How do you use theoretical frameworks in learning analytics?

1 Types of theoretical frameworks

Various theoretical frameworks can be used in learning analytics, depending on the purpose and perspective. Examples include learning theories, such as behaviorism, cognitivism, constructivism, and connectivism, which explain how people learn and help to understand learning processes and outcomes. Pedagogical theories, such as instructional design, inquiry-based learning, and problem-based learning, help to align data collection and analysis with learning objectives. Evaluation theories provide criteria and methods for assessing the quality and impact of learning analytics, while ethical theories address moral and legal issues like privacy, consent, ownership, and fairness. All of these frameworks help to ensure that data practices are respectful, transparent, and accountable.

2 How to choose a theoretical framework

When selecting a theoretical framework for your learning analytics project, there are several factors to consider. Your research or practice question should guide the selection of a framework that can help answer it. Additionally, the data sources and methods should match the framework in terms of validity, reliability, and relevance. Furthermore, consider your audience and context to ensure they understand and appreciate the framework and its implications. Lastly, consider your personal and professional values to ensure they are consistent with the framework and its ethical implications.

3 How to use a theoretical framework

Using a theoretical framework for your learning analytics project is not a one-time decision; it needs to be applied throughout the data cycle. Your framework should inform which data should be collected, how it should be collected, and how to store it securely and ethically. It should also help you select what data to analyze, how to analyze it, and how to interpret it meaningfully and critically. Additionally, your framework should guide you in choosing what data to visualize, how to visualize it, and how to communicate it effectively and engagingly. Finally, the framework should help you decide what data to act on, how to act on it, and how to evaluate it for improvement and impact.

4 Benefits of using a theoretical framework

Using a theoretical framework for your learning analytics project can bring many benefits, such as greater clarity in purpose and scope, more rigorous data collection and analysis, relevance to learning goals and strategies, the opportunity to explore new possibilities and insights from data, and ethical respect for data subjects and stakeholders. Such a framework can help you avoid confusion, bias, error, irrelevance, redundancy, stagnation, repetition, harm, and misuse.

5 Challenges of using a theoretical framework

Using a theoretical framework for your learning analytics project can also pose some challenges, such as complexity, diversity, change, and criticism. Your framework may be too abstract, narrow, or outdated to apply to your data and context, which may require adaptation and integration. It may also be too specific or rigid to represent the diversity and complexity of your learners and learning situations, requiring complementation and comparison. Moreover, it can be too controversial or contested to gain the acceptance and support of your audience and peers, requiring justification and debate. Lastly, your framework may not reflect the dynamic and evolving nature of your data and learning environment, necessitating revision and update.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?