Instructions for course participants

Topic 1: Learning Analytics – Definition, Characteristics, Purpose of Application

In the introductory topic, you will become familiar with the definition of learning analytics, key areas of application, and the types of learning analytics. The topic also covers the boundaries with related fields (academic analytics, educational data mining), the place of learning analytics in the cycle of continuous improvement of learning and teaching processes, challenges in implementation, and the connection with learning design. You will examine specific examples of learning analytics and explore its application at your own institution.

Mandatory activities (requirement for the badge): In the first unit, it is essential to watch the video on learning analytics and participate in the quiz, and it is highly recommended to study the reading material as well. In the second unit, it is mandatory to conduct a mini-study on the implementation of learning analytics at your institution and present the findings in a short essay, followed by peer assessment. It is recommended to first review the examples and listen to podcasts on learning analytics.

Topic 2: Types of Data and Data Sources

The second topic focuses on data sources and types of data, including the LMS (assessment data, log data), learning design tools (e.g., data on learning outcomes, and learning and teaching activities), other information systems (e.g., student attendance records), as well as multimodal data (e.g., collected via sensors). The topic also addresses data access and data quality. You will independently explore the data available at your institution, collect available datasets, and evaluate them.

Mandatory activities (requirement for the badge): In the first unit, it is mandatory to watch the video on data sources in learning analytics, then explore the data available at your institution, and prepare a written overview considering possible insights and ideas for application. In the second unit, it is mandatory to evaluate the datasets collected in the previous activities from available information systems.

Topic 3: Ethical Aspects and Personal Data Protection

The third topic focuses on the ethical aspects of learning analytics. It begins with an overview of the broader concept of trustworthiness in learning analytics, followed by an emphasis on ethical aspects, including privacy and data protection, autonomy and control, and data and algorithmic bias.

Mandatory activities (requirement for the badge): In the first unit, it is essential to watch the video on the trustworthiness of learning analytics, and it is also desirable to study the reading material. After that, it is necessary to complete a questionnaire related to participants’ views on the prerequisites for implementing educational interventions based on trustworthy learning analytics. In the second unit, a mandatory essay is required in which participants present their vision of the key challenges related to ethics and connections with other aspects of the trustworthiness of learning analytics, supported by previous activities such as reading ethical guidelines for the ethical use of data in learning analytics.

Topic 4: Data Collection, Analysis, and Reporting of Data Analysis Results

The fourth topic is dedicated to methods of data collection and data analysis in learning analytics, followed by reporting and visualisation through dashboards. It includes teamwork within inquiry-based learning and the development of dashboard examples.

Mandatory activities (requirement for the badge): In the first unit, as part of inquiry-based team learning, it is mandatory to identify problems and formulate a research question related to your own educational context that can be addressed with the help of learning analytics, then conduct research on it and report the results. In the second unit, it is mandatory to watch the video on reporting methods in learning analytics and then submit and peer-assess the dashboard concepts created in teams.

Topic 5: Methods of Interpreting and Using Analysed Data

The fifth topic focuses on the interpretation of learning analytics results. It includes the study of guidelines for interpreting learning analytics as well as problem-based tasks focused on interpreting concrete examples of analyses. The topic also addresses the role of teachers in interpreting and applying learning analytics results.

Mandatory activities (requirement for the badge): In the first unit, it is mandatory to watch the video on the interpretation of learning analytics and then solve a problem-based task in which examples of concrete analyses from Moodle and the BDP learning design tool must be interpreted. In addition to the video, the basis for this task are the learning analytics interpretation guidelines from the previous activity. In the second unit, it is mandatory to participate in a discussion on the role of teachers in interpreting learning analytics and implementing educational interventions.

Topic 6: Application of Learning Analytics for the Improvement of Learning and Teaching – Pedagogical Interventions

The sixth topic focuses on educational interventions based on insights from learning analytics, including the creation of an intervention plan to improve the learning design of a selected course based on analytics insights. The topic also includes reflection on the possibilities of learning analytics in one’s own teaching practice.

Mandatory activities (requirement for the badge): In the first unit, it is essential to create an intervention plan for the learning design based on the learning design analytics of the selected course. In the second unit, it is mandatory to watch the video on interventions based on learning analytics and participate in a discussion on the potential for improving one’s teaching process through the use of learning analytics and the feasibility of implementation at one’s own higher education institution based on insights from previous activities.