An enhanced learning analytics plugin for Moodle: student engagement and personalised intervention

Moodle, an open source Learning Management System (LMS), collects a large amount of data on student interactions within it, including content, assessments, and communication. Some of these data can be used as proxy indicators of student engagement, as well as predictors for performance. However, these data are difficult to interrogate and even more difficult to action from within Moodle. We therefore describe a design-based research narrative to develop an enhanced version of an open source Moodle Engagement Analytics Plugin (MEAP). Working with the needs of unit convenors and student support staff, we sought to improve the available information, the way it is represented, and create affordances for action based on this. The enhanced MEAP (MEAP+) allows analyses of gradebook data, assessment submissions, login metrics, and forum interactions, as well as direct action through personalised emails to students based on these analyses.

[1]  Larry Constantine,et al.  Structure and style in use cases for user interface design , 2001 .

[2]  Riccardo Mazza,et al.  Exploring Usage Analysis in Learning Systems: Gaining Insights From Visualisations , 2005 .

[3]  P. Massingham,et al.  Does Attendance Matter? An Examination of Student Attitudes, Participation, Performance and Attendance , 2006, Journal of University Teaching and Learning Practice.

[4]  Thomas C. Reeves,et al.  Design research from a technology perspective , 2006 .

[5]  David R. Thomas,et al.  A General Inductive Approach for Analyzing Qualitative Evaluation Data , 2006 .

[6]  Shane Dawson,et al.  Teaching smarter: how mining ICT data can inform and improve learning and teaching practice , 2008 .

[7]  Kara Dawson,et al.  Data for free: Using LMS activity logs to measure community in online courses , 2008, Internet High. Educ..

[8]  Shane Dawson,et al.  Mining LMS data to develop an "early warning system" for educators: A proof of concept , 2010, Comput. Educ..

[9]  Kimberly E. Arnold Signals: Applying Academic Analytics. , 2010 .

[10]  Jafar Habibi,et al.  Using Educational Data Mining Methods to Study the Impact of Virtual Classroom in E-Learning , 2010, EDM.

[11]  Abigail Selzer King,et al.  Using Signals for appropriate feedback: Perceptions and practices , 2011, Comput. Educ..

[12]  E. Duval Attention please!: learning analytics for visualization and recommendation , 2011, LAK.

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

[14]  Riccardo Mazza,et al.  MOCLog – Monitoring Online Courses with log data , 2012 .

[15]  Jason M. Lodge,et al.  Pigeon pecks and mouse clicks: Putting the learning back into learning analytics , 2012 .

[16]  Steven Lonn,et al.  Bridging the gap from knowledge to action: putting analytics in the hands of academic advisors , 2012, LAK '12.

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

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

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

[20]  Vernon C. Smith,et al.  Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses , 2012 .

[21]  Phillip Dawson,et al.  Workshop 1: Open-Source Learning Analytics and “what the student does” , 2012 .

[22]  Zane L. Berge,et al.  Learning analytics as a tool for closing the assessment loop in higher education , 2012 .

[23]  Shane Dawson,et al.  Numbers Are Not Enough. Why e-Learning Analytics Failed to Inform an Institutional Strategic Plan , 2012, J. Educ. Technol. Soc..

[24]  Deborah Richards,et al.  Learning analytics in higher education : a summary of tools and approaches , 2013 .

[25]  P. Prinsloo,et al.  Learning Analytics , 2013 .

[26]  Zdenek Zdráhal,et al.  Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment , 2013, LAK '13.

[27]  David Jones,et al.  The IRAC framework: locating the performance zone for learning analytics , 2013 .

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

[29]  Sebastián Ventura,et al.  Data mining in education , 2013, WIREs Data Mining Knowl. Discov..

[30]  Eitel J. M. Lauría,et al.  Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative , 2014, J. Learn. Anal..

[31]  James E. Willis,et al.  PassNote: A Feedback Tool for Improving Student Success Outcomes , 2014 .

[32]  David Jones,et al.  Three paths for learning analytics and beyond , 2014, ASCILITE Publications.

[33]  David Jones,et al.  Breaking BAD to bridge the reality/rhetoric chasm , 2014 .

[34]  Miguel Ángel Conde González,et al.  Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning , 2014, Comput. Hum. Behav..

[35]  Linda Corrin,et al.  Completing the loop : returning learning analytics to teachers , 2014 .

[36]  Abelardo Pardo Designing Learning Analytics Experiences , 2014 .

[37]  Maren Scheffel,et al.  Quality Indicators for Learning Analytics , 2014, J. Educ. Technol. Soc..

[38]  George Siemens,et al.  Let’s not forget: Learning analytics are about learning , 2015 .

[39]  Niall Sclater,et al.  Code of practice for learning analytics , 2015 .

[40]  Deborah Richards,et al.  Validating the Effectiveness of the Moodle Engagement Analytics Plugin to Predict Student Academic Performance , 2015, AMCIS.

[41]  Mandy Lupton,et al.  Learning analytics beyond the LMS: the connected learning analytics toolkit , 2015, LAK.