The importance and meaning of session behaviour in a MOOC
暂无分享,去创建一个
James Bailey | Tracii Ryan | Paula G. de Barba | Donia Malekian | Eduardo Araujo Oliveira | Gregor E. Kennedy | J. Bailey | G. Kennedy | Tracii Ryan | D. Malekian | P. D. Barba | E. Oliveira
[1] Hugh C. Davis,et al. Modelling MOOC learners' social behaviours , 2020, Comput. Hum. Behav..
[2] Bart Rienties,et al. Investigating variation in learning processes in a FutureLearn MOOC , 2019, J. Comput. High. Educ..
[3] Vianney Perchet,et al. Markov Decision Process for MOOC Users Behavioral Inference , 2019, EMOOCs.
[4] Bruno Poellhuber,et al. Understanding Participant’s Behaviour in Massively Open Online Courses , 2019, The International Review of Research in Open and Distributed Learning.
[5] Abelardo Pardo,et al. From Study Tactics to Learning Strategies: An Analytical Method for Extracting Interpretable Representations , 2019, IEEE Transactions on Learning Technologies.
[6] B. Rienties,et al. Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students' Behaviours , 2018, J. Learn. Anal..
[7] Wil M. P. van der Aalst,et al. Analysing Structured Learning Behaviour in Massive Open Online Courses (MOOCs): An Approach Based on Process Mining and Clustering , 2018, The International Review of Research in Open and Distributed Learning.
[8] Mar Pérez-Sanagustín,et al. Tools to Support Self-Regulated Learning in Online Environments: Literature Review , 2018, EC-TEL.
[9] Scott W. Brown,et al. Timing Matters: Approaches for Measuring and Visualizing Behaviours of Timing and Spacing of Work in Self-Paced Online Teacher Professional Development Courses , 2018, J. Learn. Anal..
[10] Josh Gardner,et al. Student success prediction in MOOCs , 2017, User Modeling and User-Adapted Interaction.
[11] Linda Corrin,et al. A tale of two MOOCs: How student motivation and participation predict learning outcomes in different MOOCs , 2017 .
[12] Dragan Gasevic,et al. Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance , 2017, J. Learn. Anal..
[13] Dragan Gasevic,et al. Learning analytics to unveil learning strategies in a flipped classroom , 2017, Internet High. Educ..
[14] John W. Tukey,et al. Exploratory data analysis , 1977, Addison-Wesley series in behavioral science : quantitative methods.
[15] P. D. Barba. Autonomous learning and achievement motivation in online learning environments , 2017 .
[16] Mar Pérez-Sanagustín,et al. Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses , 2017, Comput. Educ..
[17] Sandra Milligan,et al. Understanding Learning and Learning Design in MOOCs: A Measurement-Based Interpretation , 2016, J. Learn. Anal..
[18] Geert-Jan Houben,et al. Retrieval Practice and Study Planning in MOOCs: Exploring Classroom-Based Self-regulated Learning Strategies at Scale , 2016, EC-TEL.
[19] Gi Woong Choi,et al. Understanding MOOC students: motivations and behaviours indicative of MOOC completion , 2016, J. Comput. Assist. Learn..
[20] Paula de Barba,et al. The role of students' motivation and participation in predicting performance in a MOOC , 2016, J. Comput. Assist. Learn..
[21] Abelardo Pardo,et al. Data2U: scalable real time student feedback in active learning environments , 2016, LAK.
[22] Marek Hatala,et al. Does Time-on-task Estimation Matter? Implications on Validity of Learning Analytics Findings , 2016, J. Learn. Anal..
[23] Allison Littlejohn,et al. Context counts: How learners' contexts influence learning in a MOOC , 2015, Comput. Educ..
[24] Marco Kalz,et al. Time will tell: The role of mobile learning analytics in self-regulated learning , 2015, Comput. Educ..
[25] J. Broadbent,et al. Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review , 2015, Internet High. Educ..
[26] Dongho Kim,et al. Constructing Proxy Variables to Measure Adult Learners. Time Management Strategies in LMS , 2015, J. Educ. Technol. Soc..
[27] Katy Jordan,et al. Massive Open Online Course Completion Rates Revisited: Assessment, Length and Attrition , 2015 .
[28] Sherif Halawa,et al. Attrition and Achievement Gaps in Online Learning , 2015, L@S.
[29] Allyson F. Hadwin,et al. Using multiple, contextualized data sources to measure learners’ perceptions of their self-regulated learning , 2015 .
[30] Allison Littlejohn,et al. Supporting Professional Learning in a Massive Open Online Course. , 2014 .
