Research Methods in EdTech
Expert-defined terms from the Postgraduate Certificate in EdTech and AI in Education course at London School of International Business. Free to read, free to share, paired with a professional course.
Action Research #
Action Research
Explanation #
A cyclical method where practitioners identify a problem, implement an intervention, collect data, reflect, and refine the approach.
Example #
A teacher modifies a digital quiz platform after each class based on student performance data.
Application #
Improves instructional design of learning management systems (LMS) by grounding changes in real‑time feedback.
Challenges #
Requires time‑intensive data collection and may suffer from researcher bias if not carefully documented.
Adaptive Learning #
Adaptive Learning
Explanation #
Technology that adjusts content, pacing, or pathways according to each learner’s demonstrated knowledge and skill level.
Example #
An AI‑driven math app presents easier problems after a series of incorrect answers.
Application #
Enhances mastery learning in MOOCs by providing tailored remediation.
Challenges #
Designing accurate adaptation algorithms and ensuring equity across diverse learner populations.
A/B Testing #
A/B Testing
Explanation #
A controlled experiment that compares two versions of a digital artifact to determine which performs better on a defined metric.
Example #
Testing two layouts of an online discussion forum to see which yields higher participation rates.
Application #
Optimizes user interface (UI) elements of educational apps for engagement.
Challenges #
Requires sufficient sample size and careful randomization to avoid confounding variables.
Analytics Dashboard #
Analytics Dashboard
Explanation #
A visual interface that aggregates and displays metrics such as completion rates, time on task, and assessment scores.
Example #
A university’s LMS dashboard shows departmental average quiz scores across semesters.
Application #
Supports data‑driven decision making for curriculum improvement.
Challenges #
Overreliance on superficial metrics may mask deeper learning issues; data privacy must be safeguarded.
Artificial Intelligence (AI) #
Artificial Intelligence (AI)
Explanation #
Computational techniques that enable machines to mimic human cognition, such as pattern recognition, prediction, and language understanding.
Example #
An AI chatbot provides instant feedback on short‑answer questions.
Application #
Automates grading, personalizes content, and predicts student at‑risk status.
Challenges #
Algorithmic bias, transparency, and the need for large, high‑quality training datasets.
Attribution Modeling #
Attribution Modeling
Explanation #
A statistical approach that assigns credit to multiple touchpoints influencing a learner’s outcome.
Example #
Determining how webinars, email reminders, and in‑app notifications collectively affect course completion.
Application #
Guides resource allocation for effective learner support interventions.
Challenges #
Complex interaction effects and data fragmentation across platforms.
Bayesian Inference #
Bayesian Inference
Explanation #
A probabilistic framework that updates beliefs about a parameter as new evidence becomes available.
Example #
Updating the estimated difficulty of a quiz item after each cohort’s responses.
Application #
Enables dynamic assessment calibration in adaptive testing.
Challenges #
Requires computational expertise and careful selection of priors to avoid misleading results.
Blended Learning #
Blended Learning
Explanation #
An instructional design that combines face‑to‑face teaching with digital learning activities.
Example #
Students watch recorded lectures online, then engage in in‑person problem‑solving sessions.
Application #
Increases flexibility while preserving social interaction.
Challenges #
Aligning online and offline components to avoid redundancy or gaps.
Bloom’s Taxonomy #
Bloom’s Taxonomy
Explanation #
A hierarchical classification of learning objectives ranging from remembering to creating.
Example #
Designing a project where learners must synthesize research findings into a novel application.
Application #
Guides the development of assessment items aligned with desired cognitive levels.
Challenges #
Over‑reliance on lower‑order skills can limit higher‑order thinking opportunities.
Cluster Analysis #
Cluster Analysis
Explanation #
A statistical technique that groups learners based on similarity across multiple variables.
Example #
Identifying a cluster of students who frequently access multimedia resources but score low on assessments.
Application #
Targets interventions to specific learner sub‑populations.
Challenges #
Determining the optimal number of clusters and interpreting them meaningfully.
