Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs

ABSTRACT The emergence of massive open online courses not only changes the ecology of higher education, but also facilitates a blending learning paradigm, also known as small private online courses (SPOCs). In order to understand how college students interact with an SPOC platform, this study collects their online behaviors for a semester and adopts a lag sequential analysis approach to identify significant transitions between interactions with content, peers, and instructors. Regarding content, after entering courses, the students tend to access learning resources. Besides, the transitions between learning resources and personal performance are significantly interconnected to each other. Regarding peers, the interaction with classmates was mainly connected to the access of assignments and performance. Regarding instructors, the interaction with teachers was minor but connected to all other behaviors. In addition, the results also show that students’ online behavioral patterns in SPOCs may change over time. The implications of the findings for SPOCs research are discussed in this paper.

[1]  Arthur C. Graesser,et al.  Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments , 2010, Int. J. Hum. Comput. Stud..

[2]  Douglas H. Fisher,et al.  Wrapping a MOOC: Student Perceptions of an Experiment in Blended Learning , 2013 .

[3]  Tobias Hecking,et al.  Analysis of Dynamic Resource Access Patterns in Online Courses , 2014, J. Learn. Anal..

[4]  Jon Baggaley MOOC postscript , 2014 .

[5]  Philippa Hill,et al.  Online Educational Delivery Models: A Descriptive View , 2012 .

[6]  Carlos Delgado Kloos,et al.  Precise Effectiveness Strategy for analyzing the effectiveness of students with educational resources and activities in MOOCs , 2015, Comput. Hum. Behav..

[7]  K. Robert Lai,et al.  Who will pass? Analyzing learner behaviors in MOOCs , 2016, Res. Pract. Technol. Enhanc. Learn..

[8]  Anita Stepan,et al.  MASSIVE OPEN ONLINE COURSES (MOOC) DISRUPTIVE IMPACT ON HIGHER EDUCATION , 2013 .

[9]  Alla L. Nazarenko,et al.  Blended Learning vs Traditional Learning: What Works? (A Case Study Research)☆ , 2015 .

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

[11]  Min Liu,et al.  Examining learners’ perspective of taking a MOOC: reasons, excitement, and perception of usefulness , 2015 .

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

[13]  Roger Bakeman,et al.  Analyzing Interaction: Sequential Analysis with SDIS and GSEQ , 1995 .

[14]  Matthew M. Chingos,et al.  Interactive Learning Online at Public Universities: Evidence from a Six-Campus Randomized Trial. , 2014 .

[15]  Barbara Means,et al.  Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies , 2009 .

[16]  Salman Khan,et al.  The One World Schoolhouse: Education Reimagined , 2012 .

[17]  Brenda K. Bryant,et al.  Socialization: The "Implicit Curriculum" of Learning Environments. , 1978 .

[18]  Bilge Mutlu,et al.  Designing motivational agents: The role of praise, social comparison, and embodiment in computer feedback , 2011, Comput. Hum. Behav..

[19]  Chaoyun Liang,et al.  Constructing and evaluating online goal-setting mechanisms in web-based portfolio assessment system for facilitating self-regulated learning , 2013, Comput. Educ..

[20]  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.

[21]  Huei-Tse Hou,et al.  Integrating cluster and sequential analysis to explore learners' flow and behavioral patterns in a simulation game with situated-learning context for science courses: A video-based process exploration , 2015, Comput. Hum. Behav..

[22]  Xin Chen,et al.  Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization , 2016, Comput. Hum. Behav..

[23]  P. Allison,et al.  Analyzing Sequential Categorical Data on Dyadic Interaction: A Comment on Gottman , 1982 .

[24]  Hercy N. H. Cheng,et al.  Equal opportunity tactic: Redesigning and applying competition games in classrooms , 2009, Comput. Educ..

[25]  Armando Fox,et al.  From MOOCs to SPOCs , 2013, CACM.

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

[27]  Anirban Dasgupta,et al.  Superposter behavior in MOOC forums , 2014, L@S.

[28]  Allan Jeong A Guide to Analyzing Message–Response Sequences and Group Interaction Patterns in Computer‐mediated Communication , 2005 .

[29]  Katy Jordan,et al.  Initial trends in enrolment and completion of massive open online courses , 2014 .

[30]  Estrella Pulido,et al.  Using a SPOC to flip the classroom , 2015, 2015 IEEE Global Engineering Education Conference (EDUCON).

[31]  Yoav Bergner,et al.  Who does what in a massive open online course? , 2014, Commun. ACM.

[32]  Jure Leskovec,et al.  Engaging with massive online courses , 2014, WWW.

[33]  Karen Swan,et al.  LEARNING EFFECTIVENESS ONLINE: WHAT THE RESEARCH TELLS US , 2003 .

[34]  Bertrand Meyer,et al.  SPOC-supported introduction to programming , 2014, ITiCSE '14.

[35]  Yuan Wang Exploring Possible Reasons behind Low Student Retention Rates of Massive Online Open Courses: A Comparative Case Study from a Social Cognitive Perspective , 2013, AIED Workshops.