• Learning Analytics

Halimat Alabi

Area: Visual Aspects of Learning Analytics Tools

Description: Despite the many positive correlations between self-regulated learning (SRL) with academic success, limited research examines how online learners metacognitively monitor and reflect upon their learning. Learning analytics tools may be used to aid in the expansion of learner’s understanding of persistence and engagement through monitoring and evaluation, providing learners formative feedback on their self-regulatory processes as they transpire. My research explores how learning analytics employing visualization techniques may positively impact online learners. The strategic use of visualizations within these tools could provides actionable intelligence to learners on a “just in time” basis, garnering attentiveness, memorability, and the pattern recognition integral to insight generation. Pedagogically motivated visual learning analytics could increase the likelihood of academic success for a rapidly growing population of adult learners. Longitudinal study of these constructs allow for deeper exploration of the theoretical models of self-regulated learning, goal orientation, motivation, and the impact of aesthetics on the adoption and sustained use of learning analytics tools. This research contributes to the design and evaluation of pedagogically motivated learning analytics tools employing visualizations, human computer interaction, and post-secondary education.

• Learning Analytics

Fatemeh Salehian Kia

Area: Learning Analytics

I am a PhD student in the School of Interactive Arts and Technology at Simon Fraser University and a research affiliate in the School of Information at the University of Michigan, Ann Arbor. My research focuses on learning analytics and educational data mining using learners’ trace data records collected by learning management systems to gain a better understanding of learners' behaviors. I am interested particularly in learners’ self-regulatory behaviors in online learning and how learners’ trace data can help to build up more accurate picture of their learning behaviors. I was also a visiting scholar at the Center for Academic Innovation, University of Michigan, and a student researcher at the University of California, Berkeley. I obtained my MSc in Software Systems Engineering from RWTH Aachen University, Germany in 2014.