Educational Design Toolbox

Cognitive Load Theory (Sweller, 1988) focuses on the limitations of working memory — the mental space where we actively process information. This memory system can only handle a few pieces of information at once, making it easy for learners to become overloaded, especially when confronted with complex or poorly structured material.

According to the theory, there are three types of cognitive load:

1.  intrinsic load, which relates to the inherent difficulty of the content;
2. extraneous load, which is caused by confusing or unnecessary information;
3.  and germane load

Effective instructional design aims to minimize extraneous load and support germane load, while managing the complexity of the content itself. This means breaking material into manageable chunks, avoiding unnecessary distractions, and guiding students’ attention to what truly matters.

Mayer’s SOI Model (1996) describes the mental process learners go through when engaging with new information. The model breaks learning down into three steps:

• selecting relevant information from what is presented,
• organizing that information into coherent mental structures,
• and integrating it with prior knowledge.

 If any of these steps are disrupted — for example, if learners don’t know what to focus on, or can’t relate new ideas to what they already understand — learning will suffer.

The Multimedia Learning Theory, developed by Richard Mayer (2001) is based on the cognitive load theory and the SOI-model, the theory focuses specifically on how people learn from combinations of text, visuals, and audio. Research shows that people learn better from well-integrated multimedia presentations than from text alone — but only when those elements are designed in line with how the brain processes information.  

For instance, presenting spoken words alongside relevant images leads to better learning than simply showing written text, because it distributes cognitive processing across both visual and auditory channels. Likewise, eliminating irrelevant content, placing related images and text close together, and allowing learners to control the pacing of information all help reduce unnecessary cognitive strain and improve comprehension.

 [AA1]I would mention this first so that the reader knows that it is not a new theory per se, but rather related to the theories that have already been mentioned.  This helps with understanding and integrating the new information.

Constructive alignment is a principle of course design developed by John Biggs. It ensures that all aspects of teaching — learning outcomes, activities, and assessment — are aligned to support students in achieving meaningful learning.

• Constructive refers to the idea that learners actively construct meaning through relevant learning activities.
• Alignment means that the Intended Learning Outcomes (ILOs), Learning Activities, and Assessment Methods are all deliberately linked and support one another.

When these three elements are aligned, students clearly understand what is expected of them, are given the right opportunities to practice and explore, and are assessed in ways that reflect the learning goals.

Intended Learning Outcomes (ILOs) are clear statements that describe what students should be able to know, understand, or do by the end of a learning activity, course, or program. They form the foundation of constructive alignment and guide the choice of teaching methods and assessment. Good ILOs are specific, measurable, and aligned with the level of learning expected.

Bloom’s Taxonomy is a framework used to categorize cognitive learning objectives into levels of increasing complexity, from basic recall to advanced critical thinking. This helps educators formulate ILOs that match the desired depth of learning and the students' level.

The six levels of the revised taxonomy (Anderson & Krathwohl, 2001) are:

1. Remember – recall facts and basic concepts
2. Understand – explain ideas or concepts
3. Apply – use information in new situations
4. Analyze – draw connections among ideas
5. Evaluate – justify a decision or stand
6. Create – produce original work

By choosing the appropriate level and action verb (e.g., "describe" for understand, "design" for create), educators can write ILOs that are both challenging and achievable for their students.

In addition to levels of cognitive complexity, learning also involves different types of knowledge. Krathwohl’s revision of Bloom’s Taxonomy identifies four knowledge dimensions:

1. Factual Knowledge – basic elements students must know to be acquainted with a discipline (e.g., definitions, terminology).
2. Conceptual Knowledge – relationships among basic elements within a larger structure (e.g., theories, models, classifications).
3. Procedural Knowledge – how to do something; methods, techniques, and processes.
4. Metacognitive Knowledge – awareness of one’s own thinking and learning strategies.

These knowledge types can be combined with Bloom’s levels to design more balanced and comprehensive ILOs. 

Artificial Intelligence (AI) is rapidly transforming educational design — not just in what we teach, but in how we teach and design learning experiences. AI tools can assist educators in multiple ways:

• Personalizing learning by adapting content to different learners' needs.
• Generating ILOs, quizzes, or case studies aligned with your content.
• Analyzing student data to identify learning gaps or at-risk students.
• Automating feedback on assignments or formative assessments.
• Supporting creativity by helping students brainstorm, code, write, or simulate.

