The Learning Tracker: A Learner Dashboard that Encourages Self-regulation in MOOC Learners

Massive Open Online Courses (MOOCs) have the potential to make quality education affordable and available to the masses and reduce the gap between the most privileged and the most disadvantaged learners worldwide. However, this potential is overshadowed by low completion rates, often below 15%. Due to the high level of autonomy that is required when learning with a MOOC, literature identifies limited self-regulated learning skills as one of the causes that lead to early dropouts in MOOCs. Moreover, existing tools designed to aid learners in the online learning environment fail to provide the support needed for the development of such skills. The aim of the present work is to bridge this gap by investigating how self-regulated learning skills can be enhanced by encouraging metacognition and reflection in MOOC learners by means of social comparison. To this end, following an iterative process, we have developed the Learning Tracker, an interactive widget which allows learners to visualise their learning behaviour and compare it to that of previous graduates of the same MOOC. Each iteration was extensively evaluated in live TU Delft MOOCs running on the edX platform while engaging over 20.000 MOOC learners over the whole duration of each MOOC. Our results show that learners that have access to the Learning Tracker are more likely to graduate the MOOC. Moreover, we have observed that the widget has a positive impact on learners' engagement and reduces procrastination. However, we have little evidence that learners improved their self-regulated learning skills by the end of the MOOCs. Based on our results, we argue that the mere fact of receiving feedback on a limited number of learning habits could trigger self-reflection in learners and lead to improved learner performance. This work underlines the powerful effect feedback and self-reflection on one's behaviour has on learning performance. We recommend that future research should investigate learners' feedback literacy and devise effective ways of presenting learners with personalised feedback based on their goals, learning skill level and cultural background.

[1]  Judy Kay,et al.  Student Models that Invite the Learner In: The SMILI: () Open Learner Modelling Framework , 2007, Int. J. Artif. Intell. Educ..

[2]  Len Tischler,et al.  The growing interest in spirituality in business , 1999 .

[3]  Judy Kay,et al.  MOOCs: So Many Learners, So Much Potential ... , 2013, IEEE Intelligent Systems.

[4]  Xin Chen,et al.  Checkable answers: Understanding student behaviors with instant feedback in a blended learning class , 2015, 2015 IEEE Frontiers in Education Conference (FIE).

[5]  Martin D. Snyder MOOCs ( Massive Open Online Courses ) Journals and Blogs , 2016 .

[6]  Yeonjeong Park,et al.  Development of the Learning Analytics Dashboard to Support Students' Learning Performance , 2015, J. Univers. Comput. Sci..

[7]  Mirjam Hauck,et al.  MOOCs: striking the right balance between facilitation and self-determination , 2014 .

[8]  Kevin C. Almeroth,et al.  Moodog: Tracking students' Online Learning Activities , 2007 .

[9]  Geert-Jan Houben,et al.  Retrieval Practice and Study Planning in MOOCs: Exploring Classroom-Based Self-regulated Learning Strategies at Scale , 2016, EC-TEL.

[10]  Rebecca Ferguson,et al.  Learning analytics: drivers, developments and challenges , 2012 .

[11]  Douglas Kauffman Self-Regulated Learning in Web-Based Environments: Instructional Tools Designed to Facilitate Cognitive Strategy Use, Metacognitive Processing, and Motivational Beliefs , 2004 .

[12]  Doug Clow,et al.  The learning analytics cycle: closing the loop effectively , 2012, LAK.

[13]  Jeff Grann,et al.  Competency map: visualizing student learning to promote student success , 2014, LAK.

[14]  Antoine Doucet,et al.  Building engagement for MOOC students: introducing support for time management on online learning platforms , 2014, WWW.

[15]  Justin Reich,et al.  Staggered Versus All-At-Once Content Release in Massive Open Online Courses: Evaluating a Natural Experiment , 2015, L@S.

[16]  Trupti M. Kodinariya,et al.  Review on determining number of Cluster in K-Means Clustering , 2013 .

