A short introduction to ICT in special education

1. A short introduction to ICT in special education Pekka Räsänen researcher, clinical neuropsychologist Niilo Mäki Institute, Jyväskylä 29.11.2010

2. Who’s talking? An impressionistic self-portrait, 2007

3. PR Publications 2008– • Developmental and Educational Psychology • Räsänen, P., Närhi, V., & Aunio, P. (2010). [Students with a poor performance in mathematics at the beginning of the 6th grade, in EK Niemi & J Metsämuuronen (eds.) National learning outcome assessment, follow-up from third to sixth grade. National Board of Education] • Wilson, A., & Räsänen, P. (2009). Effective interventions for numeracy difficulties/disorders. In Encyclopedia of Language and Literacy Development. The Canadian Language and Literacy Research Network. http://www.literacyencyclopedia.ca/ • Räsänen, P. (2008). Supporting Literacy and Numeracy Development. A presentation at the Workshop of the Foresight project on Mental Capital & Wellbeing. Feb 29th 2008, Royal Society, London, UK. • Neuropsychology • Price, G.R., Holloway, I.D., Räsänen, P., Vesterinen, M., & Ansari, D. (2008) Impaired parietal magnitude processing in Developmental Dyscalculia. Current Biology. Ref. • Computer science and technology • Räsänen, P. , Maslova, U., & Maslov, O. (submitted). A multidimensional adaptive system for web-based computer assisted learning: A model and an implementation. • Räsänen, P., Salminen, J., Wilson, A., Aunio, P., & Dehaene, S. (2009). Computer-assisted intervention for children with low numeracy skills. Cognitive Development, 24, 450–472. -ref. • Yevseyeva, I., Miettinen, K., & Räsänen, P. (2008). Verbal ordinal classification with multicriteria decision aiding. European Journal of Operational Research. Ref. • Tests and rehabilitation materials • Mattinen, A., Räsänen, P., Hannula, M.M., & Lehtinen, E. (2008, 2010). [A program for supporting early learning prerequisities in mathematics]. NMI-Bulletin, 4, 40–53. • Aunio, P., & Räsänen, P., & Van Luit, J. E. H., (2010) [I count! A remedial programme for children’s number sense] . NMI. • Poutiainen, E., Kalska, Laasonen, Närhi, & Räsänen, P. (2010). [Trail Making – test. Manual]. Psychologist’s Publisher Corp.. • Räsänen, P., Linnanmäki, K., Haapamäki, C., & Skagersten, D. (2008). RMAT – [A mathematical achievement test for ages 9–12 in Finnish-Swedish]. NMI. • For a more complete list, see http://users.jyu.fi/~prasanen/

4. ICT in early education ? • In the 1980s there was vigorous debate about the role of technology, and computers in particular, in early childhood curriculum (Barnes & Hill, 1983; Cuffaro, 1984). Some early childhood educators were of the opinion that young children should not use computers because they: • were too abstract and only let children experience ideas/ concepts in two dimensions; • minimized the role of teachers; • did not assist children to work collaboratively; and • were used with programs that were considered to be developmentally inappropriate. • Critic is not supported with empirical data (Clements & Sarama, 2003) • No questions are asked about text books for young children, which often illustrate the same qualities of two-dimensionality, rote learning and abstract contents 4

5. Structure of the presentation • A short history of CAI • ICT in SEN • Defining ICT in SEN • Effectiveness of ICT in SEN • Case study: • LukiMat -a national special educational web-based support system on learning disabilities in early education 5

6. History of Computer Assisted Instruction: quickly and inaccurately 6

7. CAL - CAI - CAMI • CAL = Computer Assisted Learning • CAI = Computer Assisted Instruction • Adaptivity: The computer program monitors the user’s reactions in the task and changes it’s behavior to support user’s performance and learning (e.g. IRT) • Typical CAL/CAI methods • visualisation and other illustrations • supporting/intelligent agents (e.g. MS-Win Paperclip) • feedback 7