Collaborative Filtering #
Collaborative Filtering
Explanation #
An algorithmic method that suggests items to a learner based on the preferences of similar users.
Example #
Suggesting supplemental videos to a student because peers with comparable profiles found them helpful.
Application #
Personalizes resource recommendations in digital libraries.
Challenges #
Cold‑start problem for new users and potential reinforcement of echo chambers.
Content Analysis #
Content Analysis
Explanation #
A systematic process of categorizing textual or multimedia data to identify patterns or themes.
Example #
Coding forum posts to assess the prevalence of self‑efficacy statements.
Application #
Evaluates affective dimensions of learner interaction.
Challenges #
Requires reliable coding protocols and can be time‑consuming.
Construct Validity #
Construct Validity
Explanation #
The degree to which a test accurately measures the theoretical construct it intends to assess.
Example #
Validating that a digital game measures problem‑solving rather than merely reaction time.
Application #
Ensures that research findings reflect genuine learning outcomes.
Challenges #
Requires rigorous theoretical grounding and empirical testing.
Curriculum Mapping #
Curriculum Mapping
Explanation #
The process of visually linking standards, objectives, assessments, and instructional activities across a program.
Example #
Mapping digital literacy competencies to specific modules in an online teacher‑training course.
Application #
Identifies gaps and redundancies in program design.
Challenges #
Maintaining up‑to‑date maps as curricula evolve.
Data Triangulation #
Data Triangulation
Explanation #
Combining multiple data sources or methods to corroborate findings and enhance credibility.
Example #
Merging clickstream logs, survey responses, and interview transcripts to evaluate a learning analytics tool.
Application #
Provides a richer, more robust understanding of learner behavior.
Challenges #
Integrating disparate data formats and resolving conflicting evidence.
Design #
Based Research (DBR)
Explanation #
A collaborative approach that designs, implements, and studies educational interventions in real settings to refine theory and practice.
Example #
Developing a gamified feedback system, testing it across semesters, and revising design based on emergent data.
Application #
Bridges the gap between laboratory research and classroom practice.
Challenges #
Managing complexity of real‑world contexts and ensuring methodological rigor.
Digital Divide #
Digital Divide
Explanation #
The gap between individuals or groups who have adequate access to digital technologies and those who do not.
Example #
Rural students lacking high‑speed internet experience lower participation in synchronous webinars.
Application #
Informs policy decisions on infrastructure investment and inclusive design.
Challenges #
Multifaceted nature involving socioeconomic, geographic, and cultural factors.
Ecological Validity #
Ecological Validity
Explanation #
The extent to which research findings can be applied to everyday educational environments.
Example #
Testing an AI tutor in a lab versus deploying it across multiple schools.
Application #
Increases confidence that interventions will function outside controlled settings.
Challenges #
Balancing experimental control with authentic contexts.
Effect Size #
Effect Size
Explanation #
A quantitative measure of the magnitude of a treatment’s impact, independent of sample size.
Example #
Reporting that a new interactive module yields a medium effect (d = 0.5) on post‑test scores.
Application #
Facilitates comparison across studies and informs meta‑analysis.
Challenges #
Interpreting practical significance and ensuring appropriate calculation.
Ethnography #
Ethnography
Explanation #
An in‑depth qualitative method that explores learners’ lived experiences within their natural contexts.
Example #
Observing how teachers integrate a virtual reality (VR) simulation into daily lessons.
Application #
Generates nuanced insights into technology adoption processes.
Challenges #
Time‑intensive, requires reflexivity, and may raise privacy concerns.
Exploratory Factor Analysis (EFA) #
Exploratory Factor Analysis (EFA)
Explanation #
A statistical technique used to uncover underlying factor structures among observed variables.
Example #
Identifying three latent dimensions—cognitive, affective, and behavioral engagement—from questionnaire items.
Application #
Helps refine measurement instruments for EdTech research.
Challenges #
Requires large sample sizes and careful interpretation of factor loadings.
Feedback Loop #
Feedback Loop
Explanation #
A cycle where learner performance data informs immediate instructional adjustments, which in turn generate new data.