[31] Dietrich Albert,et al. A Framework for Facilitating Self-Regulation in Responsive Open Learning Environments , 2014, ArXiv.
[32] Antoine Doucet,et al. Building engagement for MOOC students: introducing support for time management on online learning platforms , 2014, WWW.
[33] Linda Corrin,et al. Visualizing patterns of student engagement and performance in MOOCs , 2014, LAK.
[34] Philip J. Guo,et al. How video production affects student engagement: an empirical study of MOOC videos , 2014, L@S.
[35] Eric Horvitz,et al. Why Stop Now? Predicting Worker Engagement in Online Crowdsourcing , 2013, HCOMP.
[36] David M. Shannon,et al. Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning , 2013 .
[37] Hitesh Gupta,et al. Global K-Means (GKM) Clustering Algorithm: A Survey , 2013 .
[38] Shirley Williams,et al. MOOCs: A systematic study of the published literature 2008-2012 , 2013 .
[39] Ryan S. Baker,et al. The Potentials of Educational Data Mining for Researching Metacognition, Motivation and Self-Regulated Learning , 2013, EDM 2013.
[40] Chris Piech,et al. Deconstructing disengagement: analyzing learner subpopulations in massive open online courses , 2013, LAK '13.
[41] V. Aleven,et al. Metacognition and Learning Technologies: An Overview of Current Interdisciplinary Research , 2013 .
[42] Trupti M. Kodinariya,et al. Review on determining number of Cluster in K-Means Clustering , 2013 .
[43] I. E. Allen,et al. Changing Course: Ten Years of Tracking Online Education in the United States. , 2013 .
[44] George Siemens,et al. Guest Editorial - Learning and Knowledge Analytics , 2012, J. Educ. Technol. Soc..
[45] J. W. Gikandi,et al. Online formative assessment in higher education: A review of the literature , 2011, Comput. Educ..
[46] O. O. Oladipupo,et al. Application of k Means Clustering algorithm for prediction of Students Academic Performance , 2010, ArXiv.
[47] R. Fisher. On the Interpretation of χ2 from Contingency Tables, and the Calculation of P , 2010 .
[48] K. Ready,et al. ONLINE COURSE EXPERIENCE MATTERS: INVESTIGATING STUDENTS' PERCEPTIONS OF ONLINE LEARNING , 2010 .
[49] Mike Joy,et al. A Self-Regulated Learning Approach: A Mobile Context-aware and Adaptive Learning Schedule (mCALS) Tool , 2008, Int. J. Interact. Mob. Technol..
[50] Sergei Vassilvitskii,et al. k-means++: the advantages of careful seeding , 2007, SODA '07.
[51] J. Metcalfe,et al. A Region of Proximal Learning Model of Study Time Allocation Journal of Memory and Language , 2005 .
[52] Joan L. Whipp,et al. Self-regulation in a web-based course: A case study , 2004 .
[53] P. Pintrich. A Conceptual Framework for Assessing Motivation and Self-Regulated Learning in College Students , 2004 .
[54] Julie Chen,et al. Time will tell. , 2003, Current surgery.
[55] Anne Nevgi,et al. Towards self-regulation in web-based learning , 2003 .
[56] Mike Sharples,et al. KLeOS: a personal, mobile, knowledge and learning organisation system , 2002, Proceedings. IEEE International Workshop on Wireless and Mobile Technologies in Education.
[57] A. Kitsantas,et al. Supporting Self-Regulation in Student-Centered Web-Based Learning Environments , 2002 .
[58] P. Pintrich. The role of goal orientation in self-regulated learning. , 2000 .
[59] Lisa K. Son,et al. Metacognitive and control strategies in study-time allocation. , 2000, Journal of experimental psychology. Learning, memory, and cognition.
[60] Anil K. Jain,et al. Data clustering: a review , 1999, CSUR.
[61] Philip H. Winne,et al. Studying as self-regulated learning. , 1998 .
[62] Mia Hubert,et al. Clustering in an object-oriented environment , 1997 .
[63] P. Pintrich. A Manual for the Use of the Motivated Strategies for Learning Questionnaire (MSLQ). , 1991 .
[64] P. Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .
[65] Mark Morgan,et al. Self-Monitoring of Attained Subgoals in Private Study. , 1985 .
[66] A. Tough. The adult's learning projects : a fresh approach to theory and practice in adult learning , 1979 .
[67] R. Fisher. On the Interpretation of χ2 from Contingency Tables, and the Calculation of P , 2018, Journal of the Royal Statistical Society Series A (Statistics in Society).