[17]  George Siemens,et al.  Guest Editorial - Learning and Knowledge Analytics , 2012, J. Educ. Technol. Soc..

[18]  Vania Dimitrova,et al.  Visualising student tracking data to support instructors in web-based distance education , 2004, WWW Alt. '04.

[19]  Yeonjeong Park,et al.  Educational Dashboards for Smart Learning: Review of Case Studies , 2014, ICSLE.

[20]  Allison Littlejohn,et al.  Instructional quality of Massive Open Online Courses (MOOCs) , 2015, Comput. Educ..

[21]  M. Boekaerts SELF-REGULATED LEARNING: A NEW CONCEPT EMBRACED BY RESEARCHERS, POLICY MAKERS, EDUCATORS, TEACHERS, AND STUDENTS , 1997 .

[22]  Justin Reich,et al.  Evaluating Geographic Data in MOOCs , 2013 .

[23]  J. Broadbent,et al.  Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review , 2015, Internet High. Educ..

[24]  Scott G. Paris,et al.  The Constructivist Approach to Self-Regulation and Learning in the Classroom , 1989 .

[25]  Sherif A. Halawa,et al.  Dropout Prediction in MOOCs using Learner Activity Features , 2014 .

[26]  Rebecca Ferguson,et al.  Moving Through MOOCS: Pedagogy, Learning Design and Patterns of Engagement , 2015, EC-TEL.

[27]  Judy Kay,et al.  Learner Know Thyself: Student Models to Give Learner Control and Responsibility , 1997 .

[28]  Reid Hastie,et al.  Order in Choice , 2009, Psychological science.

[29]  Justin Reich,et al.  Characterizing Video Use in the Catalogue of MITx MOOCs , 2014 .

[30]  Gayle S. Christensen,et al.  The MOOC Phenomenon: Who Takes Massive Open Online Courses and Why? , 2013 .

[31]  Chris Piech,et al.  Deconstructing disengagement: analyzing learner subpopulations in massive open online courses , 2013, LAK '13.

[32]  Markus A. Maier,et al.  Color and psychological functioning: the effect of red on performance attainment. , 2007, Journal of experimental psychology. General.

[33]  Erik Duval,et al.  Learning Analytics Dashboard Applications , 2013 .

[34]  Vincent Aleven,et al.  Supporting Students' Self-Regulated Learning with an Open Learner Model in a Linear Equation Tutor , 2013, AIED.

[35]  Catherine McLoughlin,et al.  The pedagogy of personalised learning: exemplars, MOOCS and related learning theories , 2013 .

[36]  Judy Kay,et al.  MOOClm: User Modelling for MOOCs , 2015, UMAP.

[37]  Birgitta König-Ries,et al.  Open social student modeling: visualizing student models with parallel introspectiveviews , 2011, UMAP'11.

[38]  A. Kluger,et al.  The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. , 1996 .

[39]  Eleonora Papleontiou-louca,et al.  The concept and instruction of metacognition , 2003 .

[40]  Geert-Jan Houben,et al.  Encouraging Metacognition & Self-Regulation in MOOCs through Increased Learner Feedback , 2016, LAL@LAK.

[41]  Il-Hyun Jo,et al.  Effects of learning analytics dashboard: analyzing the relations among dashboard utilization, satisfaction, and learning achievement , 2015, Asia Pacific Education Review.

[42]  Matthieu Cisel,et al.  Analyzing Completion Rates in the First French xMOOC , 2014 .

[43]  Justin Reich,et al.  HarvardX and MITx: The First Year of Open Online Courses, Fall 2012-Summer 2013 , 2014 .

[44]  Ryen W. White,et al.  The search dashboard: how reflection and comparison impact search behavior , 2012, CHI.

[45]  Jon Billsberry MOOCs Fad or Revolution , 2013 .

[46]  Zhenming Liu,et al.  Learning about Social Learning in MOOCs: From Statistical Analysis to Generative Model , 2013, IEEE Transactions on Learning Technologies.

