ITO5010 · Advanced data science for learning analytics

Overview

In this unit, participants will learn data analytics techniques that can be used for exploration and understanding of learners, learning processes, learning outcomes, and learning environments. A set of unsupervised and supervised machine learning techniques along with process mining techniques will be introduced. Relevant toolkits for practical implementation will also be used. Data analytics methods will be used on data collected from different data sources such as learning management systems, social media, and student records. Students will learn different approaches to link and analyse multimodal data. They will also learn how to interpret and critically assess the findings of unsupervised data analytics methods with respect to relevant theoretical frameworks about learning, teaching, and education. Moreover, students will explore ways to translate the results of unsupervised data analytics to inform decision making of different stakeholder groups.

Offerings

The Handbook publishes no offerings for this unit.

Assessment

The Handbook publishes no assessment items for this unit yet.

In-semester assessment: 100%

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Workload

Minimum total expected workload equals 12 hours per week

Student experience, not official rules. Nothing here changes what the Handbook says.