-design-agnostic) and course-design-specific indicators. The study identified several indicators of regularity of pre-class activities as significant predictors of course performance. It also demonstrated that predictive models with only generic indicators were able to explain only a small portion of the overall variability in the students' course performance, and were significantly outperformed by models that incorporated coursespecific indicators. Finally, the study findings point to the importance of assisting students in regulating their use of learning resources during class preparation activities in a FC.
PB  - Pergamon-Elsevier Science Ltd, Oxford
T2  - Computers & Education
T1  - Predictive power of regularity of pre-class activities in a flipped classroom
EP  - 168
SP  - 156
VL  - 134
DO  - 10.1016/j.compedu.2019.02.011
UR  - conv_2160
ER  - 

@article{
author = "Jovanović, Jelena and Mirriahi, Negin and Gašević, Dragan and Dawson, Shane and Pardo, Abelardo",
year = "2019",
abstract = "Flipped classroom (FC) is an active learning design requiring students to complete assigned pre-class learning activities in preparation for face-to-face sessions. Students' timely, regular, and productive engagement with the pre-class activities is considered critical for the success of the overall FC design, as these activities serve to prepare students for effective participation in face-to-face sessions. However, there is limited empirical evidence on the strength of association between students' regularity of engagement with the pre-class activities and their learning performance in a FC course. Hence, the current study uses learning trace data from three consecutive offerings of a FC course to examine students' regularity of pre-class learning activities and its association with the students' course performance. In particular, the study derives several indicators of regularity from the trace data, including indicators related to time management and those reflecting regularity in the pattern of engagement with pre-class learning activities. The association with course performance is examined by building predictive regression models with the defined indicators as features. To examine the relevance of incorporating the specificities of the instructional design in predictive models, we designed and compared two kinds of indicators: generic (i.e. course-design-agnostic) and course-design-specific indicators. The study identified several indicators of regularity of pre-class activities as significant predictors of course performance. It also demonstrated that predictive models with only generic indicators were able to explain only a small portion of the overall variability in the students' course performance, and were significantly outperformed by models that incorporated coursespecific indicators. Finally, the study findings point to the importance of assisting students in regulating their use of learning resources during class preparation activities in a FC.",
publisher = "Pergamon-Elsevier Science Ltd, Oxford",
journal = "Computers & Education",
title = "Predictive power of regularity of pre-class activities in a flipped classroom",
pages = "168-156",
volume = "134",
doi = "10.1016/j.compedu.2019.02.011",
url = "conv_2160"
}

Jovanović, J., Mirriahi, N., Gašević, D., Dawson, S.,& Pardo, A.. (2019). Predictive power of regularity of pre-class activities in a flipped classroom. in Computers & Education
Pergamon-Elsevier Science Ltd, Oxford., 134, 156-168.
https://doi.org/10.1016/j.compedu.2019.02.011
conv_2160

Jovanović J, Mirriahi N, Gašević D, Dawson S, Pardo A. Predictive power of regularity of pre-class activities in a flipped classroom. in Computers & Education. 2019;134:156-168.
doi:10.1016/j.compedu.2019.02.011
conv_2160 .

Jovanović, Jelena, Mirriahi, Negin, Gašević, Dragan, Dawson, Shane, Pardo, Abelardo, "Predictive power of regularity of pre-class activities in a flipped classroom" in Computers & Education, 134 (2019):156-168,
https://doi.org/10.1016/j.compedu.2019.02.011 .,
conv_2160 .

TY  - CONF
AU  - Matcha, Wannisa
AU  - Gašević, Dragan
AU  - Uzir, Nora'ayu Ahmad
AU  - Jovanović, Jelena
AU  - Pardo, Abelardo
AU  - Maldonado-Mahauad, Jorge
AU  - Perez-Sanagustin, Mar
PY  - 2019
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/1881
AB  - Research in learning analytics proposed different computational techniques to detect learning tactics and strategies adopted by learners in digital environments through the analysis of students' trace data. While many promising insights have been produced, there has been much less understanding about how and to what extent different data analytic approaches influence results. This paper presents a comparison of three analytic approaches including process, sequence, and network approaches for detection of learning tactics and strategies. The analysis was performed on a dataset collected in a massive open online course on software programming. All three approaches produced four tactics and three strategy groups. The tactics detected by using the sequence analysis approach differed from those identified by the other two methods. The process and network analytic approaches had more than 66% of similarity in the detected tactics. Learning strategies detected by the three approaches proved to be highly similar.
PB  - Springer International Publishing Ag, Cham
C3  - Transforming Learning with Meaningful Technologies, Ec-Tel 2019
T1  - Detection of Learning Strategies: A Comparison of Process, Sequence and Network Analytic Approaches
EP  - 540
SP  - 525
VL  - 11722
DO  - 10.1007/978-3-030-29736-7_39
UR  - conv_2376
ER  - 

@conference{
author = "Matcha, Wannisa and Gašević, Dragan and Uzir, Nora'ayu Ahmad and Jovanović, Jelena and Pardo, Abelardo and Maldonado-Mahauad, Jorge and Perez-Sanagustin, Mar",
year = "2019",
abstract = "Research in learning analytics proposed different computational techniques to detect learning tactics and strategies adopted by learners in digital environments through the analysis of students' trace data. While many promising insights have been produced, there has been much less understanding about how and to what extent different data analytic approaches influence results. This paper presents a comparison of three analytic approaches including process, sequence, and network approaches for detection of learning tactics and strategies. The analysis was performed on a dataset collected in a massive open online course on software programming. All three approaches produced four tactics and three strategy groups. The tactics detected by using the sequence analysis approach differed from those identified by the other two methods. The process and network analytic approaches had more than 66% of similarity in the detected tactics. Learning strategies detected by the three approaches proved to be highly similar.",
publisher = "Springer International Publishing Ag, Cham",
journal = "Transforming Learning with Meaningful Technologies, Ec-Tel 2019",
title = "Detection of Learning Strategies: A Comparison of Process, Sequence and Network Analytic Approaches",
pages = "540-525",
volume = "11722",
doi = "10.1007/978-3-030-29736-7_39",
url = "conv_2376"
}

Matcha, W., Gašević, D., Uzir, N. A., Jovanović, J., Pardo, A., Maldonado-Mahauad, J.,& Perez-Sanagustin, M.. (2019). Detection of Learning Strategies: A Comparison of Process, Sequence and Network Analytic Approaches. in Transforming Learning with Meaningful Technologies, Ec-Tel 2019
Springer International Publishing Ag, Cham., 11722, 525-540.
https://doi.org/10.1007/978-3-030-29736-7_39
conv_2376

Matcha W, Gašević D, Uzir NA, Jovanović J, Pardo A, Maldonado-Mahauad J, Perez-Sanagustin M. Detection of Learning Strategies: A Comparison of Process, Sequence and Network Analytic Approaches. in Transforming Learning with Meaningful Technologies, Ec-Tel 2019. 2019;11722:525-540.
doi:10.1007/978-3-030-29736-7_39
conv_2376 .

