Here’s a link to my final project, creating three artifacts to teach Periodicity in Chemistry:
Here’s a link to my final project, creating three artifacts to teach Periodicity in Chemistry:
Similar to my final design project TELE for teaching Periodicity but relevant to this post, interactives through Module B: Chemland Suite visualize periodic trends as 3D bar graphs on periodic table outline:
Pictorial representations are more approachable than raw numbers from the data booklet, intuitively comparing values between elements on different periods and groups. It however does nothing to explain fundamental concepts like why ionization energy increases across periods, resulting in similar traditional instruction problems to memorize trends without understanding. Based on my final project, Excel can be used to zoom in on specific trends, having students work through observations at their own pace, noting discrepant events through guided inquiry (Driver et al., 1994). Simulations embed just in time prompts, minimizing unnecessary details and pressing variables freely. Otherwise pure discovery can be frequently overwhelming where learners cannot reflect upon their own learning.
Simulations can be used to supplement lectures and verify empirical data from lab work, reviewing concepts to fill in missing information. Inquiry through agency models scientific reasoning, actively constructing knowledge. Conceptual models communicate invisible ideas, developing explanations to make sense of the natural world. Although textual representations enable higher precision and control, concrete environments help students use knowledge rather than simply memorize. With flexible private theories, learners develop original hypotheses formalizing ideas towards knowledge, enabling self-directed study with sufficient freedom testing alternative iterations (Xiang and Passmore, 2014). Modelling involves analyzing, synthesizing, debugging and explaining, progressing through multiple cycles of construction, quantification, interpretation and revision.
Driver, R., Asoko, H., Leach, J., Mortimer, E., & Scott, P. (1994). Constructing scientific knowledge in the classroom. Educational Researcher, 23(7), 5-12.
Xiang, L., & Passmore, C. (2014). A framework for model-based inquiry through agent-based programming. Journal of Science Education and Technology, doi:10.1007/s10956-014-9534-4
How can learning be distributed and accelerated with access to digital resources and specialized tools and what are several implications of learning of math and science just in time and on demand?
Knowledge is actively built and socially constructed upon prior conceptions and personal theories. Learning views do not require specific pedagogy necessarily, though invented constructs imposed on phenomena are socially negotiated, then validated as public knowledge. Technology enhances distributed learning, challenging ideas through discrepant events while introducing multiple ways of seeing. Learners form communities of practices through cultural apprenticeship co-constructing knowledge (Driver et al., 1994), though inquiry requires guidance being unlikely that inexperienced students learn through pure discovery. Teachers gradually withdraw support as students connect plausible mental representations towards symbolic convention, where intervention helps make sense of further action. Meaning is constructed in conversation resolving disequilibrium knowledge schemas, inducing cognitive conflict along with social interaction to provide multiple perspectives. Teachers recognize students hold plural conceptions given social context, structuring tasks to internalize and enculture experiential evidence. Students develop common sense reasoning using everyday language and pragmatic understanding rather than adopting coherent world picture, leveraging models for scope.
Globe provides universal access to employ natural curiosity, actively participating with distributed collaborators, researching spatial-temporal data. For accuracy, training the trainer ensures first line of defense against erroneous data (Butler and MacGregot, 2003). Systems provides integrated understanding with graphical, visual and technical tools, enabling international cooperation with multiple languages across isolated communities. Student interaction with adult professionals offers authenticity, enhancing commitment and quality assurance. Given uniform classification systems and protocols, sampling techniques perform over 80% accuracy, where ongoing collection allows for durable, low-cost, long-term stability, empowering students to do responsible science. Active research projects and learner involvement become valuable incentive to improve analytical interpretation, supplementing classroom activities to make informed inferences. Motivational factors like challenge, fantasy and curiosity sustain goal-directed behaviour, with challenge neither too steep or simple providing novelty, interest and importance (Srinivasan et al., 2006). Working memory allows for simultaneous processing and information preservation, providing various worked out examples as effective strategy. Challenges with time availability and systematic schedules need to be overcome, focusing on fundamental science content and method. Debugging breadboard components is time consuming, fraught with variables to address prior knowledge, ability and motivation. Curiously although simulations were less cost expensive, participants deemed software as fake unable to provide authentic experience, resulting in little quantitative difference between physical equipment (Srinivasan et al., 2006). Users described how not knowing background made practical training difficult let alone theoretical, where both real hardware and simulation laboratory provide incomplete solutions.
