Learning & Teaching with Technology Claire O’Malley School of Psychology

2 Outline Why use ICT? A brief history of ICT and learning Some approaches to learning Implications for teaching Applications to learning technologies

3 Why use ICT? Elaborates other teaching material –e.g., lectures; practicals Personalised learning –Students can learn at their own pace, in their own style Computational offloading –Computers can take care of routine stuff while students focus on the more important stuff Cognitive augmentation –Can provide learning experiences not possible by other means Others….?

4 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) ‘00s – Mobile and Ubiquitous Learning

5 Computer Assisted Learning CALComputer Assisted Learning CAIComputer Assisted Instruction CBTComputer Based Training CBLComputer Based Learning (Etc.)

6 Computer Assisted Learning “Programmed learning” Learning theory –Behaviourism & reinforcement –Associationism Learning activities –Drill-and-practice –Present-test-feedback Instructional theory –Transmission model of instruction

7 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: how do you know why the learner has made a mistake? –No theory of the learner’s knowledge

8 Intelligent Tutoring Systems ITSIntelligent Tutoring Systems ICAIIntelligent Computer Assisted Instruction AI EdArtificial Intelligence in Education (Etc.)

9 Intelligent Tutoring Systems Domain representation (what to teach) Student model (what the student knows) Teaching strategy (how to teach)

10 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!)

11 Intelligent Tutoring Systems Advantages –Explicit theory of learner’s knowledge Issues –Requires very detailed models of domain & learner –The credit assignment problem remains...

12 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

13 Essential elements ILS –Curriculum content –Record system –Management system ITS –Domain representation –Student model –Teaching strategy Functionality –Update student records –Interpret learner’s responses –Provide performance feedback to learner and teacher

14 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

15 Interactive Learning Environments Example –Papert’s LOGO system (1980) REPEAT 4 [FORWARD 90 RIGHT 90]

16 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

17 Interactive Learning Environments Advantages –Tools to think with rather than information transmission Issues –Instructional transfer –‘LOGO-as-Latin’

18 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

19 Multiple representations

20 Multiple representations

21 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

22 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

23 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’

24 Implications 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

25 References Ainsworth, S.E., (1999) A functional taxonomy of multiple representations. Computers and Education, 33(2/3), 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),