T-GEM parallels Scientific Inquiry

T-GEM framework incorporates Technology into Generate, Evaluate and Modify cycles, paralleling scientific inquiry. Authentic science involves engaging learners with model-based inquiry to make sense of observations and find patterns in information. Learners construct mental representations, predicting before exploring with computer simulations, developing abductive reasoning (Khan, 2007) where constructed hypotheses from new concepts inform later hypotheses. Students harboring alternative conceptions might be addressed through visual representations and interactive media for enriched models. Being able to for example visualize molecular levels in Chemistry highlight macroscopic properties like ductility and malleability resulting from microscopic interactions. T-GEM uses extreme cases, analogy, surprise, discrepant information, confirmation strategies, problem-solving, comparisons and incremental values for learner-centred instruction (Khan, 2007). Researchers introduced merely enough background to understand what data represents, actually discouraging students from reading texts beforehand. Students generate rules explaining aloud, using spontaneous analogies to explain relationships, making comparisons to expand scope, coordinating theoretical models with empirical consistency. Discrepant information asks students to work back from the data, postulating hidden causal factors drawing conclusions from puzzling data. Students iteratively explore logical and conceptual modification to generate multiple relationships before identifying extreme cases. By not correcting initial models, anomalies enable GEM cycles to successively refine understanding to sustain inquiry. New GEM cycles begin whenever (sub)topics are changed, generating hypothesis wondering what would happen if. Cognitive models serve as personal representations of (un)observable phenomena, where T-GEM enables teacher-student-computer interactions, making explicit connections to prevent overload during experimentation.

Similar to WISE embedding inquiry maps to change representations, T-GEM fosters asynchronous dialogue, manipulating simulations rapidly testing ideas as independent tutors. There are both affordances and limitations in using computers to teach science regarding virtual presence, weighing social context and learning intent with empirical evidence. Through predicting-observing-explaining, teacher roles shift towards poser, provider, guide, assessor, actor, lecturer, modeler and helper (Khan 2010). T-GEM provides digital representation bounding case temporally and spatially, not intended to establish generalizable claims but only comparability and translatability. Jasper videos likewise employ anchored instruction present learning in authentic contexts. Not revealing problems until the end of video clips ask students to work together determining what information is pertinent and prioritizing optimal solutions rather than single correct answers. Although WISE provides intuitive authoring functionality, educators sacrifice considerable upfront investment to setup. Moreover students would readily need computer access to work through modules at their own pace, making individual assessment challenging. MyWorld is limited to examining geographical patterns, suited for curriculum involving for example global precipitation, population densities and energy transfer. While various layers can be superimposed on maps comparing times easily creating difference maps, traditionally content is presented as foundational concepts before real world application. For instance, we cover acid precipitation in discussing oxide reactions and buffers, only afterwards extending conversations to global issues. Even Chemland suite is not intended to replace laboratories, but provide environments to analyze data, find trends, push variables to generate relationships. Simulations visually draw attention to contrasts verifying predictions exploring data through graphical trends.

Technology-enhanced inquiry explores plausibility of ideas, explaining abnormal data based on empirical consistency and theoretical models. Simulations enable learner-directed exploration and control, choosing things to explore spontaneously reselecting variables. Teachers provide rough definitions, emphasizing that individual experimenters do tons of work to get a few little data points, modelling scientific community exploration. Technology should be used to augment not replace, making unobservable processes more explicit, evaluating consistency of what if relationships. Designing technology-enhanced learning environments provide alternatives to redirect already present resources to enhance understanding. Multiple simulations from Chemland suite overlap with school curriculum, potentially supplementing lectures prompting self-discovery. Simulations can be introduced during lectures to visualize paper phenomena, engaging learners in making visible the invisible. Simulations can also enable authentic inquiry with embedded scaffolding worked into design.

Graphing Stories (without motion probes) Interpret motions on position-time graphs
Chemical Reactions: How Can We Slow Climate Change Explore limiting reactants in greenhouse context
Periodic Table


Visualize periodic trends as bar graphs (ex. radius, ionization, electronegativity, electron affinity, melting point) and electron configuration as arrow-in-box diagrams
Limiting Reactants Bar graphs indicate limiting excess quantities for five reactions
Titrations Drop the base into unknown acid solution using indicators
Calorimetry Measuring Heats of Reaction Ignite bomb calorimeter to find enthalpy changes, exploring heat capacity and transfer
Hess’s Law Create reaction mechanisms, flipping reactions to determine reaction enthalpies
Bond Energy and Delta H of Reaction Calculate enthalpy change using bond enthalpies, varying bond strengths of reactants and products


Khan, S. (2007). Model-based inquiries in chemistry. Science Education, 91(6), 877-905.

Khan, S. (2010). New pedagogies for teaching with computer simulations. Journal of Science Education and Technology, 20(3), 215-232.


  1. Hi Andrew

    I like the fact that, in the last paragraph you mentioned “abnormal data”.

    I wonder why student get frustrated when looking at abnormal data. For example, a straight line graph with one data point is not lined up with the other points.

    A good next step might be to share when TELEs would not be suitable in a lesson.


    1. Thanks for the comments Christopher. Abnormal data and discrepant events gets students to consider why there are exceptions to rules and patterns (or sometimes learners just write them off as ‘error’). I appreciate your last suggestion to consider places where TELEs would not be suitable. Immediate access to technology and internet comes to mind, though even with home connectivity, TELEs like other pedagogies are better suited towards certain learning styles more than others, needing to be matched appropriately given classroom dynamics.


  2. Thanks for your summary. I think one important distinction to make between scientific inquiry and T-GEM is the emphasis on application of knowledge. Being apart of the scientific community, I can tell you that we have not been the best at taking our knowledge (from research, observations etc) and putting it into practice. I think that’s why we know have grants specifically aimed towards “knowledge translation”, as our practice significantly lags our research finding. I really like how T-GEM emphasizes the application part because I think it’s the most useful part about learning!

    1. Thanks for the comment Momoe. I agree we as educators are prone to falling behind research findings, staying current and putting things into practice. Nice evidence with grants on knowledge translation. There’s continual balance between prescribed outcomes from the ministry and teacher discretion with breadth and depth in their subject area, given their social context.


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