Module COMP4157: LEARNING ANALYTICS

Department: Computer Science

COMP4157: LEARNING ANALYTICS

Prerequisites

• COMP2261 Artificial Intelligence AND COMP2271 Data Science

Corequisites

• None

Excluded Combination of Modules

• None

Aims

• To give students a fundamental understanding of some of the core approaches and problem-solving principles for Learning Analytics (LA) and the role of LA in current and future learning settings and environments.

Content

• Statistical Learning Analytics and visualisation: data pre-processing; methods for tackling learning analytics based on statistical approaches; the types of LA that can be done with such approaches - e.g. descriptive and beyond. Visualisation of LA data for different stakeholders - e.g. learner, teacher, administrator, etc.
• Ethics of Learner Data Usage: discussions on ethical considerations of using learner data, starting from societal view, laws involved, (common) practice, future practice. Algorithmic perspectives, such as (expanded) sensitivity analysis.
• Machine Learning based Learning Analytics: shallow and deep Machine Learning methods for LA; numerical versus textual data analytical methods for LA; combined methods; sentiment analysis for LA; the types of LA that can be done with such approaches - e.g. descriptive, diagnostic, predictive, prescriptive.

Learning Outcomes

• The key principles and methodologies of data pre-processing for learning analytics, as well as the ability to evaluate and interpret where and how to apply such methods, including in the absence of complete data.
• The key principles and methodologies for statistical learning analytics and visualisation, as well as the ability to evaluate and interpret where and how to apply such methods, including in the absence of complete data.
• The key principles of machine learning based learning analytics, as well as the ability to evaluate and interpret where and how to apply such methods, including in the absence of complete data.

• An ability to manage data and to select and apply appropriate algorithms for LA.
• An ability to implement LA solutions.
• An ability to discuss implications of solutions in real-world applications.

• An ability to undertake reasoning in relation to LA problem-solving and LA applications.
• An ability to communicate technical information related to LA.
• An ability to appreciate societal impact of LA solutions.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• Lectures enable students to learn core material and discuss it in the classroom or via smaller groups.
• Formative and summative assignments encourage and guide independent study.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

Example formative exercises given during the course.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University