ED5805 equips instructional designers and educators to use assessment data and learning analytics to design differentiated, personalized learning experiences. The course moves from data collection and analysis to design decisions — connecting what assessment reveals about individual learners to concrete changes in instructional design and delivery.
Assessment types and their instructional design roles
| Assessment Type | Purpose | Instructional Design Use |
|---|---|---|
| Diagnostic/Pre-assessment | Identify prior knowledge, skills, misconceptions | Informs initial design; identifies where differentiation is needed |
| Formative assessment | Monitor progress during instruction to adjust teaching | Triggers adaptive branching in e-learning; signals need for re-teaching |
| Summative assessment | Measure achievement at end of unit or course | Validates whether instructional design achieved outcomes |
| Learning analytics | Measure engagement, time-on-task, click-path data in digital environments | Reveals how learners interact with designed materials; informs iteration |
What ED5805 covers
Learning analytics refers to the measurement, collection, analysis, and reporting of data about learners and their learning contexts in order to understand and optimize learning and the environments in which it occurs. In digital learning environments, analytics can capture data that traditional classroom assessment cannot: time spent on each module, sequences of navigation, patterns of re-reading or re-watching, quiz attempts and error patterns, discussion post frequency and timing, and more. ED5805 examines how instructional designers can build analytics-aware courses and use the resulting data to iterate on their designs — not just evaluating whether learners achieved outcomes, but diagnosing which design elements supported or impeded learning and why, to inform both ongoing adaptive instruction and future course redesign.
Differentiated instructional design applies Carol Ann Tomlinson's differentiation framework to the design of courses and learning environments — not just classroom instruction. In e-learning, differentiation can be built into the design itself: adaptive branching that routes learners to different content based on pre-assessment performance, multiple pathways through content based on preferred learning modality, varied practice activities at different challenge levels, and flexible pacing built into the course architecture. ED5805 examines how Universal Design for Learning (UDL) principles interact with differentiated design: UDL proactively designs for the full range of learner variability (multiple means of representation, action and expression, and engagement), while differentiation adds responsive variation based on specific learner data.
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Key topics you write about in ED5805
- Learning analytics: types, collection methods, ethical considerations, use in design iteration
- Formative assessment in digital environments: embedded checks, adaptive quizzes, progress dashboards
- Differentiated instructional design: content, process, product, and environment differentiation
- UDL as a design framework: multiple means of representation, expression, and engagement
- Personalized learning: adaptive systems, learner profiles, individualized pathways
- Data ethics: learner privacy, data ownership, algorithmic bias in adaptive systems
- Formative feedback design: frequency, specificity, timing, and actionability
Analytics-informed instructional design cycle
- Design: build in data collection points (quizzes, engagement tracking, navigation logs)
- Deliver: learners interact with the course; analytics data accumulates
- Analyze: identify patterns — where do learners disengage? Which items reveal persistent misconceptions?
- Diagnose: determine whether problems are design problems (unclear explanation, insufficient practice) or learner problems (missing prerequisite knowledge)
- Iterate: revise the instructional design based on evidence; re-deploy; repeat
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Frequently asked questions
Traditional educational data — grades, test scores, attendance records — is collected at the end of learning episodes and tells you that a learner succeeded or failed. Learning analytics collects data during learning, in real time, from digital systems: how long a learner spent on each page, how many times they replayed a video segment, which quiz items they got wrong before getting right, how their engagement changed across a course. The difference is diagnostic resolution: traditional data says "this learner earned 67% on the midterm"; analytics says "this learner spent 4 minutes on module 3 (average is 18), attempted quiz 3 four times with persistent errors on items about X, and did not access the additional resources for that module." That behavioral data points to specific design decisions to investigate and revise.
UDL is a proactive design framework: it designs learning environments from the start to be accessible and flexible for the full range of learner variability, without requiring individual accommodation after the fact. UDL reduces the need for retrofitted accommodations by building in multiple means of representation (presenting content through text, audio, video, graphics), action and expression (allowing learners to demonstrate knowledge through varied products), and engagement (offering choice and relevance). Differentiated instruction is responsive: it adjusts instruction in response to specific data about specific learners, varying content, process, product, or environment based on readiness, interests, and learning profiles. In instructional design, the two approaches are complementary: UDL provides the universal foundation; differentiated design adds responsive variation based on learner data within that flexible foundation.
Key ethical concerns include learner privacy: analytics systems collect detailed behavioral data that learners may not fully realize is being captured, raising questions about informed consent and data transparency. Data ownership: who owns learner data, who can access it, and how long is it retained? Algorithmic bias: adaptive systems trained on historical data may perpetuate existing inequities if the training data itself reflects biased outcomes. Surveillance and control: detailed behavioral tracking can shift the relationship between learner and institution toward surveillance rather than support. Purpose limitation: data collected for instructional improvement should not be repurposed for other uses (employment screening, marketing) without explicit consent. ED5805 treats data ethics as integral to analytics practice, not a separate concern.
Personalized learning is an instructional approach that tailors content, pacing, and learning pathways to individual learners based on their performance, preferences, and goals. In digital environments, personalization can be delivered through adaptive learning systems that adjust which content is presented and at what difficulty level based on learner responses. The research on personalized learning is mixed: some adaptive systems show positive effects on achievement, particularly for skills-based learning where mastery sequencing is clear (mathematics, reading fluency). Effects for complex, judgment-dependent learning are less established. Effective personalization requires high-quality underlying content, valid assessment data to drive adaptation decisions, and transparency to learners about how the system is adapting to them. The risk of narrowly personalized systems is reducing learner agency and limiting exposure to challenging material.