8. Kempelen's chess playing Turk (1768) “Intelligent Machines” - Faked Chess 8

9. First steps in CAI • Skinner, H. (1866). Teaching spelling. Washington, DC: United States Patent Office; No. 52,758) • A pictorial scroll at the top of the apparatus was moved by a hand crank, successively exposing a series of pictures (e.g., the horse shown in the figure), thus providing the unit of information. The task of the student was to spell an appropriate word or words, for example "my horse." Words of up to eight letters in length could be spelled by pressing the keys on the front of the apparatus. Those keys provided the response requirement of the definition, and they moved the eight interior letter wheels, each of which contained the 26 alphabet characters and a blank space. (However, the machine did not give any feedback about the correctness of the response.) • Altman, G. G. (1897). Apparatus for teaching arithmetic. Washington, DC: United States Patent Office; No. 588,371) • self-controlling, provided a means for the learner to respond, and provided feedback about the correctness of the response • Aikins, H. A. (1913). Educational appliance. Washington, DC: United States Patent Office, No. 1,050,327) • "arithmetic, reading, spelling, foreign languages, history, geography, literature or any other subject in which questions can be asked in such a way as to demand a definite form of words ... letters ... or symbols" 9

10. First techologies • 1920s Sidney Pressey • an educational psychology professor at Ohio State University • developed mechanical machines for drill-and-practise (typewriter input) • “Lift from her [the teacher's] shoulders as much as possible of this burden and make her free for those inspirational and thought-stimulating activities which are, presumably, the real function of the teacher.” Pressey, S.L. (1926). A simple apparatus which gives tests and scores - and teaches. School and Society, 23 (586), 373-376. 10

11. 1950’s: Teaching Machines • The IBM 650, a high-speed digital computer, interfaced with a typewriter, was used as a teaching machine. This configuration was known as the "IBM 650 Inquiry Station." • The IBM Inquiry Station: The program of instructions in the computer presents the problem to the student by way of the typewriter. The student, in turn, types his answers, which is transmitted to the computer for checking. • IBM also developed a program called COURSEWRITER, the first computer language devoted to CAI programming. • Skinner, B. F. (1958). Teaching machines. Science, 128, 969-977. 11

12. 1960’s: Time of optimism • the mid 1960s there were thousands of available programs across a wide variety of school subjects • 1963 Stanford University's Institute for Mathematical Studies in the Social Sciences (IMSSS), started to develop the first comprehensive CAI elementary school curriculum • implemented on a large scale in schools in California and Mississippi. • used the IBM 1500 • computerized drill and practice lessons in an attempt to increase children's skill levels in reading and mathematics. • “In a few more years, millions of school children will have access to...the personal services of a tutor as well-informed and as responsive as Aristotle.” Patrick Suppes, 1966, director, IMSSS 12

13. 1970’s: First PCs • Pong by Atari, 1971, the first working computer game • Microcomputers: Altair, 1975; Apple II, 1976, • was quickly assimilated into schools and colleges and was the basis of many early "microprocessor" courses. That same year the Microsoft and Apple Corporations founded. • Projects “PLATO” in 1961 and “TICCIT” in 1969.  • Drill and behaviourist based CAI programs.  • Problems encountered: technical problems, availability of CAI in the school setting, lack of quality software, teacher inexperience and cost of programs. • 1978 First issue of the Journal of Special Education Technology is published 13

14. 1980’s: Graphical GUIs • 1981: Time Magazine named the computer as the man of the year • IBM PC, Amiga Commodore, Apple Macintosh • Hypercard(1986 Apple “Special education brochure”) • Books: • Behrmann, M. (1988), Integrating computers into the curriculum: A handbook for special educators. • Russell, Corwin, Mokros, & Kapisovsky (1989). Beyond drill and practice: Expanding the computer mainstream. 14

15. 1990’s: Internet • hypertext, Mosaic 1994, Netscape 1995 • gif-animations, java-script, java, Flash • exponential growth of “learning-help” web-sites • netexperimentation • 1997 the first internet-mediated java-application for assessment of math disabilities in children (Räsänen, Ylinen & Kantola: http://www.nmi.jyu.fi/arites) • 1998 A special issue on “The World Wide Web and Special Education” in Teaching Exceptional Children, 30(5), was simultaneously published on the web at: http://www.cec.sped.org/bk/tec-jour.htm 15

16. 2000 -> • boom of the mobile computing, everybody is ”online 24/7” • networked and cloud computing • Google and Google Apps • game industry leads the progress (e.g. Call of Duty: Black Ops, released in Nov 9th 2010: sold 650 mUSD in five days, had 2,6 miljon multiplayers during the first day) • Intelligent computer-assisted instruction (ICAI, also known as intelligent tutoring systems or ITSs) • older CAI modeled teacher’s actions, ITS tries to model learning process • truly intelligent systems can present problems based on models of the student's knowledge, to solve problems themselves, and to diagnose and explain student capabilities (see.a model e.g. Vasilyeva, I., Puuronen, S., Pechenizkiy, M., & Räsänen, P. (2007). Feedback Adaptation in Web-based Learning Systems. International Journal of Continuous Engineering Education and Life-Long Learning. 17(4/5), 337 – 357.) • Affordable technologies • low-end smart phones • OLPC = One laptop per child • Electronic books (1st generation: Kindle, iPad, Android, Win7 and Meego tablets) • electronic school books -> embedded CAI into school books 16

