Assessing student learning competencies with e-learning platforms via data analytics

Principal Investigator: A/P Andy W. H. Khong, Assistant Chair (Outreach), School of Electrical and Electronic Engineering
Collaborator: Kevin Hartman, Research Scientist, Centre for Research and Development in Learning
PhD Students: Ng Hongrui Kelvin, Liu Kai, School of Electrical and Electronic Engineering
Funding Agency: CRADLE@NTU Start-up Grant
Expert mountain guides have a trained ability to identify characteristics of hikers by what they leave behind in the woods. Traces of footprints and disturbed underbrush that linger long after a hiker has passed through an area can tell a lot about a person. In NTU’s learning ecosystem, the growth in technology enabled learning (TEL) activities has increased the number of similar traces left behind by students.
Students access online resources, they take online assessments, and they have online discussions. By examining students’ online trails, A/P Andy Khong, his team, and the Centre for Research and Development in Learning (CRADLE@NTU) researchers are shedding light on how those traces are influenced by learning motivations, processes, and outcomes. His team systematically analyses learner-produced data to better characterise students’ competencies, content mastery, and future professional success through learning analytics.
While analyses in itself may be insightful, it is not until those insights can be made visible so others can take action upon them that they become useful. Often this translation occurs through well-developed visualisations and notifications. This is the second piece of the puzzle A/P Khong and his team are working on. Once the key predictors of learning proficiencies such as mastery motivation, self-efficacy, and grit are identified, how do students and instructors take action on those insights? By tying those insights to a more holistic vision of student development that includes creativity, civic-mindedness, communication, and character, A/P Khong and his team are widening the scope of how students can be indirectly assessed and what those assessment measures mean.