However, integrating AI into course design also raises important questions about academic integrity, transparency, and student data privacy. Educators should be intentional in choosing where and how AI is used, ensuring it enhances learning without replacing human judgment or interaction. As AI evolves, instructional design will increasingly involve designing not just content, but human-AI collaboration in learning.

In design education, students progress through distinct stages of expertise as they develop their skills. Drawing on the framework by Dorst and Reymen (2004), this progression reflects a shift from rule-based, analytical thinking to more intuitive, reflective, and situationally aware design behavior. In general, students develop through the following stages of expertise:

• Novice: Follows explicit rules, struggles to see beyond obvious problem features.
• Advanced Beginner: Begins to recognize patterns and context but still needs guidance.
• Competent: Takes responsibility, plans carefully, learns from experience.
• Proficient: Understands the design process deeply, thinks holistically, and iterates often.
• Expert: Moves smoothly between framing problems and creating solutions, guided by intuition.

Self-Determination Theory posits that motivation flourishes when three basic psychological needs are met:

• Autonomy: Feeling that one has choice and control over their learning.
• Competence: Feeling effective and capable in tasks.
• Relatedness: Feeling connected and supported by others.
  When instruction supports these needs, learners develop intrinsic motivation, meaning they engage because the learning is meaningful and satisfying in itself. This leads to deeper learning and persistence.

Expectancy-Value Theory (Eccles & Wigfield)

This theory explains motivation as the product of two beliefs:

• Expectancy: The belief that one can succeed at a task.
• Value: The perceived importance or usefulness of the task.
  Students who expect to succeed and find value in the learning task are more likely to invest effort and persist through challenges. Instruction that clearly communicates the relevance and achievable nature of tasks can boost motivation.

Attribution Theory (Weiner)

Attribution Theory explores how learners explain their successes and failures. When students attribute outcomes to internal, controllable factors like effort or strategy, they are more motivated to try again and improve. Conversely, attributing failure to fixed traits (e.g., lack of ability) or uncontrollable factors (e.g., luck) can lead to decreased motivation and avoidance. Instruction that encourages a growth mindset by framing effort and strategy as keys to success supports resilience

The Twente Educational Model (TOM) is the foundation of all bachelor-level programmes at UT and a strong inspiration for master's programmes. TOM emphasizes modularity, integration, project-based learning, and student ownership.

  Learn more about TOM and its design principles

Key principles of TOM:

• Modular structure: Education is delivered in blocks of 15 ECs, integrating various components (theory, skills, project work) into one cohesive module.
• Project-led learning: Each module includes a team-based project where students apply what they’ve learned to real-world challenges.
• Active learning: Students are expected to take ownership of their learning, supported by active engagement and formative feedback.
• Coherence and relevance: The model encourages horizontal and vertical alignment between modules, disciplines, and skills development.

Each UT faculty publishes its own Education and Examination Regulations (EER/OER) document. These contain the legal and administrative guidelines for course structure, assessment procedures, student rights, grading systems, and more.

Key elements typically covered:

• Learning objectives and course descriptions
• Assessment methods, grading scales, and deadlines
• Retake and compensation rules
• Academic integrity and fraud policies
• Requirements for passing modules or progressing in the programme

As a course designer or lecturer, it’s essential to consult your faculty’s EER/OER regularly — particularly when making changes to assessments, introducing new components, or handling exceptional cases.

Find the EER/OER of your Faculty here: 

A typical course or module at the University of Twente is measured in European Credits (ECs), where 1 EC equals 28 hours of student workload. It is important to ensure that the total workload (including lectures, self-study, group work, and assessment) is realistic and well-distributed.

Use this workload estimator to get an evidence-informed estimate of workload. A mismatch between expected and actual workload can impact motivation, performance, and course evaluations.

Each course may have a limited budget depending on the faculty and programme. Budgeting impacts what kinds of activities or materials you can include, such as guest lectures, field trips, or specialized software.

Tips:

• Clarify your budget early with your programme coordinator or educational support staff.
• Explore open-source or UT-licensed tools before considering external purchases.

Early coordination with scheduling services is essential. Your course should be planned in line with the academic calendar, available teaching spaces, and the students’ broader timetable.

Key considerations:

• Submit scheduling requests on time.
• Consider the type of space needed: interactive sessions may require flexible or informal learning spaces.
• Check technology availability (e.g., screens, microphones, hybrid teaching setups).