[47]  Carlos Delgado Kloos,et al.  Inferring higher level learning information from low level data for the Khan Academy platform , 2013, LAK '13.

[48]  B. Zimmerman Attaining self-regulation: A social cognitive perspective. , 2000 .

[49]  Ronghuai Huang,et al.  Characteristics of distance learners: research on relationships of learning motivation, learning strategy, self‐efficacy, attribution and learning results , 2008 .

[50]  B. Zimmerman Becoming a Self-Regulated Learner: An Overview , 2002 .

[51]  Arjumand Younos Online education for developing contexts , 2012 .

[52]  Jane Sinclair,et al.  Dropout rates of massive open online courses : behavioural patterns , 2014 .

[53]  Milton Chen Visualizing the pulse of a classroom , 2003, MULTIMEDIA '03.

[54]  George Siemens,et al.  Learning analytics and educational data mining: towards communication and collaboration , 2012, LAK.

[55]  L. Festinger A Theory of Social Comparison Processes , 1954 .

[56]  Matthew D. Pistilli,et al.  Course signals at Purdue: using learning analytics to increase student success , 2012, LAK.

[57]  Erik Duval,et al.  Addressing learner issues with StepUp!: an evaluation , 2013, LAK '13.

[58]  Susan Bull Preferred Features of Open Learner Models for University Students , 2012, ITS.

[59]  Katharina Reinecke,et al.  Demographic differences in how students navigate through MOOCs , 2014, L@S.

[60]  Erik Duval,et al.  Success, activity and drop-outs in MOOCs an exploratory study on the UNED COMA courses , 2014, LAK.

[61]  Susan Bull,et al.  CALMsystem: A Conversational Agent for Learner Modelling , 2007, Knowl. Based Syst..

[62]  Kamal Bijlani,et al.  Discovering Learning Models in MOOCs Using Empirical Data , 2015 .

[63]  Joseph Jay Williams,et al.  Beyond Time-on-Task: The Relationship between Spaced Study and Certification in MOOCs , 2015, J. Learn. Anal..

[64]  Antonija Mitrovic,et al.  Evaluating the Effect of Open Student Models on Self-Assessment , 2007, Int. J. Artif. Intell. Educ..

[65]  Patrick Jermann,et al.  Identifying Styles and Paths toward Success in MOOCs , 2015, EDM.

[66]  Eric N. Wiebe,et al.  MOOCs From the Viewpoint of the Learner , 2015 .

[67]  Daniel M. Russell,et al.  Student skill and goal achievement in the mapping with google MOOC , 2014, L@S.

[68]  Dragan Gasevic,et al.  Open Learning Analytics: an integrated modularized platform , 2011 .

[69]  Gráinne Conole,et al.  MOOCs as disruptive technologies: strategies for enhancing the learner experience and quality of MOOCs Los MOOC como tecnologías disruptivas: estrategias para mejorar la experiencia de aprendizaje y la calidad de los MOOC. , 2016 .

[70]  Li Yuan,et al.  MOOCs and open education: Implications for higher education , 2013 .

[71]  Barry Smyth,et al.  Are people biased in their use of search engines? , 2008, CACM.

[72]  George Siemens,et al.  Courses : Innovation in Education ? , 2013 .

[73]  Carlos Delgado Kloos,et al.  ALAS-KA: A learning analytics extension for better understanding the learning process in the Khan Academy platform , 2015, Comput. Hum. Behav..

[74]  Rebecca Ferguson,et al.  Innovating Pedagogy 2015: Open University Innovation Report 4 , 2015 .

[75]  Abdellatif Medouri,et al.  LASyM: A Learning Analytics System for MOOCs , 2013 .

[76]  Peter Brusilovsky,et al.  Mastery grids: an open-source social educational progress visualization , 2014, ITiCSE '14.

[77]  Vania Dimitrova,et al.  CourseVis: A graphical student monitoring tool for supporting instructors in web-based distance courses , 2007, Int. J. Hum. Comput. Stud..