Matcha, Wannisa, Gašević, Dragan, Uzir, Nora'ayu Ahmad, Jovanović, Jelena, Pardo, Abelardo, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, "Detection of Learning Strategies: A Comparison of Process, Sequence and Network Analytic Approaches" in Transforming Learning with Meaningful Technologies, Ec-Tel 2019, 11722 (2019):525-540,
https://doi.org/10.1007/978-3-030-29736-7_39 .,
conv_2376 .

TY  - JOUR
AU  - Pardo, Abelardo
AU  - Jovanović, Jelena
AU  - Dawson, Shane
AU  - Gašević, Dragan
AU  - Mirriahi, Negin
PY  - 2019
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/1908
AB  - There is little debate regarding the importance of student feedback for improving the learning process. However, there remain significant workload barriers for instructors that impede their capacity to provide timely and meaningful feedback. The increasing role technology is playing in the education space may provide novel solutions to this impediment. As students interact with the various learning technologies in their course of study, they create digital traces that can be captured and analysed. These digital traces form the new kind of data that are frequently used in learning analytics to develop actionable recommendations that can support student learning. This paper explores the use of such analytics to address the challenges impeding the capacity of instructors to provide personalised feedback at scale. The case study reported in the paper showed how the approach was associated with a positive impact on student perception of feedback quality and on academic achievement. The study was conducted with first year undergraduate engineering students enrolled in a computer systems course with a blended learning design across three consecutive years (N-2013 = 290, N-2014 = 316 and N-2015 = 415).
PB  - Wiley, Hoboken
T2  - British Journal of Educational Technology
T1  - Using learning analytics to scale the provision of personalised feedback
EP  - 138
IS  - 1
SP  - 128
VL  - 50
DO  - 10.1111/bjet.12592
UR  - conv_2144
ER  - 

@article{
author = "Pardo, Abelardo and Jovanović, Jelena and Dawson, Shane and Gašević, Dragan and Mirriahi, Negin",
year = "2019",
abstract = "There is little debate regarding the importance of student feedback for improving the learning process. However, there remain significant workload barriers for instructors that impede their capacity to provide timely and meaningful feedback. The increasing role technology is playing in the education space may provide novel solutions to this impediment. As students interact with the various learning technologies in their course of study, they create digital traces that can be captured and analysed. These digital traces form the new kind of data that are frequently used in learning analytics to develop actionable recommendations that can support student learning. This paper explores the use of such analytics to address the challenges impeding the capacity of instructors to provide personalised feedback at scale. The case study reported in the paper showed how the approach was associated with a positive impact on student perception of feedback quality and on academic achievement. The study was conducted with first year undergraduate engineering students enrolled in a computer systems course with a blended learning design across three consecutive years (N-2013 = 290, N-2014 = 316 and N-2015 = 415).",
publisher = "Wiley, Hoboken",
journal = "British Journal of Educational Technology",
title = "Using learning analytics to scale the provision of personalised feedback",
pages = "138-128",
number = "1",
volume = "50",
doi = "10.1111/bjet.12592",
url = "conv_2144"
}

Pardo, A., Jovanović, J., Dawson, S., Gašević, D.,& Mirriahi, N.. (2019). Using learning analytics to scale the provision of personalised feedback. in British Journal of Educational Technology
Wiley, Hoboken., 50(1), 128-138.
https://doi.org/10.1111/bjet.12592
conv_2144

Pardo A, Jovanović J, Dawson S, Gašević D, Mirriahi N. Using learning analytics to scale the provision of personalised feedback. in British Journal of Educational Technology. 2019;50(1):128-138.
doi:10.1111/bjet.12592
conv_2144 .

Pardo, Abelardo, Jovanović, Jelena, Dawson, Shane, Gašević, Dragan, Mirriahi, Negin, "Using learning analytics to scale the provision of personalised feedback" in British Journal of Educational Technology, 50, no. 1 (2019):128-138,
https://doi.org/10.1111/bjet.12592 .,
conv_2144 .

TY  - JOUR
AU  - Fincham, Ed
AU  - Gašević, Dragan
AU  - Jovanović, Jelena
AU  - Pardo, Abelardo
PY  - 2019
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/2001
AB  - Research into self-regulated learning has traditionally relied upon self-reported data. While there is a rich body of literature that has extracted invaluable information from such sources, it suffers from a number of shortcomings. For instance, it has been shown that surveys often provide insight into students' perceptions about learning rather than how students actually employ study tactics and learning strategies. Accordingly, recent research has sought to assess students' learning strategies and, by extension, their self-regulated learning via trace data collected from digital learning environments. A number of studies have amply demonstrated the ability of educational data mining and learning analytics methods to identify patterns indicative of learning strategies within trace log data. However, many of these methods are limited in their ability to describe and interpret differences between extracted latent representations at varying levels of granularity (for instance, in terms of the underlying data of student actions and behavior). To address this limitation, the present study proposes a new methodology whereby interpretable representations of student's self-regulating behavior are derived at two theoretically inspired levels: that of learning strategies, and the study tactics that compose them.
PB  - IEEE Computer Soc, Los Alamitos
T2  - IEEE Transactions on Learning Technologies
T1  - From Study Tactics to Learning Strategies: An Analytical Method for Extracting Interpretable Representations
EP  - 72
IS  - 1
SP  - 59
VL  - 12
DO  - 10.1109/TLT.2018.2823317
UR  - conv_2165
ER  - 

