Intense, turbulent, or wallowing in the mire: A longitudinal study of cross-course online tactics, strategies, and trajectories

Internet and Higher Education - Tập 57 - Trang 100902 - 2023
Mohammed Saqr1, Sonsoles López-Pernas1, Jelena Jovanović2, Dragan Gašević3
1School of Computing, University of Eastern Finland, Joensuu, Finland
2Department of Software Engineering, University of Belgrade, Belgrade, Serbia
3Faculty of Information Technology, Monash University, Clayton, Australia

Tài liệu tham khảo

Abbott, 2000, Sequence analysis and optimal matching methods in sociology: Review and prospect, Sociological Methods & Research, 29, 3, 10.1177/0049124100029001001 Archambault, 2017, Joint trajectories of behavioral, affective, and cognitive engagement in elementary school, The Journal of Educational Research, 110, 188, 10.1080/00220671.2015.1060931 Asikainen, 2017, Do students develop towards more deep approaches to learning during studies? A systematic review on the development of students’ deep and surface approaches to learning in higher education, Educational Psychology Review, 29, 205, 10.1007/s10648-017-9406-6 Azevedo, 2009, Theoretical, conceptual, methodological, and instructional issues in research on metacognition and self-regulated learning: A discussion, Metacognition and Learning, 4, 87, 10.1007/s11409-009-9035-7 Barthakur, 2021, Assessing program-level learning strategies in MOOCs, Computers in Human Behavior, 117 Batagelj, 1988, Generalized Ward and related clustering problems, Classification and Related Methods of Data Analysis, 67 Biggs, 1987, vol. 3122 Biggs, 1988, The role of metacognition in enhancing learning, Australian Journal of Education, 32, 127, 10.1177/000494418803200201 Boekaerts, 1999, Self-regulated learning: Where we are today, International Journal of Educational Research, 31, 445, 10.1016/S0883-0355(99)00014-2 Brown, 1992, Survivorship bias in performance studies, The Review of Financial Studies, 5, 553, 10.1093/rfs/5.4.553 Carpenter, 1999, Survivorship bias and attrition effects in measures of performance persistence, Journal of Financial Economics, 54, 337, 10.1016/S0304-405X(99)00040-9 Cohen, 1992, A power primer, Psychological Bulletin, 112, 155, 10.1037/0033-2909.112.1.155 Cui, 2014, Optimized big data K-means clustering using MapReduce, The Journal of Supercomputing, 70, 1249, 10.1007/s11227-014-1225-7 Dignath, 2008, How can primary school students learn self-regulated learning strategies most effectively?: A meta-analysis on self-regulation training programmes, Educational Research Review, 3, 101, 10.1016/j.edurev.2008.02.003 Dolmans, 2016, Deep and surface learning in problem-based learning: A review of the literature, Advances in Health Sciences Education: Theory and Practice, 21, 1087, 10.1007/s10459-015-9645-6 Donker, 2014, Effectiveness of learning strategy instruction on academic performance: A meta-analysis, Educational Research Review, 11, 1, 10.1016/j.edurev.2013.11.002 Donnelly, 2010, Harmonizing technology with interaction in blended problem-based learning, Computers & Education, 54, 350, 10.1016/j.compedu.2009.08.012 Donoghue, 2021, A meta-analysis of ten learning techniques, Frontiers in Education, 6, 10.3389/feduc.2021.581216 Edmunds, 2009, Conceptions of learning, approaches to studying and personal development in UK higher education, The British Journal of Educational Psychology, 79, 295, 10.1348/000709908X368866 Enwistle, 2000, Approaches to studying and levels of understanding: The influences of teaching and assessment, Higher Education, 15, 156 Fan, 2022, Revealing the regulation of learning strategies of MOOC retakers: A learning analytic study, Computers & Education, 178, 10.1016/j.compedu.2021.104404 Fincham, 2019, From study tactics to learning strategies: An analytical method for extracting interpretable representations, IEEE Transactions on Learning Technologies, 12, 59, 10.1109/TLT.2018.2823317 Fryer, 2017, (Latent) transitions to learning at university: A latent profile transition analysis of first-year Japanese students, Higher Education, 73, 519, 10.1007/s10734-016-0094-9 Gabadinho, 2011, Analyzing and visualizing state sequences in R with TraMineR, Journal of Statistical Software, 40, 1, 10.18637/jss.v040.i04 Gašević, 2017, Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance, Journal of Learning Analytics, 4, 113, 10.18608/jla.2017.42.10 Geitz, 2016, Changing learning behaviour: Self-efficacy and goal orientation in PBL groups in higher education, International Journal of Educational Research, 75, 146, 10.1016/j.ijer.2015.11.001 Hattie, 2016, Learning strategies: A synthesis and conceptual model, NPJ Science of Learning, 1, 16013, 10.1038/npjscilearn.2016.13 Helske, 2019, Mixture