Example #
An AI tutor provides hints after each incorrect answer, then updates its model of the learner’s misconceptions.
Application #
Supports personalized learning pathways.
Challenges #
Designing timely and pedagogically sound feedback without overwhelming learners.
Flipped Classroom #
Flipped Classroom
Explanation #
A model where learners first encounter instructional content outside class (e.g., via video) and use class time for applied activities.
Example #
Students watch a tutorial on coding syntax at home, then collaborate on debugging exercises in class.
Application #
Maximizes higher‑order learning during face‑to‑face sessions.
Challenges #
Requires reliable access to pre‑class materials and learner accountability.
Framework for Learning Analytics (FLA) #
Framework for Learning Analytics (FLA)
Explanation #
A structured set of guidelines that outlines the collection, analysis, and use of learning data within an institution.
Example #
Implementing a policy that mandates anonymization of student interaction logs before analysis.
Application #
Provides a roadmap for responsible analytics deployment.
Challenges #
Balancing transparency with privacy, and aligning diverse stakeholder expectations.
Growth Mindset Intervention #
Growth Mindset Intervention
Explanation #
An instructional strategy designed to foster the belief that abilities can be developed through effort and strategies.
Example #
Embedding short videos that emphasize neuroplasticity before a challenging assessment.
Application #
Improves resilience and persistence in technology‑rich learning environments.
Challenges #
Measuring attitudinal shifts and ensuring sustained impact.
Heuristic Evaluation #
Heuristic Evaluation
Explanation #
A usability inspection method where experts assess an interface against established design principles (heuristics).
Example #
Evaluating a learning portal for consistency, error prevention, and user control.
Application #
Identifies design flaws early in development.
Challenges #
Depends on evaluator expertise and may miss context‑specific issues.
Hybrid Research Design #
Hybrid Research Design
Explanation #
A research approach that combines quantitative and qualitative components within a single study to leverage strengths of both.
Example #
Conducting a survey on learner satisfaction while also holding focus groups to explore underlying reasons.
Application #
Provides comprehensive insight into EdTech interventions.
Challenges #
Requires careful integration of data strands and expertise in both paradigms.
Human‑Centered Design (HCD) #
Human‑Centered Design (HCD)
Explanation #
A design philosophy that places learners’ needs, contexts, and feedback at the core of technology development.
Example #
Co‑creating a mobile learning app with students through iterative prototyping sessions.
Application #
Increases adoption and effectiveness of educational tools.
Challenges #
Time‑intensive stakeholder engagement and reconciling divergent user preferences.
Impact Evaluation #
Impact Evaluation
Explanation #
Systematic assessment of the long‑term effects of an intervention on targeted outcomes.
Example #
Measuring graduate employment rates three years after completing an AI‑enhanced curriculum.
Application #
Informs policy decisions and funding allocations.
Challenges #
Isolating the intervention’s effect from external influences and maintaining participant tracking.
Incremental Validity #
Incremental Validity
Explanation #
The extent to which a new measure adds explanatory power beyond existing instruments.
Example #
Demonstrating that a digital metacognition tracker predicts final grades above traditional quizzes.
Application #
Justifies adoption of novel assessment tools.
Challenges #
Requires robust statistical modeling and appropriate comparison baselines.
Infographic Literacy #
Infographic Literacy
Explanation #
The ability to decode, interpret, and critically evaluate information presented in infographic format.
Example #
Assessing learners’ capacity to extract key statistics from a research summary graphic.
Application #
Supports development of critical data literacy in digital curricula.
Challenges #
Varies with prior visual experience and cultural conventions.
Inter #
rater Reliability
Explanation #
A metric that quantifies the degree of agreement between multiple coders rating the same data.
Example #
Achieving a kappa of 0.78 when two researchers code discussion forum sentiments.
Application #
Enhances credibility of qualitative analyses.
Challenges #
Requires clear coding schemes and training to achieve high agreement.
Iterative Prototyping #
Iterative Prototyping
Explanation #
A cyclical process of creating, testing, and refining a prototype based on user feedback.