@article{
author = "Fincham, Ed and Gašević, Dragan and Jovanović, Jelena and Pardo, Abelardo",
year = "2019",
abstract = "Research into self-regulated learning has traditionally relied upon self-reported data. While there is a rich body of literature that has extracted invaluable information from such sources, it suffers from a number of shortcomings. For instance, it has been shown that surveys often provide insight into students' perceptions about learning rather than how students actually employ study tactics and learning strategies. Accordingly, recent research has sought to assess students' learning strategies and, by extension, their self-regulated learning via trace data collected from digital learning environments. A number of studies have amply demonstrated the ability of educational data mining and learning analytics methods to identify patterns indicative of learning strategies within trace log data. However, many of these methods are limited in their ability to describe and interpret differences between extracted latent representations at varying levels of granularity (for instance, in terms of the underlying data of student actions and behavior). To address this limitation, the present study proposes a new methodology whereby interpretable representations of student's self-regulating behavior are derived at two theoretically inspired levels: that of learning strategies, and the study tactics that compose them.",
publisher = "IEEE Computer Soc, Los Alamitos",
journal = "IEEE Transactions on Learning Technologies",
title = "From Study Tactics to Learning Strategies: An Analytical Method for Extracting Interpretable Representations",
pages = "72-59",
number = "1",
volume = "12",
doi = "10.1109/TLT.2018.2823317",
url = "conv_2165"
}

Fincham, E., Gašević, D., Jovanović, J.,& Pardo, A.. (2019). From Study Tactics to Learning Strategies: An Analytical Method for Extracting Interpretable Representations. in IEEE Transactions on Learning Technologies
IEEE Computer Soc, Los Alamitos., 12(1), 59-72.
https://doi.org/10.1109/TLT.2018.2823317
conv_2165

Fincham E, Gašević D, Jovanović J, Pardo A. From Study Tactics to Learning Strategies: An Analytical Method for Extracting Interpretable Representations. in IEEE Transactions on Learning Technologies. 2019;12(1):59-72.
doi:10.1109/TLT.2018.2823317
conv_2165 .

Fincham, Ed, Gašević, Dragan, Jovanović, Jelena, Pardo, Abelardo, "From Study Tactics to Learning Strategies: An Analytical Method for Extracting Interpretable Representations" in IEEE Transactions on Learning Technologies, 12, no. 1 (2019):59-72,
https://doi.org/10.1109/TLT.2018.2823317 .,
conv_2165 .

TY  - CONF
AU  - Matcha, Wannisa
AU  - Gašević, Dragan
AU  - Uzir, Nora'ayu Ahmad
AU  - Jovanović, Jelena
AU  - Pardo, Abelardo
PY  - 2019
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/1968
AB  - Learning analytics has the potential to detect and explain characteristics of learning strategies through analysis of trace data and communicate the findings via feedback. However, the role of learning analytics-based feedback in selection and regulation of learning strategies is still insufficiently explored and understood. This research aims to examine the sequential and temporal characteristics of learning strategies and investigate their association with feedback. Three years of trace data were collected from online pre-class activities of a flipped classroom, where different types of feedback were employed in each year. Clustering, sequence mining, and process mining were used to detect and interpret learning tactics and strategies. Inferential statistics were used to examine the association of feedback with the learning performance and the detected learning strategies. The results suggest a positive association between the personalised feedback and the effective strategies.
PB  - Assoc Computing Machinery, New York
C3  - Proceedings of the 9th International Conference on Learning Analytics & Knowledge (Lak'19)
T1  - Analytics of Learning Strategies: Associations with Academic Performance and Feedback
EP  - 470
SP  - 461
DO  - 10.1145/3303772.3303787
UR  - conv_2197
ER  - 

@conference{
author = "Matcha, Wannisa and Gašević, Dragan and Uzir, Nora'ayu Ahmad and Jovanović, Jelena and Pardo, Abelardo",
year = "2019",
abstract = "Learning analytics has the potential to detect and explain characteristics of learning strategies through analysis of trace data and communicate the findings via feedback. However, the role of learning analytics-based feedback in selection and regulation of learning strategies is still insufficiently explored and understood. This research aims to examine the sequential and temporal characteristics of learning strategies and investigate their association with feedback. Three years of trace data were collected from online pre-class activities of a flipped classroom, where different types of feedback were employed in each year. Clustering, sequence mining, and process mining were used to detect and interpret learning tactics and strategies. Inferential statistics were used to examine the association of feedback with the learning performance and the detected learning strategies. The results suggest a positive association between the personalised feedback and the effective strategies.",
publisher = "Assoc Computing Machinery, New York",
journal = "Proceedings of the 9th International Conference on Learning Analytics & Knowledge (Lak'19)",
title = "Analytics of Learning Strategies: Associations with Academic Performance and Feedback",
pages = "470-461",
doi = "10.1145/3303772.3303787",
url = "conv_2197"
}

Matcha, W., Gašević, D., Uzir, N. A., Jovanović, J.,& Pardo, A.. (2019). Analytics of Learning Strategies: Associations with Academic Performance and Feedback. in Proceedings of the 9th International Conference on Learning Analytics & Knowledge (Lak'19)
Assoc Computing Machinery, New York., 461-470.
https://doi.org/10.1145/3303772.3303787
conv_2197

Matcha W, Gašević D, Uzir NA, Jovanović J, Pardo A. Analytics of Learning Strategies: Associations with Academic Performance and Feedback. in Proceedings of the 9th International Conference on Learning Analytics & Knowledge (Lak'19). 2019;:461-470.
doi:10.1145/3303772.3303787
conv_2197 .

Matcha, Wannisa, Gašević, Dragan, Uzir, Nora'ayu Ahmad, Jovanović, Jelena, Pardo, Abelardo, "Analytics of Learning Strategies: Associations with Academic Performance and Feedback" in Proceedings of the 9th International Conference on Learning Analytics & Knowledge (Lak'19) (2019):461-470,
https://doi.org/10.1145/3303772.3303787 .,
conv_2197 .

