Design a Short Courselet on Motivation Psychology: Curriculum, Activities and Assessment

Hook: Build motivation skills fast — without the fluff

Students and instructors tell us the same thing: they want a compact, evidence-based curriculum that teaches how motivation works, gives hands-on habit formation practice, and ends with a real-world, data-driven project — not a set of disconnected readings. This courselet does exactly that. It's designed for undergraduates who need a short, rigorous, and practical introduction to motivation psychology they can use in coursework, wellness programs, or community projects.

Quick overview: What this courselet delivers

• Duration: 4 weeks (4 × 90–120 minute seminars) + 2-week pilot project
• Credit: Module/mini-credential or extra credit for larger courses
• Focus: Theories of willpower, habit formation exercises, and an evidence-based final project
• Format: Mix of micro-lectures, active labs, peer review, and a small-scale behavior change pilot
• Assessment: Formative quizzes, a habit diary, and a graded final project with a reproducibility checklist

Why run this courselet now? 2026 trends that matter

By 2026, several developments make a short, applied course on motivation especially timely:

• AI-supported coaching: Personalization via generative AI coaches and recommender systems has matured; students can prototype AI-assisted habit nudges while learning ethical limits.
• Micro-credentials and modular learning: Universities and platforms now accept stackable courselets as demonstrable skills for internships and teaching portfolios — think of distribution and reuse similar to modern cross-platform content workflows.
• Open science & reproducibility: Late-2025 pushes for preregistration and transparent reporting mean student projects should emphasize reproducible methods and attention to data sovereignty when working with multi-jurisdictional datasets.
• Wearables & passive data: Increased availability of wearable and smartphone sensor data enables feasible, low-burden measurement in short pilots — when paired with strong privacy safeguards and a clear data-minimization plan.

Learning outcomes

• Explain contemporary theories of willpower and motivation and how they differ in predicting behaviour change.
• Design and run short habit formation exercises using implementation intentions and context cues.
• Use behaviour change techniques (BCTs) to build an intervention, collect basic data, and report effect sizes.
• Critically evaluate the ethical trade-offs of digital nudges, AI coaching, and data privacy.

Compact syllabus: 4 weeks + project

Week 1 — Theories of willpower (90–120 minutes)

1. Micro-lecture (20 mins): Dual-process models, self-determination theory (autonomy, competence, relatedness), and the evolution of the ego depletion debate.
2. Activity (30 mins): Rapid case studies — students read two short vignettes and identify which theory best predicts behaviour.
3. Lab (30 mins): Self-control framing task. Students reframe a common academic goal (e.g., study schedule) using autonomy-supportive language and compare predicted motivation levels.
4. Homework: Short reflection + 5-question formative quiz.

Week 2 — Habit formation mechanics (90–120 minutes)

1. Micro-lecture (25 mins): Habit loop (cue → routine → reward), context-dependency, Fogg's Tiny Habits, and implementation intentions.
2. Activity (20 mins): Create a 2-week Tiny Habit plan (start tiny, anchor to an existing routine, celebrate immediately).
3. Lab (40 mins): Habit Diary practice — commit to a small habit, log context cues and automaticity using the Self-Report Habit Index (SRHI) items adapted for short use.
4. Homework: Begin 2-week habit diary; weekly check-in via LMS.

Week 3 — Behaviour change techniques & digital tools

1. Micro-lecture (25 mins): Introduction to the Behaviour Change Technique Taxonomy (Michie et al.) and nudging vs. persuasion.
2. Activity (30 mins): Group design sprint — pick a target behaviour (sleep consistency, study session start, exercise) and storyboard a 2-week intervention using 3–4 BCTs.
3. Demo (20 mins): Low-code prototyping with forms, push-notifications, or simple chatbots; an overview of privacy considerations with wearables and LLMs.
4. Homework: Finalize intervention design for the evidence-based final project.

Week 4 — Project prep, ethics & evaluation

1. Micro-lecture (20 mins): Measurement choices (self-report vs passive), sample size and power for small pilots, pre-registration essentials.
2. Activity (40 mins): Peer review of project plans — each student gives 10 minutes of feedback using a rubric (theory match, feasibility, measurement validity, ethics).
3. Wrap-up (20 mins): Setup for the 2-week pilot; create consent form template and data-handling plan.

Evidence-based final project (2-week pilot)

Students implement a short intervention and submit a reproducible report. Options:

• Within-subject pilot: n = 1–10 (repeated daily measures) testing a tiny-habit + reward strategy.
• Between-subject micro-RCT: class-level randomization to brief nudge vs control (feasible with 30+ participants across sections).
• Technology prototype: a low-code chatbot or rule-based notification system tested with beta users and qualitative feedback.

Required deliverables:

1. Pre-registered project plan (one page): hypotheses, BCTs used, outcome measures, and analysis plan.
2. Two-week data file and code/notebook (R, Python, or Google Sheets) that reproduces summary statistics and pre-specified analyses. Consider using a data-sovereignty checklist if participants span institutions or countries.
3. 4–6 minute video or poster summarizing method, results (effect sizes + CI), and practical recommendations.
4. Short reflection on ethics, limitations, and reproducibility steps taken.