[78]  Judy Kay,et al.  Metacognition and Open Learner Models , 2008 .

[79]  Sherif Halawa,et al.  Attrition and Achievement Gaps in Online Learning , 2015, L@S.

[80]  Jim E. Greer,et al.  Interacting with Inspectable Bayesian Student Models , 2004, Int. J. Artif. Intell. Educ..

[81]  J. Flavell Metacognition and Cognitive Monitoring: A New Area of Cognitive-Developmental Inquiry. , 1979 .

[82]  Daniel A. McFarland,et al.  Encouraging Forum Participation in Online Courses with Collectivist, Individualist and Neutral Motivational Framings , 2014 .

[83]  Tatjana Welzer,et al.  Learning Technology for Education in Cloud , 2015, Communications in Computer and Information Science.

[84]  Cristóbal Cobo,et al.  The real component of virtual learning: motivations for face-to-face MOOC meetings in developing and industrialised countries , 2015 .

[85]  Erik Duval,et al.  Empowering Students to Reflect on their Activity with StepUp!: Two Case Studies with Engineering Students , 2012, ARTEL@EC-TEL.

[86]  Agnes Kukulska-Hulme,et al.  Challenges for conceptualising EU MOOC for vulnerable learner groups , 2014 .

[87]  B. Zimmerman Self-Regulated Learning and Academic Achievement: An Overview , 1990 .

[88]  Erik Duval,et al.  Visualizing Activities for Self-reflection and Awareness , 2010, ICWL.

[89]  Judy Kay,et al.  Open Learner Models , 2010, Advances in Intelligent Tutoring Systems.

[90]  Kenneth R. Koedinger,et al.  Learning is Not a Spectator Sport: Doing is Better than Watching for Learning from a MOOC , 2015, L@S.

[91]  Carlos Delgado Kloos,et al.  GLASS: a learning analytics visualization tool , 2012, LAK '12.

[92]  Laxmisha Rai,et al.  Influencing Factors of Success and Failure in MOOC and General Analysis of Learner Behavior , 2016 .

[93]  Peter Brusilovsky,et al.  An Intelligent Interface for Learning Content: Combining an Open Learner Model and Social Comparison to Support Self-Regulated Learning and Engagement , 2016, IUI.

[94]  H. Pashler,et al.  Distributed practice in verbal recall tasks: A review and quantitative synthesis. , 2006, Psychological bulletin.

[95]  A. Maslow A Theory of Human Motivation , 1943 .

[96]  Bart Rienties,et al.  The impact of learning design on student behaviour, satisfaction and performance: A cross-institutional comparison across 151 modules , 2016, Comput. Hum. Behav..

[97]  Anastasia Kitsantas,et al.  Enhancing self-regulation of practice: the influence of graphing and self-evaluative standards , 2007 .

[98]  Erik Duval,et al.  Goal-oriented visualizations of activity tracking: a case study with engineering students , 2012, LAK.

[99]  M. Carter Visible learning: a synthesis of over 800 meta‐analyses relating to achievement , 2009 .

[100]  Tom McKlin,et al.  The challenges of using a MOOC to introduce "absolute beginners" to programming on specialized hardware , 2014, L@S.

[101]  Cathy Sandeen Integrating MOOCS into Traditional Higher Education: The Emerging “MOOC 3.0” Era , 2013 .

[102]  R. Azevedo,et al.  The Role of Self-Regulated Learning in Fostering Students' Conceptual Understanding of Complex Systems with Hypermedia , 2004 .

[103]  René F. Kizilcec,et al.  Motivation as a Lens to Understand Online Learners , 2015, ACM Trans. Comput. Hum. Interact..

[104]  Kyparisia A. Papanikolaou,et al.  Constructing Interpretative Views of Learners’ Interaction Behavior in an Open Learner Model , 2015, IEEE Transactions on Learning Technologies.

[105]  C. Bremer,et al.  HOW TO ANALYZE PARTICIPATION IN A (C) MOOC , 2013 .