Example #
Building successive versions of a virtual lab simulation, each incorporating learner suggestions.
Application #
Accelerates design improvements and aligns product with learner needs.
Challenges #
Managing scope creep and ensuring each iteration is sufficiently evaluated.
Learning Analytics #
Learning Analytics
Explanation #
The measurement, collection, analysis, and reporting of data about learners and their contexts to improve learning.
Example #
Predicting dropout risk using attendance, assignment submission, and forum participation data.
Application #
Enables early warning systems and targeted support.
Challenges #
Data quality, privacy concerns, and interpretation of complex metrics.
Learning Management System (LMS) #
Learning Management System (LMS)
Explanation #
A software application for the administration, documentation, tracking, reporting, and delivery of educational courses.
Example #
Moodle hosting a blended course with quizzes, forums, and gradebooks.
Application #
Centralizes instructional resources and learner data.
Challenges #
Usability, scalability, and alignment with pedagogical goals.
Learning Style Theory #
Learning Style Theory
Explanation #
A contested framework suggesting that individuals learn best when instruction aligns with their preferred sensory modality.
Example #
Offering both video and text explanations for a concept.
Application #
Often used to justify differentiated resource provision.
Challenges #
Empirical support is weak; over‑reliance may limit instructional diversity.
Latent Growth Modeling (LGM) #
Latent Growth Modeling (LGM)
Explanation #
A statistical technique that models change over time at the individual level, capturing both initial status and growth rate.
Example #
Tracking students’ self‑efficacy scores across five semesters to identify distinct growth patterns.
Application #
Informs design of interventions that target specific phases of learner development.
Challenges #
Requires multiple measurement points and sophisticated software.
Mixed Methods #
Mixed Methods
Explanation #
An approach that deliberately combines both qualitative and quantitative data collection and analysis within a single study.
Example #
Surveying 300 learners on satisfaction while conducting 20 in‑depth interviews.
Application #
Provides a richer, more nuanced understanding of EdTech impacts.
Challenges #
Demands expertise in both paradigms and careful timing of data collection phases.
Multimodal Learning Analytics (MMLA) #
Multimodal Learning Analytics (MMLA)
Explanation #
The analysis of data from multiple channels (e.g., audio, video, physiological sensors) to understand complex learning processes.
Example #
Using facial expression detection to gauge frustration during a coding task.
Application #
Offers deeper insights into affective and cognitive states.
Challenges #
Technical integration, data storage, and ethical considerations around biometric data.
Neural Network #
Neural Network
Explanation #
A computational model inspired by the brain’s interconnected neurons, capable of learning complex patterns from data.
Example #
A convolutional neural network classifies handwritten mathematical symbols.
Application #
Powers advanced adaptive assessment and content recommendation systems.
Challenges #
Opacity (“black‑box” problem), large training data requirements, and computational cost.
Observational Study #
Observational Study
Explanation #
A research design that records behavior in real‑world settings without manipulating variables.
Example #
Monitoring how students interact with an AR app during a museum visit.
Application #
Captures authentic usage patterns of educational technologies.
Challenges #
Limited control over extraneous variables and potential observer bias.
Open Educational Resources (OER) #
Open Educational Resources (OER)
Explanation #
Freely accessible teaching, learning, and research materials that can be adapted and redistributed.
Example #
A university adopts a CC‑BY textbook for an introductory AI course.
Application #
Reduces cost barriers and promotes collaborative content creation.
Challenges #
Ensuring quality, sustainability, and alignment with curriculum standards.
Ordinal Scale #
Ordinal Scale
Explanation #
A measurement scale that reflects relative ordering of items but does not assume equal intervals between points.
Example #
Survey items ranging from “Strongly disagree” to “Strongly agree.”
Application #
Commonly used in attitude and satisfaction questionnaires.
Challenges #
Limits the types of statistical analyses that can be performed.
Participatory Design #
Participatory Design
Explanation #
A collaborative approach where end‑users actively contribute to the design process of a technology.
Example #
Teachers and students jointly designing the navigation structure of a new LMS module.