17. Subgroups of SEN

21. Subgroups of students (%) with SEN in Finnish and Chech primary education Source: Eurostat 21

22. Subgroups of SEN • Three main groups • Children with specific deficits in perception, motor or general cognitive skills • small groups with clearly less than 1% prevalence • Children with average or low average general skills, but specific difficulties in language and/or basic academic skills (reading, writing, arithmetic) • large group, depending on criteria 5–15% • Children with difficulties in emotion or behaviour • severe cases form a small group (less than 1%), difficult to estimate the prevalence of mild cases due to differences between countries and their clinical and diagnostic practices 22

23. ICT in SEN:Defining ICT in SEN • in a broad sense ICT in SEN can be categorised as • a learning AID • a TARGET for learning • a TOOL for learning

24. ICT as a learning AID • Most typical target population: • persons with severe deficits in perception and communication • specific techologies to improve perception and communication • general ICT techologies to aid perception and communication 24

25. Specific technologies to AID perception and communication • Technologies to improve hearing • coclear implant, Bone Anchored Hearing Aid, Middle Ear Implant • a common procedure • Technologies to improve vision • retinal implants • Bionic vision • early experiments going on • Typically connected more to the medical and neuropsychological research than to educational sciences 25

26. General technologies to support perception, communication and learning • Technologies to support hearing impaired • text processors, email, SMS • visual communication tools for sign language • usage of multimedia for illustrations and production • Technologies to support vision impaired • audiobooks • computers to increase font and image size for students with low-vision • text to speech -applications 26

27. ICT as a TARGET for learning • a rarely raised question in early education and especially SEN education with young children • most common and simple form: typewriting skills • problem: rapid development in ICT technologies • computers: about 2 years life cycle • mobile phones: about 8 months life cycle • children with SEN are typically slow learners, therefore the rapid changes in ICT interfaces and logic of usage produces extra demands for them • applications developed for children with SEN and younger children usually have stripped-down interfaces and functionalities, and therefore require less training than technologies targeted for everyday use (e.g. mobile phones). 27

28. ICT as a TOOL for learning • The most typical target of research on ICT in education • Educational applications (CAI) • ICT tools in education • distance education / video-congressing • information search / web • using/having computers in the classroom • interactive presentation tools / intelligent blackboard • teachers’ skills and attitudes towards using ICT in education 28

29. Research results and effectivenessWhat do we really know about CAI ?

30. Research: A long history of mixed results • McKeown, E. N. (1965). A comparison of the teaching of arithmetic in grade four by teaching machine, programmed booklet, and traditional methods. Ontario Journal of Educational Research, 7, 289-295. • -> No differences between methods • Lin, A. & al. (1994) CAI and the Development of Automaticity in Mathematics Skills in Students with and without Mild Mental Handicaps. Computers in the Schools, 11(1), 43-58. • -> CAI + extended practise enhance learning • Nicol, M.M. & Anderson, A. (2000). Computer-assisted vs. teacher-directed teaching of numeracy in adults. Journal of Computer Assisted Learning, 16, 184–192. • -> no differences, a non-significant “trend” towards CAI • Lack of controlled studies 30

31. Edyburn, D.L. (2004). 2003 in review: A synthesis of the special education technology literature. Journal of Special Education Technology, 19(4), 57-80. 31

32. Measuring effectiveness of special educational interventions • A most typical way of measuring effectiveness of interventions (e.g. educational interventions) is to use standardised Effect Size (ES) • with a standardised ES different types of interventions can be compared • in ES the amount of change is compared to the variance of the phenomenon in the sample (or population) • the most common metric is Cohen’s d (M2 – M1/SD) • a rule of thumb in effect size estimates: • d > .8 (large), d ≈ .5 (medium), d < .2 (small) 32

33. General effects of • school... • Do we know that ? • street kids • handicapped children without schooling • drop-outs • and • - between schools differences ->> School itself is a HUGH intervention 33

34. The results from a mega-meta-analysisLipsey,M.W. & Wilson,D.B.(1993)The efficacy of psychological, educational, and behavioral treatment: Confirmation from meta-analysis. American Psychologist, 48(12), 1181-1209