Science in visiting hands-on interactive centers allow free-choice learning guided by well-formed interests. Leisure settings provide brief, moderately structured activity while retaining considerable personal control. Visitors as active meaning seekers balance learning and entertainment categorized into five broad motivational categories: Explorers, Facilitators, Hobbyists, Experience seekers, Rechargers (Falk and Storksdieck, 2010). Recollections of exhibits highlight personal curiosity, excitement, allowing faster and better learning, motivated by personal curiosity. Instead of disliking school for reading without application and witness in real life, free-choice learning offers realistic expectations over compulsory. Fascinating objects help crystallize meaning to pursue learning satisfaction without external validation, where genuine openness to learn immersed within setting minimizes performance mentality.
Butler, D. M., & MacGregor, I. D. (2003). globe: Science and education. Journal of Geoscience Education, 51(1), 9-20. doi:10.5408/1089-9995-51.1.9
Driver, R., Asoko, H., Leach, J., Mortimer, E., & Scott, P. (1994). Constructing scientific knowledge in the classroom. Educational Researcher, 23(7), 5-12.
Falk, J. H., & Storksdieck, M. (2010). Science learning in a leisure setting. Journal of Research in Science Teaching, 47(2), 194-212.
Srinivasan, S., Pérez, L. C., Palmer, R. D., Brooks, D. W., Wilson, K., & Fowler, D. (2006). Reality versus simulation. Journal of Science Education and Technology, 15(2), 137-141.
Cognition is embodied in physical activity interacting within contextual situations, constructing knowledge instead of traditional encoding, remembering and recalling. Learning incorporates cerebral with bodily, negotiating meaning through social activity externalizing thinking as behavior. Reciprocal interaction between mind and environment offers differing perspectives, for example interpreting 3D space using bodies as data points. Our brains map objects to representations, where mental reasoning depends on factors like economic status, ethnicity, family support, teacher quality and school preparation (Winn, 2003). Biological adaptation transforms pictures into associative networks dynamically interacting sensory inputs. Bodily engagement like thought-gesture coproduction grounds perception and action with physical environment (Núñez, 2012), where experience develops structure towards higher level conceptual systems. Reality environment can differ from known umwelt (Winn, 2003) based on individual background, where multiple ways of knowing removes fixed objective standards to assess knowledge. Thinking is embodied and situated, testing hypotheses and explanations, where science uses methodical experimentation collecting empirical data to avoid reductionism. Science operationalizes cognitive mechanisms, using speech, gesture, eye and thought synchronization to make introspections of observations.
Technology reduces physical limits enabling metaphorical representations out of reach from direct experience, instrumenting tools to observe phenomena and make inferences. Explanations are limited by natural environment viewing from particular time space scales. Metaphors however risk misinterpretation or incomplete understanding, for example distorting time whose simplification results in misconception (Winn, 2003). Complete attention to immersion may produce flow as total engagement and enjoyment losing track of time, while reduced presence caused by distraction or discomfort impedes learning. Aleahmad and Slotta (2002) used handheld technologies with browser-based learning environments to scaffold data collection and reflection activities. WISE Inquiry maps coordinated activities, embedding pop-up notes or hints. Convenient portable data creation makes content dynamic with syncing, where beaming checklists have ‘cool’ factor maximizing take-home learning through social networks. Learners select criteria for wider audiences, evaluating sources when synthesizing observations.
During teacher education, a particular lesson involved volunteers lining up holding papers containing numbers and a decimal point. As students reorganized themselves along the line, they embodied significant figures in Chemistry. However the extent of learning content between observers and participants may vary, having difficulty translating activity back into traditional media. Educators persuade students to challenge uncertainty with difficulty embedded in constructive activity towards intuitive explanations. Optimal challenge elicits curiosity to adapt embodied interactions to affect deeply-rooted belief and genetic predisposition to change. Instruction proves effective only when employed at appropriate levels of granularity. Embeddedness employs interdependence between cognition and environment, dynamically interacting to regularly process learning.