[106]  Ryan Shaun Joazeiro de Baker,et al.  Stupid Tutoring Systems, Intelligent Humans , 2016, International Journal of Artificial Intelligence in Education.

[107]  Yianna Vovides,et al.  Elusive Learning - Using Learning Analytics to Support Reflective Sensemaking of Ill-Structured Ethical Problems: A Learner-Managed Dashboard Solution , 2016, Future Internet.

[108]  Larry Ambrose,et al.  The power of feedback. , 2002, Healthcare executive.

[109]  María Jesús Rodríguez-Triana,et al.  Understanding learning at a glance: an overview of learning dashboard studies , 2016, LAK.

[110]  P. Pintrich The Role of Metacognitive Knowledge in Learning, Teaching, and Assessing , 2002 .

[111]  David E. Pritchard,et al.  Studying Learning in the Worldwide Classroom Research into edX's First MOOC. , 2013 .

[112]  S. Kuhar,et al.  Taking Advantage of Education Data: Advanced Data Analysis and Reporting in Virtual Learning Environments , 2011 .

[113]  E H Cooper,et al.  The total-time hypothesis in verbal learning. , 1967, Psychological bulletin.

[114]  Mark Warschauer,et al.  Predicting MOOC performance with Week 1 Behavior , 2014, EDM.

[115]  Judy Kay,et al.  New Opportunities with Open Learner Models and Visual Learning Analytics , 2015, AIED.

[116]  Ruth Cobos Pérez,et al.  Open-DLAs: An Open Dashboard for Learning Analytics , 2016, L@S.

[117]  Design-Based Research: An Emerging Paradigm for Educational Inquiry , 2003 .

[118]  Doug Clow,et al.  MOOCs and the funnel of participation , 2013, LAK '13.

[119]  Frank Linton,et al.  Recommender Systems for Learning: Building User and Expert Models through Long-Term Observation of Application Use , 2000, User Modeling and User-Adapted Interaction.

[120]  Karl Steffens,et al.  Are MOOCs Promising Learning Environments , 2015 .

[121]  Barry J. Zimmerman,et al.  Developing Self-Regulated Learners: Beyond Achievement to Self-Efficacy , 1996 .

[122]  Judy Kay,et al.  SMILI☺: a Framework for Interfaces to Learning Data in Open Learner Models, Learning Analytics and Related Fields , 2016, International Journal of Artificial Intelligence in Education.

[123]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[124]  Erik Duval,et al.  Learning dashboards: an overview and future research opportunities , 2013, Personal and Ubiquitous Computing.

[125]  Eva Durall,et al.  Learning Analytics as a Metacognitive Tool , 2014, CSEDU.

[126]  Mar Pérez-Sanagustín,et al.  Recommending Self-Regulated Learning Strategies Does Not Improve Performance in a MOOC , 2016, L@S.

[127]  Carlos Alario-Hoyos,et al.  Scaffolding Self-learning in MOOCs , 2014 .

[128]  Donatella Persico,et al.  Self-Regulated Learning in Technology Enhanced Learning Environments , 2011 .

[129]  P. Winne,et al.  Feedback and Self-Regulated Learning: A Theoretical Synthesis , 1995 .

[130]  van der Wmp Wil Aalst,et al.  Uncovering learning patterns in a MOOC through conformance alignments , 2015 .

[131]  Björn Hartmann,et al.  Should your MOOC forum use a reputation system? , 2014, CSCW.

[132]  John Champaign,et al.  Learning in an introductory physics MOOC: All cohorts learn equally, including an on-campus class , 2014 .

[133]  Kasia Muldner,et al.  Exploring the Impact of a Learning Dashboard on Student Affect , 2015, AIED.

[134]  S. Stein What's All the Fuss About? , 1996 .

[135]  Hugh C. Davis,et al.  Visualising the MOOC experience: a dynamic MOOC dashboard built through institutional collaboration , 2016 .

[136]  Erik Duval,et al.  The student activity meter for awareness and self-reflection , 2012, CHI Extended Abstracts.