ITO5007 · Introduction to data science for learning analytics

Overview

This unit will focus on the key data science approaches as commonly used in learning analytics. Students will work on relevant datasets and develop skills in identifying suitable data indicators for specific contexts. This unit provides foundations for feature engineering to extract relevant indicators from raw data about learning. Relevant data analytics tools and techniques will also be introduced. This will provide students with experience in working on common tasks in learning analytics using data science approaches that are situated in established frameworks used for designing and evaluating learning environments.

Offerings

The Handbook publishes no offerings for this unit.

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

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

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

Learning outcomes

1. Analyse the role of data in learning analytics;
2. Identify and apply basic tools for performing exploratory data analysis, visualisation, and predictive modelling in learning analytics;
3. Discuss and evaluate the legal and ethical issues due to the use of data science in learning analytics;
4. Develop and critically assess a detailed plan for data science implementation in learning analytics.

Workload

Minimum total expected workload equals 12 hours per week

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