Assessment rubric (sample)

Use this weighted checklist for grading the final project (100 points):

• Theoretical grounding — clear link between chosen theories and intervention (20 pts)
• Design & feasibility — realistic BCTs, clear procedure, consent/data plan (20 pts)
• Measurement & analysis — valid measures, pre-registered analysis, effect size reporting (25 pts)
• Reproducibility & transparency — data/code shared (or simulated if confidential), preregistration (15 pts)
• Reflection & ethics — privacy considerations, limits, next steps (10 pts)
• Communication — clarity of video/poster, concision, and actionable recommendations (10 pts)

In-class activities: Templates and quick wins

Habit Diary template (daily: 2 weeks)

• Date
• Time & context cue (What triggered the habit?)
• Action taken (1–2 sentences)
• Automaticity rating (1–7 scale: felt automatic?)
• Reward/celebration (Did you celebrate?)
• Notes (barriers/facilitators)

Implementation Intention template

Structure: If [trigger], then I will [behaviour] for [duration].

Example: If I finish lunch, then I will walk for 7 minutes outside the building.

Rapid peer-review checklist (5 min)

• Is the behaviour specific and measurable?
• Is the intervention small and actionable?
• Are outcome measures valid and realistic for 2 weeks?
• Is participant burden low and consent clear?
• Could this be implemented without instructor support?

Measurement tips for compact pilots

• Prefer daily or twice-daily micro-surveys for rapid time-series analysis — consider best practices from guides on running paid or recruited surveys.
• Use validated short scales where possible (adapt SRHI items for automaticity). Cite sources for measure validity in project write-ups.
• When using wearables or passive sensing, include an explicit data-minimization plan and anonymization steps.
• Report effect sizes and confidence intervals — p-values alone are insufficient for pilots.

Tools & resources (practical picks for 2026)

• Low-code prototyping: Google Forms + Apps Script, Glide Apps, or MIT App Inventor for small interventions — pair these with simple deployment workflows described in hybrid micro-studio playbooks for small teams.
• Habit trackers: Streaks/Loop alternatives and open-source trackers that export CSV (prioritize exportability and privacy).
• AI assistance: Use LLMs for brainstorming intervention wording and drafting consent language — see practical notes on using guided LLM workflows — but require human review for ethics and clarity.
• Reproducibility: GitHub Classroom, Google Colab, or OSF for preregistration and sharing materials; complement these with governance for prompt and model versioning when projects use LLMs.

Ethics, privacy & responsible AI

Students must plan for:

• Informed consent with explicit data usage statements and opt-out options.
• Minimization of sensitive data collection (avoid recording content of messages; collect timing or counts when possible).
• When testing AI-driven nudges, disclose LLM use and avoid automated mental health advice — consider lessons from AI tools in clinical settings such as AI medication assistants when deciding whether automated guidance is appropriate.
• Data retention and deletion policy aligned to institutional requirements — large projects may need infrastructure guidance such as hybrid sovereign cloud patterns.

Examples from real runs (experience-driven tips)

Case 1 — Tiny Habit for Study Start: A student cohort used an anchor ('after turning on my laptop') and a micro-routine ('open notes for 5 minutes'). Over 2 weeks, automaticity scores rose from a mean of 2.1 to 4.3 on a 7-point scale. Key success: precise cue and immediate micro-reward.

Case 2 — Digital Nudge for Sleep Timing: Groups using a rule-based notification at a fixed bedtime reduced variability in sleep onset by ~30 minutes on average. Important lesson: notifications need user control — opt-out and timing adjustments improved adherence.

Advanced strategies & future predictions

• Personalized BCT stacking: By late 2025, early studies showed promise for algorithmic matching of behaviour change techniques to user profiles (motivation type, context). Courselets should introduce simple personalization templates and reference algorithmic matching work such as edge vs cloud inference considerations when deciding whether personalization runs on-device or in the cloud.
• Sensor-informed interventions: Wearable triggers (e.g., activity dips) can power just-in-time prompts, but data governance and cost trade-offs are central.
• Micro-RCTs become standard: With platforms for rapid randomization and analysis, expect more courselets to run micro-RCTs even in short timelines — pair experimental designs with careful consent and recruitment guidance such as in survey recruitment best practices.

“Teach students to build small, testable interventions. Replicable pilots beat perfect-but-theoretical projects every time.”

Instructor checklist: Ready-to-run in one week

• Set up LMS module with readings and short videos for each week.
• Create a one-page consent template and data-handling plan.
• Prepare habit diary Google Sheet and a short formative quiz bank.
• Draft the assessment rubric and peer-review form.
• Reserve 2 lab sessions for pilot troubleshooting and data upload.

Sample assignments (copy-paste-ready)

Assignment 1 — Tiny Habit Plan (Due Week 2)

1. Describe your tiny habit using the implementation intention template.
2. List the BCTs you will use (max 3) and why they match theory.
3. Submit a filled Habit Diary for at least 4 days.

Assignment 2 — Final Pilot Submission (Due end of course)

1. Pre-registered plan (one page).
2. Data and analysis notebook.
3. 4–6 min presentation + 300-word reflection.

Actionable takeaways — what to implement this week

• Pick one tiny habit and anchor it to an established routine today (use the implementation intention template).
• Run a 3-day habit diary to learn what context cues actually trigger the behaviour.
• If you're an instructor, prep the consent template and one reproducibility checklist before launching a pilot.

Call to action

Ready to convert this outline into a courselet for your class? Download the full editable syllabus, consent template, rubric, and habit diary CSV from our resources page (or adapt the templates in your LMS). If you’d like a tailored version for your department — for example, a psychology lab, health promotion course, or teaching practicum — contact us for a one-hour curriculum consult and starter package.

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