Aleahmad, T. & Slotta, J. (2002). Integrating handheld Technology and web-based science activities: New educational opportunities. Paper presented at ED-MEDIA 2002 World Conference on Educational Multimedia, Hypermedia & Telecommunications. Proceedings (14th, Denver, Colorado, June 24-29, 2002); see IR 021 687.
Núñez, R. (2012). On the science of embodied cognition in the 2010s: Research questions, appropriate reductionism, and testable explanations. Journal of the Learning Sciences, 21(2), 324-336.
Winn, W. (2003). Learning in artificial environments: Embodiment, embeddedness, and dynamic adaptation. Technology, Instruction, Cognition and Learning, 1(1), 87-114.
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|
|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.
A challenging Chemistry concept is explaining periodic trends, connecting related concepts of radii, ionization energy, electronegativity, electron affinity and melting point. Although data booklets provide empirical values, learners not only fail to appreciate how much work is done by the scientific community over lengthy periods for each point (Khan, 2010), they resort to memorizing trends with little understanding. For example, on practice tests students explain why Fluorine has the highest ionization energy because it is most electronegative (both of which are effects of underlying principles of effective nuclear charge and radii). On explaining radii, they state Fluorine is smallest because atoms get smaller towards the top right corner of the periodic table. Possible misconceptions arise given the order of magnitude in picometers, abstractly described at sizes too small to visualize. Similarities in definitions between ionization energy and electron affinity (and later electronegativity) make learning trends challenging as students attempt to understand key concepts while expected to compare different elements given periodic table arrangement.
A possible T-GEM Cycle might be as follows:
Generate: Define atomic radii and ionization energy so learners have rough idea of what data represent
Evaluate: Present radii data for an individual row (ex. Li to Ne) asking students to find trend between radii and atomic number. A possible conclusions is that atomic radii decreases with more protons, graphing element radii versus atomic number.
Modify: Have students compare whether pattern works for other rows on the periodic table (ex. Na to Ar). Identify discrepancies like: Why is Na bigger than Ne (reviewing number of shells), and Why is Ne bigger than F (introducing electron repulsion).
Evaluate: Present ionization energies for individual periods, asking students to find trend between IE and atomic number. A possible conclusion is that ionization becomes harder with more protons, graphing IE versus atomic number.
Modify: Have students compare whether pattern works for other periods. Identify discrepancies like: Why does Na have lower ionization energy than Ne (reviewing number of shells), and Why is O’s electron harder to remove than N (introducing half filled p stability). Learners can extend trends comparing ionization against radii.
A possible technology contribution would be the ‘Periodic Table’ Chemland simulation: http://employees.oneonta.edu/viningwj/sims/periodic_table.html
Clicking ‘Relative Radius Covalent’, displays relative element radii as bar graphs arranged on the periodic table, using visuals to make sense of raw data. Learners can similarly click ‘Relative Energy First Ionization’ to test whether their discovered patterns are empirically consistent or whether theoretical models need to be reorganized.
*For my final TELE design, I am considering addressing similar concepts but primarily using Excel to graph data to make visual sense of patterns in the data booklet. Compiling information for atomic number, radii, first ionization, electron affinity, electronegativity and melting points, students can identify patterns across individual rows and columns, presenting discrepant events to have students iteratively refine models.
Khan, S. (2010). New pedagogies for teaching with computer simulations. Journal of Science Education and Technology, 20(3), 215-232.
Traditionally educators view content and process as having competing priorities, designing technology-supported inquiry to address memorization and recitation. Since classroom resources and time are scarce, evaluating Learning-for-Use (LfU) requires considerable risk and reward in pedagogical reform. Given a preliminary understanding of MyWorld, I can imagine teaching lessons on comparing geographical precipitation and energy balance in different biomes. Or for motion kinematics, measuring distances between cities and travel times enables contextual learning of average and instantaneous velocities. With albeit out-dated information, LfU compiles and visualizes actual data files, superimposing mapview layers to customize appearance based on range of interest. Students can make predictions before exploration, clicking underlay to graph entire/current selections, comparing data sets with parallel cursor movements, creating actual difference graphs averaging statistics using colour to define categories. Dynamically interacting real-world data mimics authentic science practice, operationalizing inert knowledge towards construction, forming connections towards accessible goals. Learners initiate reflection progressing incrementally given stepwise elaboration, creating appropriate indices to retrieve memory as useable knowledge.