35. Kavale, K.A. (2007). Quantitative Research Synthesis: Meta-Analysis of Research on Meeting Special Educational Needs. In Lani Florian (Ed.) Handbook of special education. Sage Publications. 35

36. Kulik, J.A. (1994). Meta-analytic studies of findings on computer-based instruction. In Baker & O’Neil (eds.) Technology assessment in education and training. NJ: Lawrence Erlbaum. Average ES of CAI in elementary schools was .32 (SD=.39). This means about 3 months gain due to CAI. 36

37. Räsänen, P., Salminen, J., Wilson, A., Aunio, P., & Dehaene, S. (2009). Computer-assisted intervention for children with low numeracy skills. Cognitive Development, 24, 450–472. Effect sizes for CAI on early numbers skills (6–9y) on different types of interventions in groups of children with SEN 37

38. Summary of research results • ICT as an AID • Specific aids • clear improvements in perception and communication • concerns only very small proportion of population • General aids • opens access for learning • ICT as a TARGET • not well studied in the population of young children with SEN • ICT as a TOOL • concerns a large proportion of population • Effect sizes of CAI have not changed from 60s even though there has been an extensive development in technology • ES varies from as low as .3 (small effect) to .9 (large) • a lot of research and development work going on 38

39. Reminder: ES and ROI • Effect size is not the same thing as the return on investment (ROI) of psychological and educational interventions • however, if we do have estimates of ES, the cost-benefit ratios and break-even point effect sizes can be calculated a-priori • see e.g. Wittmann,W.W., Nübling, R. & Schmidt, J. (2002) Evaluationsforschung und Programmevaluation im Gesundheitswesen. Zeitschrift für Evaluation,1. 39

40. Case study:LukiMat -a national special educational web-based support system on learning disabilities in early education An example about the ongoing research and development work in the Niilo Mäki Institute

41. Niilo Mäki Institute • a foundation based research and development centre in Central Finland • Aim: to prevent exclusion from learning by • research & development • clinical work (diagnostics and rehabilitation) • training and publishing • the largest research, training and publication unit on learning disorders in Finland (about 50 workers) 41

42. www.lukimat.fi • a project on developing nation-wide services • on disabilities in reading and mathematics • in early education • via web • started 1997 • funded by the Ministry of Education • employs about 10 persons in total (researchers, programmers, assistants) 42

43. www.lukimat.fi • Contains • a knowledge base for teachers and parents • computer assisted educational games for reading and mathematics • evidence-based approach (i.e. combined to research-based development work cycles) • a web-based java-environment for assessments and research • everything is free for end-users 43

44. www.lukimat.fi • Impact (from start 1.11.2007 – 19.11.2010) • Web pages • nearly 400 000 visitors, 2,4 miljon visits • Friday 19.11. – 2970 visitors • Games • Already over 60 000 single users for reading and math games • compare: Finland • about 5 000 special teachers • 30 000 lower primary school teachers • 58 000 children /year 44

45. www.lukimat.fi • Research results (reading) • at risk 1st graders • Computer-assisted reading game playing combined to special education produced better results (1 year follow-up ES 1,0) than special education alone in children with risk for reading disabilities • Computer-game players reached the level of normal readers • Saine, N.L., Lerkkanen, M-K., Ahonen, T., Tolvanen, A. & Lyytinen, H. (2010). Predicting word-level reading fluency outcomes in three contrastive groups: Remedial and computer-assisted remedial reading intervention, and mainstream instruction. Learning and Individual Differences, 20, 402–414. • results require replication in Finnish and other languages 45

46. www.lukimat.fi • Research results (math) • Low performing pre-schoolers • two different types of games produced a slightly different learning effect • improvement in number sense during the intervention compared to typically performing controls (ES of gain .36–.53) • Räsänen, P., Salminen, J., Wilson, A., Aunio, P., & Dehaene, S. (2009). Computer-assisted intervention for children with low numeracy skills. Cognitive Development, 24, 450–472. • requires replications with the new game versions 46

47. www.lukimat.fi • international co-operation on research and development possible with research institutions and governmental agencies • games are already translated to several languages 47

48. Thank you! Contact info Pekka Räsänen Niilo Mäki Institute PO Box 35 FIN-40014 University of Jyväskylä FINLAND t. +358 50 4343 495 e. pekka.rasanen@nmi.fi w. http://users.jyu.fi/~prasanen