Current Graduate Students

PhD

Current
Student

Liaqat Ali

Thesis Topic: Leveraging MSLQ Data for Predicting Student Achievement Goal Orientations

Collaborative online learning environments such as MOOCs have created new roles and responsibilities for both teacher and learner where the onus of learning is shifting towards learners, and the teacher acts more like a facilitator. These shifted roles have created new motivational, cognitive, and performance challenges for learners. My research investigates the twin discourses of learning analytics, and achievement motivation in the context of online learning systems. My inquiry focuses on: (a) analyzing students’ activity data and building predictive models for identifying behavioral patterns, and (b) motivating a change of behaviour in students for adopting effective learning activities. In my current research project, I am examining how learning motivation and achievement goals relate to a student’s study activities. The ability to predict students’ goal orientations from their online activities will support user-level course adaptations and early interventions to support learning. Previously, we have proposed an empirically validated model of factors influencing educators’ beliefs for adopting an online learning environment. In particular, the model explains how the usage beliefs about the learning analytics of a tool are associated with the intention to adopt the tool.

PhD

Current

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.

PhD

Current
Student

Fatemeh Salehian Kia

Area: Learning Analytics

I am starting my PhD studies with my knowledge in computer science that I have refined over my two years of Master’s studies at RTWH Aachen. My focus is in the broader field of Educational Technology in which I have developed an interest in the area of Learning Analytics and Educational Data Mining. My master thesis concerned the design and implementation of a data collection and analysis tool for a Learning Management System (LMS). The application tool was developed to enable collection and analysis of user’s behavioral information and its context. The visual indicators were incorporated into the learning dashboard to provide feedback over different time scales in order to encourage learners to reflect on their respective performance. Recently, I my work has focused on visual aspects of learning analytics. In particular, I study how to extract meaningful data from log dataset of a virtual learning platform to inform effective visualizations. My multiple study and research experiences has molded me into the scholar I am in the process of becoming. Currently I am in the process of defining my thesis research.