MyWorld and Google Earth promote spatial thinking and geographic conceptions, producing environmental citizens that make sustainable decisions. Inquiry-based investigations of sea ice distribution and local weather phenomena are current, valid and essential for persistent understandings and multiple intelligences (Bodzin et al., 2014). Constructivist models enable cognitive flexibility, iteratively promoting teacher pedagogical content knowledge to accommodate differentiated learners. Construction does not invalidate reading, viewing and listening, actively making observations through personal experience and peer communication, applying sense-making to interact with the world. In particular, LfU frameworks purposely lack absolute solutions, asking learners to evaluate priorities where for example urban expansion results in vegetation loss, automobile dependence, along with diminished heat dissipation. Students interpret time-sequenced data to explore alternative energy sources and efficient practices to minimize environmental impact. LfU reveals misconceptions and deficits to promote innovation, achieving both scalability and portability engaging learners with motivating contexts personally relevant to daily lives. To minimize visualizations detracting from learners, teachers encourage understanding with embedded prompts to focus observations.
Traditional inform, verify and practice become transmission which does not acknowledge motivation and refinement. The LfU approach uses exploration, discovery and invention to build contextual interpretive framework, eliciting curiosity from direct experience reinforced by reflection (Edelson, 2001). Technology guided investigations pose violations of expectations developing authentic motivation to naturally apply knowledge, where situational interest articulates prior conceptions to activate existing knowledge. LfU grounds abstract understanding in concrete experience, providing simulations to participate in guided discovery focusing on accessibility and applicability when faced with demands and limitations. LfU addresses the content process dichotomy by combining effectiveness and efficiency in time-limited system.
Bodzin, A. M., Anastasio, D., & Kulo, V. (2014). Designing Google Earth activities for learning Earth and environmental science. In Teaching science and investigating environmental issues with geospatial technology (pp. 213-232). Springer Netherlands.
Edelson, D.C. (2001). Learning-for-use: A framework for the design of technology-supported inquiry activities. Journal of Research in Science Teaching,38(3), 355-385.
Upon exploring the WISE library, I customized the project ‘Graphing Stories (with motion probes)’, having done a similar activity during my practicum. The Authoring Tool is user-friendly and intuitive, adding activities and steps with the editor ‘refreshing as I type’ and preview directly beside. To supplement the project sequence, I designed my own activity that incorporates a PhET Simulation called Moving Man. Adding steps of different types enabled variety and progression, moving from ‘Brainstorm’ to ‘Table’ to ‘Annotator’ to ‘Survey’. Brainstorming differences between scalars and vectors reveal student preconceptions, which is enhanced by gating responses before seeing peer feedback, allowing students to reply anonymously in risk free environments. ‘Fill the Blank’ provides checkpoints before progressing further: Distance is to displacement as speed is to . I was surprised to find a step icon designated for PhET simulations, providing easy access linking through URL. Students can record their sample data in ‘Table’, visualizing points and making graphs to compare with simulations. ‘Reflection Notes’ can make students aware of their own thinking. The ‘Annotator’ step asks students to move the man back and forth, then upload a screenshot of the position-time graph for others to interpret. The editor can require predictions before entering, or more guidance with starter sentences. ‘Drawing’ allows freeform sketches, designing frames for stop motion animation. ‘Survey’ icon enables both multiple choice with shuffling, inline feedback and multiple correct functionality. Open responses can display answers, locking after submission and completion before progression.
*When using ‘Table’ to make graphs, upon assigning columns and rows toggling through U = uneditable for student, I get an error message ‘Data in table is invalid, please fix and try again’. Is anyone else having the same problem?