TY  - CONF
AU  - Jovanović, Jelena
AU  - Gašević, Dragan
AU  - Pardo, Abelardo
AU  - Dawson, Shane
AU  - Whitelock-Wainwright, Alexander
PY  - 2019
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/1967
AB  - The use of learning trace data together with various analytical methods has proven successful in detecting patterns in learning behaviour, identifying student profiles, and clustering learning resources. However, interpretation of the findings is often difficult and uncertain due to a lack of contextual data (e.g., data on student motivation, emotion or curriculum design). In this study we explored the integration of student selfreports about cognitive load and self-efficacy into the learning process and collection of relevant students' perceptions as learning traces. Our objective was to examine the association of traced measures of relevant learning constructs (cognitive load and self-efficacy) with i) indicators of the students' learning behaviour derived from trace data, and ii) the students' academic performance. The results indicated the presence of association between some indicators of students' engagement with learning activities and traced measures of cognitive load and self-efficacy. Correlational analysis demonstrated significant positive correlation between the students' course performance and traced measures of cognitive load and self-efficacy.
PB  - Assoc Computing Machinery, New York
C3  - Proceedings of the 9th International Conference on Learning Analytics & Knowledge (Lak'19)
T1  - Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load
EP  - 520
SP  - 511
DO  - 10.1145/3303772.3303782
UR  - conv_2198
ER  - 

@conference{
author = "Jovanović, Jelena and Gašević, Dragan and Pardo, Abelardo and Dawson, Shane and Whitelock-Wainwright, Alexander",
year = "2019",
abstract = "The use of learning trace data together with various analytical methods has proven successful in detecting patterns in learning behaviour, identifying student profiles, and clustering learning resources. However, interpretation of the findings is often difficult and uncertain due to a lack of contextual data (e.g., data on student motivation, emotion or curriculum design). In this study we explored the integration of student selfreports about cognitive load and self-efficacy into the learning process and collection of relevant students' perceptions as learning traces. Our objective was to examine the association of traced measures of relevant learning constructs (cognitive load and self-efficacy) with i) indicators of the students' learning behaviour derived from trace data, and ii) the students' academic performance. The results indicated the presence of association between some indicators of students' engagement with learning activities and traced measures of cognitive load and self-efficacy. Correlational analysis demonstrated significant positive correlation between the students' course performance and traced measures of cognitive load and self-efficacy.",
publisher = "Assoc Computing Machinery, New York",
journal = "Proceedings of the 9th International Conference on Learning Analytics & Knowledge (Lak'19)",
title = "Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load",
pages = "520-511",
doi = "10.1145/3303772.3303782",
url = "conv_2198"
}

Jovanović, J., Gašević, D., Pardo, A., Dawson, S.,& Whitelock-Wainwright, A.. (2019). Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load. in Proceedings of the 9th International Conference on Learning Analytics & Knowledge (Lak'19)
Assoc Computing Machinery, New York., 511-520.
https://doi.org/10.1145/3303772.3303782
conv_2198

Jovanović J, Gašević D, Pardo A, Dawson S, Whitelock-Wainwright A. Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load. in Proceedings of the 9th International Conference on Learning Analytics & Knowledge (Lak'19). 2019;:511-520.
doi:10.1145/3303772.3303782
conv_2198 .

Jovanović, Jelena, Gašević, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander, "Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load" in Proceedings of the 9th International Conference on Learning Analytics & Knowledge (Lak'19) (2019):511-520,
https://doi.org/10.1145/3303772.3303782 .,
conv_2198 .

TY  - JOUR
AU  - Pardo, Abelardo
AU  - Gašević, Dragan
AU  - Jovanović, Jelena
AU  - Dawson, Shane
AU  - Mirriahi, Negin
PY  - 2019
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/1947
AB  - The success of the flipped classroom (FC) is effectively reliant on the level of student engagement with the preparatory activities prior to attending face-to-face teaching sessions. Information about the nature and level of student engagement with these activities can help instructors make informed decisions regarding how to best support student learning. Despite the comprehensive data collection enabled by the increasing presence of education technologies, few studies have used these data to investigate the range of strategies students employ in FC models. This study addresses this deficit by proposing an analytical approach that allows for exploring, first, the strategies students use to interact with online preparation activities; and, second, evolution of those strategies over the duration of a course delivered with a FC pedagogy. The proposed approach identified eight learning strategies and six trajectories of strategy change over the duration of the course, with statistically significant effects of strategy change trajectories on academic performance.
PB  - IEEE Computer Soc, Los Alamitos
T2  - IEEE Transactions on Learning Technologies
T1  - Exploring Student Interactions With Preparation Activities in a Flipped Classroom Experience
EP  - 346
IS  - 3
SP  - 333
VL  - 12
DO  - 10.1109/TLT.2018.2858790
UR  - conv_2218
ER  - 

@article{
author = "Pardo, Abelardo and Gašević, Dragan and Jovanović, Jelena and Dawson, Shane and Mirriahi, Negin",
year = "2019",
abstract = "The success of the flipped classroom (FC) is effectively reliant on the level of student engagement with the preparatory activities prior to attending face-to-face teaching sessions. Information about the nature and level of student engagement with these activities can help instructors make informed decisions regarding how to best support student learning. Despite the comprehensive data collection enabled by the increasing presence of education technologies, few studies have used these data to investigate the range of strategies students employ in FC models. This study addresses this deficit by proposing an analytical approach that allows for exploring, first, the strategies students use to interact with online preparation activities; and, second, evolution of those strategies over the duration of a course delivered with a FC pedagogy. The proposed approach identified eight learning strategies and six trajectories of strategy change over the duration of the course, with statistically significant effects of strategy change trajectories on academic performance.",
publisher = "IEEE Computer Soc, Los Alamitos",
journal = "IEEE Transactions on Learning Technologies",
title = "Exploring Student Interactions With Preparation Activities in a Flipped Classroom Experience",
pages = "346-333",
number = "3",
volume = "12",
doi = "10.1109/TLT.2018.2858790",
url = "conv_2218"
}

Pardo, A., Gašević, D., Jovanović, J., Dawson, S.,& Mirriahi, N.. (2019). Exploring Student Interactions With Preparation Activities in a Flipped Classroom Experience. in IEEE Transactions on Learning Technologies
IEEE Computer Soc, Los Alamitos., 12(3), 333-346.
https://doi.org/10.1109/TLT.2018.2858790
conv_2218

Pardo A, Gašević D, Jovanović J, Dawson S, Mirriahi N. Exploring Student Interactions With Preparation Activities in a Flipped Classroom Experience. in IEEE Transactions on Learning Technologies. 2019;12(3):333-346.
doi:10.1109/TLT.2018.2858790
conv_2218 .