Application #
Increases relevance and acceptance of EdTech solutions.
Challenges #
Managing divergent ideas and ensuring equitable participation.
Pedagogical Content Knowledge (PCK) #
Pedagogical Content Knowledge (PCK)
Explanation #
The intersection of content knowledge and pedagogy, enabling teachers to convey concepts effectively.
Example #
Using a visual metaphor to explain recursion in programming.
Application #
Guides professional development for technology‑enhanced instruction.
Challenges #
Translating PCK into digital formats without loss of nuance.
Personal Learning Environment (PLE) #
Personal Learning Environment (PLE)
Explanation #
A set of tools, resources, and services that learners curate to support their own learning processes.
Example #
A student’s PLE includes a note‑taking app, a curated YouTube playlist, and a citation manager.
Application #
Encourages autonomy and lifelong learning habits.
Challenges #
Requires digital literacy and may lead to fragmented experiences.
Phenomenography #
Phenomenography
Explanation #
A research method that explores the different ways people experience or conceptualize a phenomenon.
Example #
Identifying distinct ways teachers perceive the role of AI in assessment.
Application #
Informs design of professional development programs.
Challenges #
Requires rigorous interview techniques and careful categorization.
Predictive Modeling #
Predictive Modeling
Explanation #
The use of statistical or algorithmic techniques to forecast future outcomes based on historical data.
Example #
Predicting which students will achieve proficiency on a digital literacy test.
Application #
Enables proactive interventions and resource allocation.
Challenges #
Model overfitting, data drift, and interpretability concerns.
Qualitative Coding #
Qualitative Coding
Explanation #
The systematic process of labeling segments of textual or visual data to identify patterns and themes.
Example #
Coding chat transcripts for instances of collaborative problem solving.
Application #
Provides depth to understanding of learner interactions.
Challenges #
Labor‑intensive and subject to coder bias.
Randomized Controlled Trial (RCT) #
Randomized Controlled Trial (RCT)
Explanation #
A study where participants are randomly allocated to either an intervention or a comparison condition to assess causal effects.
Example #
Randomly assigning half of a cohort to receive an AI‑driven tutoring system and half to a traditional textbook.
Application #
Generates high‑quality evidence on EdTech efficacy.
Challenges #
Ethical considerations, logistical complexity, and participant attrition.
Rasch Modeling #
Rasch Modeling
Explanation #
A probabilistic model that estimates both item difficulty and learner ability on a common scale.
Example #
Calibrating quiz items so that a score of 70 indicates mastery of the underlying skill.
Application #
Supports adaptive testing and fair assessment across diverse learners.
Challenges #
Requires unidimensionality and sufficient response data.
Reflective Practice #
Reflective Practice
Explanation #
The habit of continuously analyzing one’s own teaching actions to improve future performance.
Example #
An instructor reviews analytics dashboards after each session to adjust pacing.
Application #
Promotes continuous improvement in technology integration.
Challenges #
Time constraints and need for supportive feedback mechanisms.
Remote Proctoring #
Remote Proctoring
Explanation #
The use of technology to monitor exam takers remotely to ensure academic integrity.
Example #
Using webcam and AI‑based facial recognition to detect cheating during an online final.
Application #
Enables secure assessment in fully digital courses.
Challenges #
Balancing security with student privacy and accessibility.
Research Ethics Board (REB) #
Research Ethics Board (REB)
Explanation #
A committee that reviews research proposals to ensure ethical standards are met, especially regarding human participants.
Example #
Obtaining approval before collecting clickstream data from learners.
Application #
Safeguards participant rights and institutional compliance.
Challenges #
Navigating lengthy approval processes and evolving data regulations.
Response Time Analysis #
Response Time Analysis
Explanation #
Examining the interval between a learner’s stimulus and their response to infer processing difficulty.
Example #
Measuring longer response times on complex problem‑solving items in a digital quiz.
Application #
Identifies concepts that may require additional scaffolding.
Challenges #
Requires precise timestamp synchronization and may be confounded by external distractions.
