How a student and a faculty member each use Knowledge Explorer to shift from passive information consumption to active, self-regulated learning.
A college student facing a difficult topic uses myKE to build a personalized understanding on their own terms — choosing what to explore, at what depth, and in what order.
Sofia's Research Methods course has just covered Null Hypothesis Significance Testing (NHST). She passed the quiz by memorising the steps, but her professor's feedback keeps using words like "p-value interpretation," "Type I error trade-offs," and "statistical power" — and she can't connect them.
She has a major paper due in three weeks that requires her to choose and justify a statistical approach. Memorised steps won't get her there.
Sofia opens ke.skoonline.org and types: Domain → Psychology, Subdomain → Research Methods, Concept → Null Hypothesis Significance Testing.
Within seconds a map appears. She can immediately see that NHST sits at the centre, with prerequisite nodes like Probability Distributions and Sampling Theory, and enables nodes like Effect Size, Statistical Power, and Confidence Intervals.
She clicks the Sampling Theory prerequisite node. The details panel opens with a plain-language definition and three action buttons. She clicks "Why it matters" — the chat explains how NHST assumes random sampling and what breaks down without it.
She then clicks the requires edge between Statistical Power and Sample Size. myKE explains the relationship: increasing sample size increases power, and why ignoring this leads to underpowered studies that produce misleading results.
The node Effect Size keeps appearing in her professor's slides. She right-clicks it and selects Explore in New Tab. A new map generates centred on Effect Size, revealing its relationship to Cohen's d, power analysis, and practical vs. statistical significance.
She now has two tabs open — her original NHST map and the Effect Size deep-dive — and navigates between them as she reads her textbook.
Two days before her paper deadline, Sofia uses the Q2L (Question to Learn) mode on the NHST map. myKE generates a scenario: "A clinical trial reports p = 0.048 with a sample of n = 40 and Cohen's d = 0.18. The authors conclude their intervention is effective. A reviewer disputes this. Why might the reviewer be sceptical?"
Sofia selects Statistical Power and Effect Size as her focus concepts, then submits her question: "What does it mean when a result is statistically significant but the effect size is small?"
myKE evaluates it as deep and enters discussion mode — asking her follow-up questions rather than giving the answer directly. By the end of the exchange she has articulated, in her own words, the difference between statistical and practical significance.
The next day Sofia activates Wonderment on the Statistical Power node. Three AI agents simultaneously surface what she hadn't considered: the Empiricist shows a paradox about underpowered studies that correctly detected real effects by accident; the Connector links statistical power to signal-to-noise ratios in engineering; the Provocateur inverts the question — "What would research look like if we designed for maximum power rather than minimum sample size?"
She selects the paradox observation, picks a "reframe" genius question, and composes an answer. When the critics panel arrives, she reads it twice — it challenges her answer directly. The synthesis reframes her entire understanding of why sample size is a research design decision, not a statistical afterthought.
The morning before submission, Sofia tries When Things Break Down on her NHST map. myKE generates a scenario: "A psychology researcher ran 40 subjects, got p = 0.049, published the result. Two years later, three labs failed to replicate it. Why did the original study likely fail?"
Sofia expands three evidence clues (sample demographics, effect size, study design) and asks two investigation questions about what conditions produce false positives. She submits her diagnosis: "The study was underpowered — p < 0.05 was achieved at an n where the true effect size would require n=120. The result was a false positive from sampling noise." The AI scores it as correct with deep reasoning — she identified the mechanism, not just the symptom.
Before submitting her paper, Sofia opens "How Am I Doing?". The dashboard shows her interaction pattern across three sessions: she spent most of her time on nodes in the Apply and Analyze Bloom levels, asked two deep Q2L questions, and her AI Collaboration score is high on Iteration Depth — she followed up multiple times rather than accepting the first response.
It also flags that she never engaged with the Confidence Intervals node despite it appearing in her map. She spends fifteen minutes there before closing the laptop.
A faculty member uses myKE's teacher tools to shift pre-class preparation from passive reading to active concept exploration — and uses the resulting analytics to teach more responsively.
Prof. Osei runs a flipped classroom for Introduction to Cognitive Science. Students are supposed to engage with material before class so sessions can focus on application and discussion. In practice, most arrive having skimmed — they can name concepts but cannot connect them.
Week 4 topic: Working Memory and Cognitive Load. Last year, 60% of students failed the follow-up quiz on this topic. He wants a different approach this time.
Prof. Osei logs in as Teacher and opens the Curriculum Builder. He seeds it with three core concepts: Working Memory, Cognitive Load Theory, and Dual Coding. He clicks AI Expand.
myKE generates a full concept structure: it adds Baddeley's Model, Phonological Loop, Visuospatial Sketchpad, Central Executive, Intrinsic/Extraneous/Germane Load, and their relationships. Prof. Osei reviews the map, removes two nodes he considers out of scope for Week 4, and rearranges the suggested learning sequence to match his course arc.
He creates a class called COGS 101 — Week 4 and generates an enrollment code. He posts the code and a two-sentence instruction on the course LMS:
"Before Thursday's class, spend 30–40 minutes exploring the Working Memory map on ke.skoonline.org. Your goal is not to read every node — it is to find the two or three connections you find most surprising and be ready to explain why."
He deliberately does not prescribe a path. That ambiguity is the instruction.
Over the 48 hours before Thursday, 31 of 35 students log in. Their exploration patterns diverge immediately:
Wednesday evening, Prof. Osei opens the Teacher Analytics Dashboard. He can see:
He rewrites the first 20 minutes of Thursday's class. Instead of introducing Germane Load from scratch, he opens with: "Most of you skipped Germane Load on the map. Let's talk about why that node is the most important one there."
Thursday's class runs differently. Because students arrive with genuine exposure — not just claimed reading — Prof. Osei can open with discussion questions rather than explanation:
"Someone in Q2L asked: 'Why does Cognitive Load decrease as expertise increases — isn't more knowledge more to manage?' Who can answer that using what you found on the map?"
The CS students draw on their cache analogy. The Psychology students connect it to schema automation. A student who had explored Dual Coding connects it to chunking. The class constructs the explanation collectively.
Prof. Osei spends the last 15 minutes on Germane Load — the one node most students skipped — knowing they now have the prerequisite understanding to grasp it.
Over the following weeks, Prof. Osei uses the Learner Profile view to monitor individual students. He notices two patterns:
High self-regulators (about 40% of the class) revisit nodes after class, expand sub-maps independently, and show increasing Q2L depth scores over time — their questions move from factual to causal across weeks.
Passive consumers (about 25%) visit the map, read the top-level description of each node, and close it. They never use Q2L. Their interaction pattern looks like skimming the textbook, just faster.
He emails the passive-consumer group personally — not to penalise, but to invite office hours. He uses their specific map paths as the conversation starter: "I see you spent time on Working Memory but didn't click the edge to Cognitive Load. What made that connection unclear?"