Pardo, Abelardo, Gašević, Dragan, Jovanović, Jelena, Dawson, Shane, Mirriahi, Negin, "Exploring Student Interactions With Preparation Activities in a Flipped Classroom Experience" in IEEE Transactions on Learning Technologies, 12, no. 3 (2019):333-346,
https://doi.org/10.1109/TLT.2018.2858790 .,
conv_2218 .

TY  - JOUR
AU  - Mirriahi, Negin
AU  - Jovanović, Jelena
AU  - Dawson, Shane
AU  - Gašević, Dragan
AU  - Pardo, Abelardo
PY  - 2018
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/1832
AB  - The rapid growth of blended and online learning models in higher education has resulted in a parallel increase in the use of audio-visual resources among students and teachers. Despite the heavy adoption of video resources, there have been few studies investigating their effect on learning processes and even less so in the context of academic development. This paper uses learning analytic techniques to examine how academic teaching staff engage with a set of prescribed videos and video annotations in a professional development course. The data was collected from two offerings of the course at a large research-intensive university in Australia. The data was used to identify patterns of activity and transition states as users engaged with the course videos and video annotations. Latent class analysis and hidden Markov models were used to characterise the evolution of engagement throughout the course. The results provide a detailed description of the evolution of learner engagement that can be readily translated into action aimed at increasing the quality of the learning experience.
PB  - Australasian Soc Computers Learning Tertiary Education-Ascilite, Tugun
T2  - Australasian Journal of Educational Technology
T1  - Identifying engagement patterns with video annotation activities: A case study in professional development
EP  - 72
IS  - 1
SP  - 57
VL  - 34
DO  - 10.14742/ajet.3207
UR  - conv_2024
ER  - 

@article{
author = "Mirriahi, Negin and Jovanović, Jelena and Dawson, Shane and Gašević, Dragan and Pardo, Abelardo",
year = "2018",
abstract = "The rapid growth of blended and online learning models in higher education has resulted in a parallel increase in the use of audio-visual resources among students and teachers. Despite the heavy adoption of video resources, there have been few studies investigating their effect on learning processes and even less so in the context of academic development. This paper uses learning analytic techniques to examine how academic teaching staff engage with a set of prescribed videos and video annotations in a professional development course. The data was collected from two offerings of the course at a large research-intensive university in Australia. The data was used to identify patterns of activity and transition states as users engaged with the course videos and video annotations. Latent class analysis and hidden Markov models were used to characterise the evolution of engagement throughout the course. The results provide a detailed description of the evolution of learner engagement that can be readily translated into action aimed at increasing the quality of the learning experience.",
publisher = "Australasian Soc Computers Learning Tertiary Education-Ascilite, Tugun",
journal = "Australasian Journal of Educational Technology",
title = "Identifying engagement patterns with video annotation activities: A case study in professional development",
pages = "72-57",
number = "1",
volume = "34",
doi = "10.14742/ajet.3207",
url = "conv_2024"
}

Mirriahi, N., Jovanović, J., Dawson, S., Gašević, D.,& Pardo, A.. (2018). Identifying engagement patterns with video annotation activities: A case study in professional development. in Australasian Journal of Educational Technology
Australasian Soc Computers Learning Tertiary Education-Ascilite, Tugun., 34(1), 57-72.
https://doi.org/10.14742/ajet.3207
conv_2024

Mirriahi N, Jovanović J, Dawson S, Gašević D, Pardo A. Identifying engagement patterns with video annotation activities: A case study in professional development. in Australasian Journal of Educational Technology. 2018;34(1):57-72.
doi:10.14742/ajet.3207
conv_2024 .

Mirriahi, Negin, Jovanović, Jelena, Dawson, Shane, Gašević, Dragan, Pardo, Abelardo, "Identifying engagement patterns with video annotation activities: A case study in professional development" in Australasian Journal of Educational Technology, 34, no. 1 (2018):57-72,
https://doi.org/10.14742/ajet.3207 .,
conv_2024 .

TY  - CONF
AU  - Dawson, Shane
AU  - Jovanović, Jelena
AU  - Gašević, Dragan
AU  - Pardo, Abelardo
PY  - 2017
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/1727
AB  - Learning analytics research has often been touted as a means to address concerns regarding student retention outcomes. However, few research studies to date, have examined the impact of the implemented intervention strategies designed to address such retention challenges. Moreover, the methodological rigor of some of the existing studies has been challenged. This study evaluates the impact of a pilot retention program. The study contrasts the findings obtained by the use of different methods for analysis of the effect of the intervention. The pilot study was undertaken between 2012 and 2014 resulting in a combined enrolment of 11,160 students. A model to predict attrition was developed, drawing on data from student information system, learning management system interactions, and assessment. The predictive model identified some 1868 students as academically at-risk. Early interventions were implemented involving learning and remediation support. Common statistical methods demonstrated a positive association between the intervention and student retention. However, the effect size was low. The use of more advanced statistical methods, specifically mixed-effect methods explained higher variability in the data (over 99%), yet found the intervention had no effect on the retention outcomes. The study demonstrates that more data about individual differences is required to not only explain retention but to also develop more effective intervention approaches.
PB  - Assoc Computing Machinery, New York
C3  - Seventh International Learning Analytics & Knowledge Conference (LAK'17)
T1  - From prediction to impact: Evaluation of a learning analytics retention program
EP  - 478
SP  - 474
DO  - 10.1145/3027385.3027405
UR  - conv_2379
ER  - 

@conference{
author = "Dawson, Shane and Jovanović, Jelena and Gašević, Dragan and Pardo, Abelardo",
year = "2017",
abstract = "Learning analytics research has often been touted as a means to address concerns regarding student retention outcomes. However, few research studies to date, have examined the impact of the implemented intervention strategies designed to address such retention challenges. Moreover, the methodological rigor of some of the existing studies has been challenged. This study evaluates the impact of a pilot retention program. The study contrasts the findings obtained by the use of different methods for analysis of the effect of the intervention. The pilot study was undertaken between 2012 and 2014 resulting in a combined enrolment of 11,160 students. A model to predict attrition was developed, drawing on data from student information system, learning management system interactions, and assessment. The predictive model identified some 1868 students as academically at-risk. Early interventions were implemented involving learning and remediation support. Common statistical methods demonstrated a positive association between the intervention and student retention. However, the effect size was low. The use of more advanced statistical methods, specifically mixed-effect methods explained higher variability in the data (over 99%), yet found the intervention had no effect on the retention outcomes. The study demonstrates that more data about individual differences is required to not only explain retention but to also develop more effective intervention approaches.",
publisher = "Assoc Computing Machinery, New York",
journal = "Seventh International Learning Analytics & Knowledge Conference (LAK'17)",
title = "From prediction to impact: Evaluation of a learning analytics retention program",
pages = "478-474",
doi = "10.1145/3027385.3027405",
url = "conv_2379"
}