Scalable Learning Analytics #
Scalable Learning Analytics
Explanation #
Techniques that allow analytics to handle large volumes of learner data efficiently.
Example #
Deploying a distributed processing pipeline to analyze millions of interaction events nightly.
Application #
Supports institution‑wide dashboards and early‑warning systems.
Challenges #
Infrastructure costs, data governance, and ensuring algorithmic fairness at scale.
Self‑Efficacy #
Self‑Efficacy
Explanation #
An individual’s belief in their capability to execute tasks and achieve goals.
Example #
Learners with high self‑efficacy persist longer on challenging coding exercises.
Application #
Predictor of engagement and performance in technology‑rich environments.
Challenges #
Measuring accurately and distinguishing from related constructs like self‑esteem.
Sentiment Analysis #
Sentiment Analysis
Explanation #
Computational technique that determines the emotional tone behind textual data.
Example #
Analyzing forum posts to gauge student satisfaction with a new LMS feature.
Application #
Provides rapid feedback on learner attitudes.
Challenges #
Contextual nuance, sarcasm detection, and language diversity.
Sequential Mixed Methods #
Sequential Mixed Methods
Explanation #
A mixed‑methods approach where one data collection phase follows another, informing subsequent steps.
Example #
Conducting a survey first, then using its results to design focus‑group questions.
Application #
Allows refinement of instruments based on preliminary findings.
Challenges #
Extended timelines and need for flexible research planning.
Simulation #
Based Learning
Explanation #
Educational experiences that replicate real‑world processes through interactive digital models.
Example #
A virtual chemistry lab where students conduct experiments without hazardous materials.
Application #
Provides safe, repeatable practice for complex skills.
Challenges #
High development costs and ensuring fidelity to authentic tasks.
Social Network Analysis (SNA) #
Social Network Analysis (SNA)
Explanation #
A method for mapping and measuring relationships and flows between people, groups, or entities.
Example #
Visualizing peer‑to‑peer communication patterns in an online discussion forum.
Application #
Identifies influential learners and potential collaboration gaps.
Challenges #
Data privacy, dynamic network changes, and interpretation of structural metrics.
Software Usability Testing #
Software Usability Testing
Explanation #
Systematic evaluation of how easily users can learn and use a software product to achieve specific goals.
Example #
Observing students as they navigate a new e‑portfolio platform while verbalizing their thoughts.
Application #
Informs iterative improvements to interface design.
Challenges #
Recruiting representative participants and capturing authentic usage contexts.
Standardized Assessment #
Standardized Assessment
Explanation #
An evaluation administered and scored in a consistent manner across different settings.
Example #
A national computer‑science proficiency test delivered online.
Application #
Facilitates comparison of learner performance across institutions.
Challenges #
Aligning test items with diverse curricula and ensuring cultural fairness.
Statistical Power #
Statistical Power
Explanation #
The probability that a test will correctly reject a false null hypothesis, i.e., detect a true effect.
Example #
A study with 80% power to detect a medium effect of a new gamified assignment.
Application #
Guides sample size calculations for EdTech experiments.
Challenges #
Balancing power with resource constraints and ethical considerations of participant recruitment.
Student‑Generated Content (SGC) #
Student‑Generated Content (SGC)
Explanation #
Learning materials created by learners themselves, often shared within a community.
Example #
Students produce tutorial videos on using a new data‑visualization tool.
Application #
Enhances deep learning and builds digital portfolios.
Challenges #
Quality control and ensuring alignment with learning objectives.
Survey Instrument Validation #
Survey Instrument Validation
Explanation #
The process of confirming that a questionnaire accurately measures the intended constructs and yields consistent results.
Example #
Conducting a factor analysis to confirm that items load onto intended dimensions of digital competence.
Application #
Increases confidence in data collected for EdTech research.
Challenges #
Requires iterative refinement and sufficient respondent numbers.
Technology Acceptance Model (TAM) #
Technology Acceptance Model (TAM)
Explanation #
A theoretical framework that predicts user adoption of technology based on perceived benefits and effort.