hidden Markov models for sequence data: The seqHMM package in R, Journal of Statistical Software, 88, 10.18637/jss.v088.i03 Helske, 2018, Combining sequence analysis and hidden Markov models in the analysis of complex life sequence data, Life Course Research and Social Policies, 185, 10.1007/978-3-319-95420-2_11 Holm, 1979, A simple sequentially rejective multiple test procedure, Scandinavian Journal of Statistics, Theory and Applications, 6, 65 Hosenfeld, 1976, Learning about learning: Discovering our students’ strategies, Foreign Language Annals, 9, 117, 10.1111/j.1944-9720.1976.tb02637.x Ifenthaler, 2020, Utilising learning analytics to support study success in higher education: A systematic review, Educational Technology Research and Development: ETR & D, 10.1007/s11423-020-09788-z Jovanović, 2020, Supporting actionable intelligence: reframing the analysis of observed study strategies, Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, 161, 10.1145/3375462.3375474 Jovanović, 2017, Learning analytics to unveil learning strategies in a flipped classroom, Internet and Higher Education, 33, 74, 10.1016/j.iheduc.2017.02.001 Jovanović, 2021, Students matter the most in learning analytics: The effects of internal and instructional conditions in predicting academic success, Computers & Education, 172, 104251, 10.1016/j.compedu.2021.104251 Kinnebrew, 2013, A contextualized, differential sequence mining method to derive students’ learning behavior patterns, Journal of Educational Data Mining, 5, 190 Kovanović, 2015, Analytics of communities of inquiry: Effects of learning technology use on cognitive presence in asynchronous online discussions, The Internet and Higher Education, 27, 74, 10.1016/j.iheduc.2015.06.002 Kovanović, 2016, Profiling MOOC course returners: How does student behavior change between two course enrollments?, 269 Lietz, 2010, The effects of college students’ personal values on changes in learning approaches, Research in Higher Education, 51, 65, 10.1007/s11162-009-9147-6 López-Pernas, 2021, Putting it all together: Combining learning analytics methods and data sources to understand students’ approaches to learning programming, Sustainability, 13, 4825, 10.3390/su13094825 Loyens, 2008, Self-directed learning in problem-based learning and its relationships with self-regulated learning, Educational Psychology Review, 20, 411, 10.1007/s10648-008-9082-7 Marton, 2006, Sameness and difference in transfer, Journal of the Learning Sciences, 15, 499, 10.1207/s15327809jls1504_3 Marton, 1976, On qualitative differences in learning: I-outcome and process, The British Journal of Educational Psychology, 46, 4, 10.1111/j.2044-8279.1976.tb02980.x Matcha, 2019, Detection of learning strategies: A comparison of process, sequence and network analytic approaches, 525, 10.1007/978-3-030-29736-7_39 Matcha, 2019, Analytics of learning strategies: Associations with academic performance and feedback, 461 Matcha, 2020, Analytics of learning strategies: Role of course design and delivery modality, Journal of Learning Analytics, 7, 45, 10.18608/jla.2020.72.3 Matthias, 2014 Mirriahi, 2018, Identifying engagement patterns with video annotation activities: A case study in professional development, Australasian Journal of Educational Technology, 34, 57, 10.14742/ajet.3207 Ottenstein, 2020, Recall bias in emotional intensity ratings: Investigating person-level and event-level predictors, Motivation and Emotion, 44, 464, 10.1007/s11031-019-09796-4 Oxford, 2002, Language learning strategies in a nutshell: Update and ESL suggestions, Methodology in Language Teaching, 124, 10.1017/CBO9780511667190.018 Parpala, 2010, Students’ approaches to learning and their experiences of the teaching-learning environment in different disciplines, The British Journal of Educational Psychology, 80, 269, 10.1348/000709909X476946 Patil, 2021, Visualizations with statistical details: The’ggstatsplot’approach, Journal of Open Source Software, 6, 10.21105/joss.03167 Pintrich, 2000, Multiple goals, multiple pathways: The role of goal orientation in learning and achievement, Journal of Educational Psychology, 92, 544, 10.1037/0022-0663.92.3.544 Poquet, 2021, Recommendations for network research in learning analytics: To open a conversation Saint, 2022, Temporally-focused analytics of self-regulated learning: A systematic review of literature, Computers and Education: Artificial Intelligence, 3 Saqr, 2021, The longitudinal trajectories of online engagement over a full program, Computers & Education, 175, 104325, 10.1016/j.compedu.2021.104325 Saqr, M., & Lopez-Pernas, S. (2021b). Idiographic learning analytics: A single student (N = 1) approach using psychological networks. In Companion