The Web-based Inquiry Science Environment (WISE) defines inquiry as engaging students with authentic science, providing flexibly adaptive curricula to intentionally shape learner repertoire. This includes diagnosing problems, critiquing experiments, planning investigations, researching alternatives, searching information, constructing models, communicating audiences and forming arguments (Linn et al., 2003). Responding to assumptions of learners holding multiple conflicting ideas, rather than constantly seeking teachers for guidance, embedded prompts offer assessment feedback and metacognitive critique at the right level, having been iteratively refined over time. Relative ease of customizing projects enhances relevance to match individual curriculum, using Scaffolded Knowledge Integration (SKI) based on premises: Making science visible and accessible, promoting lifelong learning through peer support (Linn et al. 2003). Accessibility is more than simplifying vocabulary which may actually reduce impact, but connects personal ideas with appropriate grain size. Presenting learners with compelling alternatives enables gradual fading in scaffolding for subsequent projects. Pivotal cases, evidence pages and inquiry maps bring concepts to life, transforming recipe into opportunity ascertaining connections to project. Making things visible involves more than assessment towards modelling wrong paths and debugging practices. Visual simulations at times confuse more than inform, but can direct attention towards zone of proximal development in supporting knowledge integration. Structured collaboration frames critical questions for group arguments, enabling anonymous contributions to reduce stereotypical responses, sustaining inquiry to evaluate validity of alternatives. Technology transforms canned tools towards autonomous inquiry, undergoing iterative refinement over mobile platforms. Handheld devices provide novel learning opportunities beaming information with teachers as facilitators becoming more expert at guiding inquiry.
Classroom practices shift over time employing instruction, experience and reflection to reorganize knowledge. Generating predictions reveal student preconceptions, using personally relevant examples designing hands-on investigations, exploring new representations and practices with capability to electronically respond (Williams et al., 2004). Integrating technology provides real opportunities to sustain interactions with different questioning types: logistical, factual and conceptual. Learners are encouraged to challenge perspectives, solve problems, learning through self-discovery becoming independent thinkers. Teaching is contrasted with telling, providing inquiry orientation that values student opinion, refocusing attention to integrate knowledge and interpret conceptions. With repeated opportunities to reorganize prior ideas, learners support claims with evidence, revealing misconceptions and growing familiarity to figure out alone in small groups. Guided inquiry selectively holds back answers encouraging student-directed engagement as practicing scientists. Students learn by doing and understand better finding (Furtak, 2006). The pedagogy is amorphous between direct traditional and open inquiry, having students rediscover supposed predetermined and pre-existing knowledge. Questions like whether correct answers exist may compel students to explore phenomena or give up. Teachers can deliberately create uncertainty, rationalizing constructivist perspective, avoiding expected results, deferring to later. With false I don’t know, in-school socialization helps students not come to seek answers, being comfortable sharing perspectives, predicting, voting and experimenting to analyze unexpected challenges.
Furtak, E. M. (2006). The problem with answers: An exploration of guided scientific inquiry teaching. Science Education, 90(3), 453-467. doi:10.1002/sce.20130
Linn, M. C., Clark, D., & Slotta, J. D. (2003). WISE design for knowledge integration. Science Education, 87(4), 517-538. doi:10.1002/sce.10086
Williams, M., Linn, M. C., Ammon, P., & Gearhart, M. (2004). Learning to teach inquiry science in a technology-based environment: A case study. Journal of Science Education and Technology, 13(2), 189-206. doi:10.1023/B:JOST.0000031258.17257.48
The Jasper materials respond to passivity in learning working through canned problems towards correct solutions isolated from context. Jasper incorporates video-based instruction providing generative activities and cooperative situations, whose supporting theoretical framework is that of anchored instruction, where teaching is contextualized in problem-rich environments that engage sustained exploration (Cognition and Technology Group at Vanderbilt, 1992a). While traditional questioning selects important data beforehand, numbers presented during clips might be pertinent, asking learners to differentiate what is meaningful for the problem, categorizing and prioritizing optimal solutions. Rather than isolating content, Jasper components are posed in realistic context to promote independent thinkers that can identify personal issues, moving learners from passive inert knowledge to constructivist exploration of misconceptions. Such problem-based learning enables self-generated information that is better remembered than transmitted knowledge. Unlike Khan Academy, videos can be segmented to make complexity manageable, employing media platform to accommodate poor readers with realistic narratives, enhancing opportunities to develop confidence, generating sub-problems to extend exploration. Video disc format enables controlled access, searching back and freeze-frame, to identify additional relevant data. With multiple rescue solutions possible, students communicate reasoning cooperatively, posing analog problems to master skills in context. Rather than errorless learning, authentic problems offer frameworks to correct preconceptions, whose real-world complexity produces motivation to create inquiry communities for improved interaction.