Example #
Using TAM to assess faculty willingness to adopt a new analytics dashboard.
Application #
Guides change management strategies for EdTech deployment.
Challenges #
May oversimplify complex motivational factors and cultural influences.
Thick Description #
Thick Description
Explanation #
Detailed narrative that situates observations within their broader social and cultural context.
Example #
Providing a vivid account of how learners interact with an AR field‑trip in a rural school.
Application #
Enhances transferability of qualitative findings.
Challenges #
Requires extensive fieldwork and narrative skill.
Time‑Series Analysis #
Time‑Series Analysis
Explanation #
Statistical techniques for analyzing data points collected sequentially over time to identify patterns.
Example #
Examining weekly login frequencies to detect seasonal dips in platform usage.
Application #
Informs scheduling of interventions and resource planning.
Challenges #
Requires consistent data collection intervals and handling of autocorrelation.
Transferability #
Transferability
Explanation #
The extent to which research findings can be applied to other settings, populations, or times.
Example #
Assessing whether results from a pilot AI tutoring study in one university hold for community colleges.
Application #
Supports scaling decisions for EdTech innovations.
Challenges #
Differences in infrastructure, learner demographics, and institutional culture may limit applicability.
Triangulation #
Triangulation
Explanation #
The use of multiple methods or data sources to cross‑verify findings and strengthen credibility.
Example #
Combining clickstream analysis, survey responses, and interview data to evaluate a new LMS feature.
Application #
Reduces bias and enhances robustness of conclusions.
Challenges #
Requires careful coordination and synthesis of heterogeneous data.
Usability Heuristics #
Usability Heuristics
Explanation #
General rules of thumb for evaluating the ease of use and efficiency of an interface.
Example #
Checking that error messages are clearly displayed and offer constructive solutions.
Application #
Guides rapid assessment of educational software.
Challenges #
Heuristics are generic and may need adaptation for specific learning contexts.
Validity Threats #
Validity Threats
Explanation #
Factors that can compromise the accuracy of inferences drawn from research data.
Example #
Participants dropping out disproportionately from the control group, threatening internal validity.
Application #
Informs rigorous study design and interpretation.
Challenges #
Identifying and mitigating multiple concurrent threats.
Video Analytics #
Video Analytics
Explanation #
The extraction of meaningful data from video recordings to assess learner behavior and affect.
Example #
Measuring head nods as a proxy for comprehension during a recorded lecture.
Application #
Provides real‑time feedback to instructors on student engagement.
Challenges #
Privacy concerns, processing overhead, and accuracy of affect detection algorithms.
Virtual Reality (VR) Pedagogy #
Virtual Reality (VR) Pedagogy
Explanation #
Instructional strategies designed to leverage the unique affordances of VR environments for learning.
Example #
A VR anatomy lab where students manipulate 3‑D models of the human heart.
Application #
Enhances spatial reasoning and experiential learning.
Challenges #
High hardware costs, motion sickness, and designing curriculum‑aligned experiences.
Weighted Least Squares (WLS) #
Weighted Least Squares (WLS)
Explanation #
A regression method that assigns different weights to observations to account for varying variance.
Example #
Applying WLS to model student scores where variance differs across proficiency levels.
Application #
Improves model accuracy when assumptions of ordinary least squares are violated.
Challenges #
Determining appropriate weights and ensuring model stability.
Wikis as Learning Platforms #
Wikis as Learning Platforms
Explanation #
Online environments that allow multiple users to create, edit, and organize content collectively.
Example #
A course‑wide wiki where students co‑author summaries of research articles.
Application #
Fosters collective knowledge building and digital literacy.
Challenges #
Monitoring content quality and managing edit conflicts.
Zero‑Inflated Models #
Zero‑Inflated Models
Explanation #
Statistical models that account for excess zeros in count data by modeling the zero-generating process separately.
Example #
Modeling the number of times students access a supplemental resource, where many never access it.
Application #
Provides more accurate estimates of usage patterns in EdTech adoption studies.
Challenges #
Model complexity and interpretation of dual processes.