Learning & Teaching with Technology Claire O’Malley School of Psychology.
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Transcript of Learning & Teaching with Technology Claire O’Malley School of Psychology.
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Learning & Teaching with Technology
Claire O’Malley
School of Psychology
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Outline
• Why use IT?
• A brief history of IT and learning
• Some approaches to learning
• Implications for teaching
• Applications to learning technologies
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Why use IT?• Elaborates other teaching material
– e.g., lectures; practicals
• Students can learn at their own pace• Computers can take care of the routine stuff while
students focus on the more important stuff• Can provide learning experiences not possible
with other means• Others….?
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Paradigms in Educational Computing
‘60s – Computer Assisted Learning (CAL)
‘70s – Intelligent Tutoring Systems (ITS)
‘80s – Interactive Learning Environments (ILEs)
‘90s – Computer Supported Collaborative Learning (CSCL)
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Computer Assisted Learning
CAL Computer Assisted Learning
CAI Computer Assisted Instruction
CBT Computer Based Training
CBL Computer Based Learning
(Etc.)
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Computer Assisted Learning• “Programmed learning”• Learning theory
– Behaviourism & reinforcement
– Associationism
• Learning activities– Drill-and-practice
– Present-test-feedback
• Instructional theory– Transmission model of instruction
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Computer Assisted Learning
• Advantages– Instruction adapted to individual needs
• Issues– How to give the right feedback at the right time– How to diagnose ‘errors’
• The ‘credit assignment’ problem
– No theory of the learner’s knowledge
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Intelligent Tutoring Systems
ITS Intelligent Tutoring Systems
ICAI Intelligent Computer Assisted Instruction
AI Ed Artificial Intelligence in Education
(Etc.)
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Intelligent Tutoring Systems
Domain representation(what to teach)
Student model(what the student knows)
Teaching strategy(how to teach)
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Intelligent Tutoring Systems
• Adaptive control of teaching• Learning theory
– Representational change
• Learning activities– Goal directed problem solving– Skill acquisition (drill-and-practice)
• Instructional theory– Transmission model (but adaptive!)
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Intelligent Tutoring Systems
• Advantages– Explicit theory of learner’s knowledge
• Issues– Requires very detailed models of domain &
learner– The credit assignment problem remains...
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Integrated Learning Systems (ILS)
• Originally developed in USA (Patrick Suppes, Stanford, 1970s)
• Modern version– E.g., RM’s SuccessMaker– a system that includes extensive courseware
plus management software usually running on a networked system
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Essential elements
• ILS– Curriculum content
– Record system
– Management system
• ITS– Domain representation
– Student model
– Teaching strategy
FunctionalityUpdate student recordsInterpret learner’s responsesProvide performance feedback to learner and teacher
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Interactive Learning Environments
• Simulations, microworlds, spreadsheets, etc.
• Learning theory– Learning is best achieved when learners
actively construct their own knowledge
• Learning activities– Discovery learning, experiential learning, etc.– Instructional theory
• Learner-as-tutor
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Interactive Learning Environments
• Example– Papert’s LOGO system (1980)
REPEAT 4[FORWARD 90
RIGHT 90]
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Papert’s ‘Powerful Ideas’• Making thinking explicit
• Making reasoning and its consequences ‘visible’
• Fostering effective problem solving & planning skills
• Learning to learn from errors – ‘debugging’ skills
• Developing reflective metacognitive skills
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Interactive Learning Environments
• Advantages– Tools to think with rather than information
transmission
• Issues– Instructional transfer– ‘LOGO-as-Latin’
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Computer Based Representations• Routine computations can be off-loaded • Can focus learners’ attention on the essentials of the
domain• Computer based notational systems may capture
procedures or abstract structure in perceptually concrete ways
• Representations can be placed under active control • Interactive manipulation may help learners construct their
own understanding of a domain• Screen based representation can be more easily shared
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Multiple representations
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Multiple representations
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Benefits of Graphical Representations
• Reducing effort needed for search and recognition • Transformation of the problem space • Can support powerful perceptual inferences • Often have emergent features that make implicit
information explicit• Experts have more highly structured and principled
representations than novices
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Multiple Representations
• Support different ideas/processes• Can promote deeper understanding
– Common invariants allow learners to construct abstractions
– Representations at different levels of abstraction• But learners need support in mappings
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Computer Supported Collaborative Learning• Groupwork, peer tutoring, computer-
mediated communication• Learning theory
– Socio-cultural context of learning
• Learning activities– Knowledge building communities
• Instructional theory– Apprenticeship; ‘legitimate peripheral
participation’
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Implications for LearningImplications for Learning• Learning occurs most effectively in
situations resembling those of eventual practice
• Learning should involve "legitimate peripheral participation" in communities of practice (Lave & Wenger, 1991)
• Learning occurs when the learner is confronted with a "problematic" situation
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References
• Ainsworth, S.E., (1999) A functional taxonomy of multiple representations. Computers and Education, 33(2/3), 131-152.
• Koschmann, T. (1996) CSCL: Theory and Practice of a New Paradigm. Erlbaum. (Chapter1)
• Papert, S. (1980) Mindstorms: Children, Computers and Powerful Ideas. Basic Books.
• Wood, D. & Underwood, J. (1999) Integrated learning systems in the classroom. Computers & Education, 33 (2/3), 91-108.