Specifics from the videos: Trip planning requires multiple considerations of fuel consumption, tank capacity and wind speeds, where headwind definitions are introduced in context using easy numbers for computation. Communication employs reasoning using analogies to change perspectives while describing historical artifacts of real people. Learners consider whether the most popular solution is always the best, which varies between schools and classes given priorities. Considering other variables like expenses, risk and time prompts students to rework plans to iteratively make them better. Solutions pose new ideas for challenges, sifting through information to decide what factors are important.
Groups work through problems on a just-in-time basis, watching videos without knowing what problems need solving, evaluating variables for multiple plans. Moving from stone-age designs to related adventures extends canned problems using what-if questioning (Biswas et al., 2001). Coaches scaffold guided discovery in exploratory setting, varying interaction styles to create learning opportunities, generalizing smart tools between problems. Learning occurs during teaching as well, doing reverse mentoring where tutors learn alongside tutees. When given the possibility for self-explanation, students tend to understand content better while preparing to teach. Differing experiences generate different questions, collaboratively working through multiple paths with conflicting solutions. Rather than viewing learning as fact accumulation, inert associations can be integrated constructively to consider multiple solution possibilities. Students learn through social activity to evaluate alternatives, whose problem-solving space improves performance, employing thinking-aloud during solving process in making cooperative solutions (Vye et al., 1997).
Biswas, G. Schwartz, D. Bransford, J. & The Teachable Agent Group at Vanderbilt (TAG-V) (2001). Technology support for complex problem solving: From SAD environments to AI. In K.D. Forbus and P.J. Feltovich (Eds.)Smart Machines in Education: The Coming Revolution in Education Technology. AAAI/MIT Press, Menlo, Park, CA. [Retrieved October 22, 2012, from: http://www.vuse.vanderbilt.edu/~biswas/Research/ile/papers/sad01/sad01.html
Cognition and Technology Group at Vanderbilt (1992a). The Jasper experiment: An exploration of issues in learning and instructional design. Educational Technology, Research and Development, 40(1), 65-80.
Vye, Nancy J.; Goldman, Susan R.; Voss, James F.; Hmelo, Cindy; Williams, Susan (1997). Complex mathematical problem solving by individuals and dyads. Cognition and Instruction, 15(4), 435-450.
Shulman (1986) described how qualification and eligibility tests historically revolved around basic content like reading, writing, arithmetic skills, needing teachers to demonstrate subject matter knowledge before teaching. However mere knowledge does not guarantee effective instruction, requiring interplay between content and pedagogy. The pendulum swings back and forth regarding how new knowledge is acquired, with implications for classroom management, organizing activities, planning lessons and judging understanding among others. Content is represented in different ways to accommodate students, with teachers asking questions at various Bloom taxonomy levels, probing alternative views to accumulating wisdom of practice. Shulman (1987) highlights an issue that teaching is conducted without a history of practice or audience of peers. Though learning ultimately remains student responsibility, educators design teaching for comprehension, reasoning, transformation and reflection so unknowing comes to articulate what they know. Transformation involves preparation, selection, adaptation within instruction and evaluation, bridging comprehension and thinking for students through lecture and demonstration towards cooperative learning and reciprocal teaching. Learners work through misconceptions and expectations, reasoning through discussion cycling seamlessly between phases.
An example of PCK that comes to mind is teaching basic motion concepts, going beyond reading definitions of displacement, velocity and acceleration in textbooks, to comparing and contrasting scalars and vectors, making everyday connections to speedometers and marketing to help audiences build upon previous knowledge. The significance in distinguishing quantities that have magnitude with and without direction, needs to convince learners why they cannot maintain pre-existing beliefs when confronted with contradictions. Science naturally has self-corrective features in building increasingly complex models to explain observations and make predictions. Labs can be teacher-directed or student-centred to investigate terminal velocity and ramp height for example. Technology comes in when motion sensors are utilized to construct position-time and velocity-time graphs in real time, learning how to interpret and change between graphs. Simulations like PhET Moving Man can be used to introduce, teach or reinforce concept knowledge.
Shulman, L.S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4 -14.
Shulman, L.S. (1987). Knowledge and teaching. The foundations of a new reform. Harvard Educational Review, 57(1)1-23.