Dawson, S., Jovanović, J., Gašević, D.,& Pardo, A.. (2017). From prediction to impact: Evaluation of a learning analytics retention program. in Seventh International Learning Analytics & Knowledge Conference (LAK'17)
Assoc Computing Machinery, New York., 474-478.
https://doi.org/10.1145/3027385.3027405
conv_2379

Dawson S, Jovanović J, Gašević D, Pardo A. From prediction to impact: Evaluation of a learning analytics retention program. in Seventh International Learning Analytics & Knowledge Conference (LAK'17). 2017;:474-478.
doi:10.1145/3027385.3027405
conv_2379 .

Dawson, Shane, Jovanović, Jelena, Gašević, Dragan, Pardo, Abelardo, "From prediction to impact: Evaluation of a learning analytics retention program" in Seventh International Learning Analytics & Knowledge Conference (LAK'17) (2017):474-478,
https://doi.org/10.1145/3027385.3027405 .,
conv_2379 .

TY  - JOUR
AU  - Jovanović, Jelena
AU  - Gašević, Dragan
AU  - Dawson, Shane
AU  - Pardo, Abelardo
AU  - Mirriahi, Negin
PY  - 2017
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/1698
PB  - Elsevier Science Inc, New York
T2  - Internet and Higher Education
T1  - Learning analytics to unveil learning strategies in a flipped classroom
EP  - 85
SP  - 74
VL  - 33
DO  - 10.1016/j.iheduc.2017.02.001
UR  - conv_1921
ER  - 

@article{
author = "Jovanović, Jelena and Gašević, Dragan and Dawson, Shane and Pardo, Abelardo and Mirriahi, Negin",
year = "2017",
publisher = "Elsevier Science Inc, New York",
journal = "Internet and Higher Education",
title = "Learning analytics to unveil learning strategies in a flipped classroom",
pages = "85-74",
volume = "33",
doi = "10.1016/j.iheduc.2017.02.001",
url = "conv_1921"
}

Jovanović, J., Gašević, D., Dawson, S., Pardo, A.,& Mirriahi, N.. (2017). Learning analytics to unveil learning strategies in a flipped classroom. in Internet and Higher Education
Elsevier Science Inc, New York., 33, 74-85.
https://doi.org/10.1016/j.iheduc.2017.02.001
conv_1921

Jovanović J, Gašević D, Dawson S, Pardo A, Mirriahi N. Learning analytics to unveil learning strategies in a flipped classroom. in Internet and Higher Education. 2017;33:74-85.
doi:10.1016/j.iheduc.2017.02.001
conv_1921 .

Jovanović, Jelena, Gašević, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin, "Learning analytics to unveil learning strategies in a flipped classroom" in Internet and Higher Education, 33 (2017):74-85,
https://doi.org/10.1016/j.iheduc.2017.02.001 .,
conv_1921 .

TY  - CONF
AU  - Pardo, Abelardo
AU  - Mirriahi, Negin
AU  - Martinez-Maldonado, Roberto
AU  - Jovanović, Jelena
AU  - Dawson, Shane
AU  - Gašević, Dragan
PY  - 2016
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/1564
AB  - The pervasive collection of data has opened the possibility for educational institutions to use analytics methods to improve the quality of the student experience. However, the adoption of these methods faces multiple challenges particularly at the course level where instructors and students would derive the most benefit from the use of analytics and predictive models. The challenge lies in the knowledge gap between how the data is captured, processed and used to derive models of student behavior, and the subsequent interpretation and the decision to deploy pedagogical actions and interventions by instructors. Simply put, the provision of learning analytics alone has not necessarily led to changing teaching practices. In order to support pedagogical change and aid interpretation, this paper proposes a model that can enable instructors to readily identify subpopulations of students to provide specific support actions. The approach was applied to a first year course with a large number of students. The resulting model classifies students according to their predicted exam scores, based on indicators directly derived from the learning design.
PB  - Assoc Computing Machinery, New York
C3  - LAK '16 Conference Proceedings: the Sixth International Learning Analytics & Knowledge Conference
T1  - Generating Actionable Predictive Models of Academic Performance
EP  - 478
SP  - 474
DO  - 10.1145/2883851.2883870
UR  - conv_1887
ER  - 

@conference{
author = "Pardo, Abelardo and Mirriahi, Negin and Martinez-Maldonado, Roberto and Jovanović, Jelena and Dawson, Shane and Gašević, Dragan",
year = "2016",
abstract = "The pervasive collection of data has opened the possibility for educational institutions to use analytics methods to improve the quality of the student experience. However, the adoption of these methods faces multiple challenges particularly at the course level where instructors and students would derive the most benefit from the use of analytics and predictive models. The challenge lies in the knowledge gap between how the data is captured, processed and used to derive models of student behavior, and the subsequent interpretation and the decision to deploy pedagogical actions and interventions by instructors. Simply put, the provision of learning analytics alone has not necessarily led to changing teaching practices. In order to support pedagogical change and aid interpretation, this paper proposes a model that can enable instructors to readily identify subpopulations of students to provide specific support actions. The approach was applied to a first year course with a large number of students. The resulting model classifies students according to their predicted exam scores, based on indicators directly derived from the learning design.",
publisher = "Assoc Computing Machinery, New York",
journal = "LAK '16 Conference Proceedings: the Sixth International Learning Analytics & Knowledge Conference",
title = "Generating Actionable Predictive Models of Academic Performance",
pages = "478-474",
doi = "10.1145/2883851.2883870",
url = "conv_1887"
}

Pardo, A., Mirriahi, N., Martinez-Maldonado, R., Jovanović, J., Dawson, S.,& Gašević, D.. (2016). Generating Actionable Predictive Models of Academic Performance. in LAK '16 Conference Proceedings: the Sixth International Learning Analytics & Knowledge Conference
Assoc Computing Machinery, New York., 474-478.
https://doi.org/10.1145/2883851.2883870
conv_1887

Pardo A, Mirriahi N, Martinez-Maldonado R, Jovanović J, Dawson S, Gašević D. Generating Actionable Predictive Models of Academic Performance. in LAK '16 Conference Proceedings: the Sixth International Learning Analytics & Knowledge Conference. 2016;:474-478.
doi:10.1145/2883851.2883870
conv_1887 .