Proceedings of the 11th International Conference on Learning Analytics & Knowledge (LAK 2021), 12–16 April 2021, Irvine, CA, USA (pp. 456–463). https://doi.org/10.13140/RG.2.2.10956.13443. Saqr, 2022, How CSCL roles emerge, persist, transition, and evolve over time: A four-year longitudinal study, Computers & Education, 189, 104581, 10.1016/j.compedu.2022.104581 Saqr, 2021, The relational, co-temporal, contemporaneous, and longitudinal dynamics of self-regulation for academic writing, Research and Practice in Technology Enhanced Learning, 16, 29, 10.1186/s41039-021-00175-7 Scharff, 2017, Exploring metacognition as support for learning transfer, Teaching & Learning Inquiry The ISSOTL Journal, 5, 10.20343/teachlearninqu.5.1.6 Schraw, 2006, Promoting self-regulation in science education: Metacognition as part of a broader perspective on learning, Research in Science Education, 36, 111, 10.1007/s11165-005-3917-8 Schuster, 2020, Transfer of metacognitive skills in self-regulated learning: An experimental training study, Metacognition and Learning, 15, 455, 10.1007/s11409-020-09237-5 Sher, 2020, Analyzing the consistency in within-activity learning patterns in blended learning, ACM International Conference Proceeding Series, 1 Siemens, 2013, Learning analytics: The emergence of a discipline, The American Behavioral Scientist, 57, 1380, 10.1177/0002764213498851 Siemens, 2014, Supporting and promoting learning analytics research, Journal of Learning Analytics, 1, 3, 10.18608/jla.2014.11.2 Stefanou, 2013, Self-regulation and autonomy in problem- and project-based learning environments, Active Learning in Higher Education, 14, 109, 10.1177/1469787413481132 Studer, 2013 Tait, 1996, Identifying students at risk through ineffective study strategies, Higher Education, 31, 97, 10.1007/BF00129109 Tan, 2018, Learning profiles, behaviors and outcomes: Investigating international students’ learning experience in an English MOOC, 214 Theobald, 2021, Self-regulated learning training programs enhance university students’ academic performance, self-regulated learning strategies, and motivation: A meta-analysis, Contemporary Educational Psychology, 66 Tomczak, 2014, The need to report effect size estimates revisited. An overview of some recommended measures of effect size, Trends in Sport Sciences, 21, 19 Torrance, 2000, Individual differences in undergraduate essay-writing strategies: A longitudinal study, Higher Education, 39, 181, 10.1023/A:1003990432398 Uzir, 2020, Analytics of time management and learning strategies for effective online learning in blended environments, 392 Uzir, 2020, Analytics of time management strategies in a flipped classroom, Journal of Computer Assisted Learning, 36, 70, 10.1111/jcal.12392 Vanthournout, 2013, Assessing students’ development in learning approaches according to initial learning profiles: A person-oriented perspective, Studies in Educational Evaluation, 39, 33, 10.1016/j.stueduc.2012.08.002 Veenman, 2006, Metacognition and learning: Conceptual and methodological considerations, Metacognition and Learning, 1, 3, 10.1007/s11409-006-6893-0 Vermetten, 1999, Consistency and variability of learning strategies in different university courses, Higher Education, 37, 1, 10.1023/A:1003573727713 Watkins, 1985, A longitudinal study of the approaches to learning of Australian tertiary students, Human Learning: Journal of Practical Research & Applications, 4, 127 Wilding, 2006, Life goals, approaches to study and performance in an undergraduate cohort, The British Journal of Educational Psychology, 76, 171, 10.1348/000709904X24726 Winne, 2020, Construct and consequential validity for learning analytics based on trace data, Computers in Human Behavior, 112 Wood, 2003, Problem based learning, BMJ, 326, 328, 10.1136/bmj.326.7384.328 Yasmin., 2013, Application of the classification tree model in predicting learner dropout behaviour in open and distance learning, Distance Education, 34, 218, 10.1080/01587919.2013.793642 Zacharis, 2015, A multivariate approach to predicting student outcomes in web-enabled blended learning courses, Internet and Higher Education, 27, 44, 10.1016/j.iheduc.2015.05.002 Zeegers, 2004, Student learning in higher education: a path analysis of academic achievement in science, Higher Education Research & Development, 23, 35, 10.1080/0729436032000168487 Zhen, 2020, Trajectory patterns of academic engagement among elementary school students: The implicit theory of intelligence and academic self-efficacy matters, The British Journal of Educational Psychology, 90, 618, 10.1111/bjep.12320 Zheng, 2020, Profiling self-regulation behaviors in STEM learning of engineering design, Computers & Education, 143, 10.1016/j.compedu.2019.103669