LAK 19

Selected papers

There were 9 accepted articles for discussion in this workshop. Authors of these articles were from 6 different countries. 

Two of the articles present analysis of technology implementation focusing on teachers. In Learning Analytics Dashboard Widgets to Author Teaching-Learning Cases for Evidence-based Education, Majumdar et.al. (2019) components of LAViEW, a learning dashboard to assist authoring of teaching-learning cases (TLC) by practitioners is described. The TLCs would enable to capture problems identified in a specific context, its indicators in terms of dashboard visualizations, solution and results. Authors propose it as the unit of analysis for evidence-based teaching and learning. In Behind the Scenes: Designing a Learning Analytics Platform for Higher Education, Chounta et.al. (2019) reports findings from stakeholder studies during development phase of a LA platform. Their LA platform is targeted for higher education academic institutions in Estonia and this study focus on the teachers’ perspective. 

Three articles propose models related to learner’s artifact evaluation or log analysis to extract evidence. In Quantitative Evaluation of Concept Maps: An Evidence-Based Approach, Kadam et.al. (2019) propose automated evaluation algorithm of student submitted concept map assignment. In Modelling students’ effort using behavioral data, Moissa et.al. (2019) use interaction and eye gaze data to model student’s effort. In LASAT: Learning Activity Sequence Analysis Tool, Mishra et.al. (2019) present a case-study of utility of various sequence analysis algorithms which assist in extracting and interpreting students’ learning behaviors extracted as frequent patterns (sequence of activities) from their activity traces logged in computer-based learning environments. These algorithms, developed in Institute for Software Integrated Systems, Vanderbilt University, are packaged in a toolkit with the aim to make it accessible to wider community of researchers and practitioners. 

Three articles focus on the context of MOOCs. In Automated MOOC/SPOC Learning Design Verification based on Instructional Design Theories, Lei et.al. (2019) propose a mechanism that can quickly visualize the courseware with faulty or at-risk designs that may cause obstacles for learners, which allows just-in-time revisions. In Using Log Data to Evaluate MOOC Engagement and Inform Instructional Design, Chai et.al. (2019) discusses a framework of MOOC engagement composed of learning-interface, learner-content and learner-community interactions. They illustrate how to utilize the framework with log data from 10 MOOC courses offered by Hong Kong University. In CLEAR: Cohort-Level Evidence Analysis and Reflection Process as a methodology to assist MOOC Providers and Adopters for effective teaching-learning using MOOCs, Warriem and Balaji (2019) discuss a case study of National Programme on Technology Enhanced Learning (NPTEL), a national MOOC initiative from India. They focus on the issue of persistent engagement of learners in MOOCs and propose a process flow that will assist the MOOC providers as well as institutions signed up with NPTEL to utilize the evidences available from previous offerings of courses and take meaningful actions on it.

Finally, in Extracting Self-Direction Strategies and Representing Practices in GOAL System, Li et.al (2019) provides an instance of building a framework for tracking self-directed actions of learners and illustrates how to utilize it for extracting evidence of best practices and self-reflection. The work is in the context of the GOAL system, where learners use their automatically collected self-data regarding learning and physical activities, to foster various self-direction skills. 

Reference:

Chai Y., Lei C., Kwok Y.(2019) Using Log Data to Evaluate MOOC Engagement and Inform Instructional Design. 

Chounta I., Pedaste M., Saks K. (2019) Behind the Scenes: Designing a Learning Analytics Platform for Higher  Education. 

Kadam K., Deep A., Prasad P., Mishra S.(2019) Quantitative Evaluation of Concept Maps An Evidence-Based Approach. 

Lei C., Hou X., Wang J., Guo Y.(2019) Automated MOOC/SPOC Learning Design Verification  based on Instructional Design Theories. 

Li H., Majumdar R.,  Yang Y.Y.,  Flanagan B., Ogata H.(2019) Extracting Self-Direction Strategies and Representing Practices in GOAL System. 

Majumdar R., Akçapınar A., Akçapınar G.,  Flanagan B. and Ogata H. (2019) Learning Analytics Dashboard Widgets to Author Teaching-Learning Cases for Evidence-based Education. 

Mishra S., Munshi A., Rushdy M., Biswas G.(2019) LASAT: Learning Activity Sequence Analysis Tool. 

Moissa B., Bonnin G., Castagnos S. and Boyer A. (2019) Modelling students’ effort using behavioral data.  

Warriem J.M., Balaji B.(2019) CLEAR: Cohort-Level Evidence Analysis and Reflection Process as a methodology to assist MOOC Providers and Adopters for effective teaching-learning using MOOCs.