Pardo, Abelardo, Mirriahi, Negin, Martinez-Maldonado, Roberto, Jovanović, Jelena, Dawson, Shane, Gašević, Dragan, "Generating Actionable Predictive Models of Academic Performance" in LAK '16 Conference Proceedings: the Sixth International Learning Analytics & Knowledge Conference (2016):474-478,
https://doi.org/10.1145/2883851.2883870 .,
conv_1887 .

TY  - JOUR
AU  - Cuzzola, John
AU  - Jovanović, Jelena
AU  - Bagheri, Ebrahim
AU  - Gašević, Dragan
PY  - 2015
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/1404
AB  - Websites offering daily deal offers have received widespread attention from the end-users. The objective of such Websites is to provide time limited discounts on goods and services in the hope of enticing more customers to purchase such goods or services. The success of daily deal Websites has given rise to meta-level daily deal aggregator services that collect daily deal information from across the Web. Due to some of the unique characteristics of daily deal Websites such as high update frequency, time sensitivity, and lack of coherent information representation, many deal aggregators rely on human intervention to identify and extract deal information. In this paper, we propose an approach where daily deal information is identified, classified and properly segmented and localized. Our approach is based on a semi-supervised method that uses sentence-level features of daily deal information on a given Web page. Our work offers (i) a set of computationally inexpensive discriminative features that are able to effectively distinguishWeb pages that contain daily deal information; (ii) the construction and systematic evaluation of machine learning techniques based on these features to automatically classify daily deal Web pages; and (iii) the development of an accurate segmentation algorithm that is able to localize and extract individual deals from within a complex Web page. We have extensively evaluated our approach from different perspectives, the results of which show notable performance.
PB  - Elsevier, Amsterdam
T2  - Applied Soft Computing
T1  - Automated classification and localization of daily deal content from the Web
EP  - 256
SP  - 241
VL  - 31
DO  - 10.1016/j.asoc.2015.02.029
UR  - conv_1703
ER  - 

@article{
author = "Cuzzola, John and Jovanović, Jelena and Bagheri, Ebrahim and Gašević, Dragan",
year = "2015",
abstract = "Websites offering daily deal offers have received widespread attention from the end-users. The objective of such Websites is to provide time limited discounts on goods and services in the hope of enticing more customers to purchase such goods or services. The success of daily deal Websites has given rise to meta-level daily deal aggregator services that collect daily deal information from across the Web. Due to some of the unique characteristics of daily deal Websites such as high update frequency, time sensitivity, and lack of coherent information representation, many deal aggregators rely on human intervention to identify and extract deal information. In this paper, we propose an approach where daily deal information is identified, classified and properly segmented and localized. Our approach is based on a semi-supervised method that uses sentence-level features of daily deal information on a given Web page. Our work offers (i) a set of computationally inexpensive discriminative features that are able to effectively distinguishWeb pages that contain daily deal information; (ii) the construction and systematic evaluation of machine learning techniques based on these features to automatically classify daily deal Web pages; and (iii) the development of an accurate segmentation algorithm that is able to localize and extract individual deals from within a complex Web page. We have extensively evaluated our approach from different perspectives, the results of which show notable performance.",
publisher = "Elsevier, Amsterdam",
journal = "Applied Soft Computing",
title = "Automated classification and localization of daily deal content from the Web",
pages = "256-241",
volume = "31",
doi = "10.1016/j.asoc.2015.02.029",
url = "conv_1703"
}

Cuzzola, J., Jovanović, J., Bagheri, E.,& Gašević, D.. (2015). Automated classification and localization of daily deal content from the Web. in Applied Soft Computing
Elsevier, Amsterdam., 31, 241-256.
https://doi.org/10.1016/j.asoc.2015.02.029
conv_1703

Cuzzola J, Jovanović J, Bagheri E, Gašević D. Automated classification and localization of daily deal content from the Web. in Applied Soft Computing. 2015;31:241-256.
doi:10.1016/j.asoc.2015.02.029
conv_1703 .

Cuzzola, John, Jovanović, Jelena, Bagheri, Ebrahim, Gašević, Dragan, "Automated classification and localization of daily deal content from the Web" in Applied Soft Computing, 31 (2015):241-256,
https://doi.org/10.1016/j.asoc.2015.02.029 .,
conv_1703 .

TY  - JOUR
AU  - Cuzzola, John
AU  - Jovanović, Jelena
AU  - Bagheri, Ebrahim
AU  - Gašević, Dragan
PY  - 2015
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/1427
AB  - Considering the ever-increasing speed at which new textual content is generated, an efficient and effective use of large text corpora requires automated natural language processing and text analysis tools. A subset of such tools, namely automated semantic annotation tools, are capable of interlinking syntactical forms of text with their underlying semantic concepts. The optimal performance of automated semantic annotation tools often depends on tuning the values of the tools' adjustable parameters to the specificities of the annotation task, and particularly to the characteristics of the text to be annotated. Such characteristics include the text domain, terseness or verbosity level, text length, structure and style. Since the default configuration of annotation tools is not suitable for the large variety of input texts that different combinations of these attributes can produce, users often need to adjust the annotators' tunable parameters in order to get the best results. However, the configuration of semantic annotators is presently a tedious and time consuming task as it is primarily based on a manual trial-and-error process. In this paper, we propose a Parameter Tuning Architecture (PTA) for automating the task of configuring parameter values of semantic annotation tools. We describe the core fitness functions of PTA that operate on the quality of the annotations produced, and offer a solution, based on a genetic algorithm, for searching the space of possible parameter values. Our experiments demonstrate that PTA enables effective configuration of parameter values of many semantic annotation tools.
PB  - Pergamon-Elsevier Science Ltd, Oxford
T2  - Expert Systems with Applications
T1  - Evolutionary fine-tuning of automated semantic annotation systems
EP  - 6877
IS  - 20
SP  - 6864
VL  - 42
DO  - 10.1016/j.eswa.2015.04.054
UR  - conv_1723
ER  - 

