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Lei Bao P hysics E ducation R esearch G roup Department of Physics The O hio S tate U niversity

Context Cues and Learning: A virtual environment platform for education research and practice. Lei Bao P hysics E ducation R esearch G roup Department of Physics The O hio S tate U niversity. About this Research.

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Lei Bao P hysics E ducation R esearch G roup Department of Physics The O hio S tate U niversity

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  1. Context Cues and Learning: A virtual environment platform for education research and practice Lei Bao PhysicsEducationResearchGroup Department of Physics The Ohio State University

  2. About this Research • A basic study on context dependence of learning: how low-level sensory cues from contexts affect the learning process? • Theoretical development of measurement methods and representations to study the issue of context dependency of learning. • Experimental research using Virtual Environment as a platform to investigate the interactive relations between context cues and learning processes. • Applications of research in education practice.

  3. Areas of Studies • Conduct systematic qualitative research to identify and investigate specific context cues (especially sensory cues) of physics examples that are important to the students’ learning of the physics concepts. • Develop methods to measure, assess and represent the involvement of context cues in different stages of learning. • Using the developed representation, study how the identified context cues affect learning as well as how students’ previous knowledge affects their interpretations of context cues. • Develop Virtual Environment physics activities as a platform to investigate experimentally how low-level sensory cues such as haptic cues affect learning and how such cues may be addressed in instruction.

  4. Cognitive Associations: Implications from Psychology, and Neural Science • Association is one common function of neural systems • Associative network (memory developing and retrieving); Pattern associative network (linking different pieces of knowledge) • Context stimuli are automatically integrated together to form episodic memories of a context scenarios. • There are fundamental uncertainties in cognitive processes.

  5. Context Cues and Knowledge Development A context setting often contains multiple cues that can each activate associations with different pieces of knowledge. In current instruction: • Only abstract cues of physical representations used by the instructors are seriously addressed in class: • Implicit on the actual sensible and appropriate interpretations. • Often discussed in isolated situations. • Students only get fragmented pieces rather than a integrative understanding. • Cues in real life experience are often ignored: • Visual cues of a moving object. • Auditory cues associated with moving/vibrating objects. • Haptic cues of pushing an object.

  6. Problems of Disintegrated Context Cues in Knowledge Development – Fragmentation of Knowledge

  7. Integrating Multiple Context Cues in Learning to Develop Coherent Knowledge

  8. An Experiment Using Virtual Environment Physics Activities as a Platform for Research and Instruction Advantages of using a virtual environment • Flexible control of multiple context cues which are otherwise difficult to be manipulated in real life situations: • Human response time: Something happens too fast such as a collision now can be interacted in slow motion to allow enough time for students to process the sensory information and make appropriate associations. • Physical limits: Some experiments require a physical dimension or quantity difficult to realize, e.g., make a cart moving at an easily observable constant acceleration for some time often requires a large space and special equipment. • Seamless integration of multiple representations used in instruction: We can easily put arrows and numbers on the object to represent velocities, forces, accelerations, etc. The coexistence of multiple representations can significantly enhance the development of an integrated understanding. • Open environment for self-motivated explorations and investigative learning. • Convenience and availability (eventually low cost) to students making it possible to learn at “anytime anywhere”.

  9. Context Cues Addressed in Current VE Activities • Visual Cues: • Visual stimuli of motion • Integrated multiple numerical and graphical representations • Sceneries resembling real world situations • Auditory Cues: • Sound reflecting motion on different surfaces, velocity and acceleration (in case of a motor) • Haptic Cues: • Feeling of forces exerted on an object from different directions and magnitudes: being “hit”, vibration, etc. • Maintaining a force in different directions and magnitudes: pushing/dragging an object with and without friction.

  10. Virtual Environment Examples • Circular Motion: Interactive associations between the feelings and controls of the magnitudes and directions of forces and the visual feedback of the real-time velocity and trajectory of the motion.

  11. 1-D Linear Motion: Major “Misconception” – “Motion indicates force”.Major “Hidden Misunderstanding” – Friction force. • Interactive associations between the feelings and controls of the magnitudes and directions of the pushingforce on an object and the visual feedback of the real-time velocity of the motion as a result of the pushing force and a modifiable frictional force.  A force sensor and feedback device currently under development. The VE activity for 1-D linear motion 

  12. Current Research Efforts • Conduct case studies using VE activities. • Integrate qualitative and quantitative measurements based on Model Analysis. • Apply the research methods to develop a knowledge base on the interactive features of low-level context cues in learning. • Apply the research results to develop examples of instructional interventions using VE activities to address certain destructive effects of low-level context cues. For more information about this research and potential collaborations in developing and using VE activities in instruction and research, please visit: www.modelanalysis.net.

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