@article{
author = "Cuzzola, John and Jovanović, Jelena and Bagheri, Ebrahim and Gašević, Dragan",
year = "2015",
abstract = "Considering the ever-increasing speed at which new textual content is generated, an efficient and effective use of large text corpora requires automated natural language processing and text analysis tools. A subset of such tools, namely automated semantic annotation tools, are capable of interlinking syntactical forms of text with their underlying semantic concepts. The optimal performance of automated semantic annotation tools often depends on tuning the values of the tools' adjustable parameters to the specificities of the annotation task, and particularly to the characteristics of the text to be annotated. Such characteristics include the text domain, terseness or verbosity level, text length, structure and style. Since the default configuration of annotation tools is not suitable for the large variety of input texts that different combinations of these attributes can produce, users often need to adjust the annotators' tunable parameters in order to get the best results. However, the configuration of semantic annotators is presently a tedious and time consuming task as it is primarily based on a manual trial-and-error process. In this paper, we propose a Parameter Tuning Architecture (PTA) for automating the task of configuring parameter values of semantic annotation tools. We describe the core fitness functions of PTA that operate on the quality of the annotations produced, and offer a solution, based on a genetic algorithm, for searching the space of possible parameter values. Our experiments demonstrate that PTA enables effective configuration of parameter values of many semantic annotation tools.",
publisher = "Pergamon-Elsevier Science Ltd, Oxford",
journal = "Expert Systems with Applications",
title = "Evolutionary fine-tuning of automated semantic annotation systems",
pages = "6877-6864",
number = "20",
volume = "42",
doi = "10.1016/j.eswa.2015.04.054",
url = "conv_1723"
}

Cuzzola, J., Jovanović, J., Bagheri, E.,& Gašević, D.. (2015). Evolutionary fine-tuning of automated semantic annotation systems. in Expert Systems with Applications
Pergamon-Elsevier Science Ltd, Oxford., 42(20), 6864-6877.
https://doi.org/10.1016/j.eswa.2015.04.054
conv_1723

Cuzzola J, Jovanović J, Bagheri E, Gašević D. Evolutionary fine-tuning of automated semantic annotation systems. in Expert Systems with Applications. 2015;42(20):6864-6877.
doi:10.1016/j.eswa.2015.04.054
conv_1723 .

Cuzzola, John, Jovanović, Jelena, Bagheri, Ebrahim, Gašević, Dragan, "Evolutionary fine-tuning of automated semantic annotation systems" in Expert Systems with Applications, 42, no. 20 (2015):6864-6877,
https://doi.org/10.1016/j.eswa.2015.04.054 .,
conv_1723 .

TY  - JOUR
AU  - Jovanović, Jelena
AU  - Bagheri, Ebrahim
AU  - Gašević, Dragan
PY  - 2015
UR  - https://rfos.fon.bg.ac.rs/handle/123456789/1426
AB  - The concept of social goals refers to organizational goals that are defined in an open and transparent manner; they serve as social objects that incite both formal and informal collaboration around shared interests/objectives. Our objective is to facilitate the comprehension of social goals and examine the role of social goals as scaffolds of social learning in an organization. To this end, we followed an approach based on the visualization of social goals and explored how different presentations of goals, specifically, faceted goal browsing, graph-based visualization, and timeline-based visualization, contribute to the realization of the stated objective. To assess this approach, we conducted a between subjects study where each participant performed a set of goal comprehension tasks with one of the examined presentations of goals. The study demonstrated that our visualizations of goals increase the accuracy of the overall comprehension of an organization's goals; this positive effect is also present when the comprehension of relationships-either explicit or implicit ties-among social goals is needed. The results also confirmed that our graph-based visualization of social goals could serve as a facilitator of social learning in an organization.
PB  - IEEE-Inst Electrical Electronics Engineers Inc, Piscataway
T2  - IEEE Transactions on Human-Machine Systems
T1  - Comprehension and Learning of Social Goals Through Visualization
EP  - 489
IS  - 4
SP  - 478
VL  - 45
DO  - 10.1109/THMS.2015.2419083
UR  - conv_1730
ER  - 

@article{
author = "Jovanović, Jelena and Bagheri, Ebrahim and Gašević, Dragan",
year = "2015",
abstract = "The concept of social goals refers to organizational goals that are defined in an open and transparent manner; they serve as social objects that incite both formal and informal collaboration around shared interests/objectives. Our objective is to facilitate the comprehension of social goals and examine the role of social goals as scaffolds of social learning in an organization. To this end, we followed an approach based on the visualization of social goals and explored how different presentations of goals, specifically, faceted goal browsing, graph-based visualization, and timeline-based visualization, contribute to the realization of the stated objective. To assess this approach, we conducted a between subjects study where each participant performed a set of goal comprehension tasks with one of the examined presentations of goals. The study demonstrated that our visualizations of goals increase the accuracy of the overall comprehension of an organization's goals; this positive effect is also present when the comprehension of relationships-either explicit or implicit ties-among social goals is needed. The results also confirmed that our graph-based visualization of social goals could serve as a facilitator of social learning in an organization.",
publisher = "IEEE-Inst Electrical Electronics Engineers Inc, Piscataway",
journal = "IEEE Transactions on Human-Machine Systems",
title = "Comprehension and Learning of Social Goals Through Visualization",
pages = "489-478",
number = "4",
volume = "45",
doi = "10.1109/THMS.2015.2419083",
url = "conv_1730"
}

Jovanović, J., Bagheri, E.,& Gašević, D.. (2015). Comprehension and Learning of Social Goals Through Visualization. in IEEE Transactions on Human-Machine Systems
IEEE-Inst Electrical Electronics Engineers Inc, Piscataway., 45(4), 478-489.
https://doi.org/10.1109/THMS.2015.2419083
conv_1730

Jovanović J, Bagheri E, Gašević D. Comprehension and Learning of Social Goals Through Visualization. in IEEE Transactions on Human-Machine Systems. 2015;45(4):478-489.
doi:10.1109/THMS.2015.2419083
conv_1730 .

Jovanović, Jelena, Bagheri, Ebrahim, Gašević, Dragan, "Comprehension and Learning of Social Goals Through Visualization" in IEEE Transactions on Human-Machine Systems, 45, no. 4 (2015):478-489,
https://doi.org/10.1109/THMS.2015.2419083 .,
conv_1730 .