A short introduction to ICT in
special education
Pekka Räsänen
researcher, clinical neuropsychologist
Niilo Mäki Institute, Jyväskylä
29.11.2010
Who’s talking?
An impressionistic self-portrait, 2007
PR Publications 2008–
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Developmental and Educational Psychology
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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]
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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/
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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
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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
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Räsänen, P. , Maslova, U., & Maslov, O. (submitted). A multidimensional adaptive system for web-based computer assisted learning: A model and an
implementation.
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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.
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Yevseyeva, I., Miettinen, K., & Räsänen, P. (2008). Verbal ordinal classification with multicriteria decision aiding. European Journal of Operational
Research. Ref.
Tests and rehabilitation materials
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Mattinen, A., Räsänen, P., Hannula, M.M., & Lehtinen, E. (2008, 2010). [A program for supporting early learning prerequisities in mathematics]. NMIBulletin, 4, 40–53.
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Aunio, P., & Räsänen, P., & Van Luit, J. E. H., (2010) [I count! A remedial programme for children’s number sense] . NMI.
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Poutiainen, E., Kalska, Laasonen, Närhi, & Räsänen, P. (2010). [Trail Making – test. Manual]. Psychologist’s Publisher Corp..
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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/
ICT in early education ?
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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:
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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
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Structure of the presentation
1. A short history of CAI
2. ICT in SEN
• Defining ICT in SEN
• Effectiveness of ICT in SEN
3. Case study:
• LukiMat -a national special educational webbased support system on learning disabilities in
early education
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History of Computer Assisted Instruction:
quickly and inaccurately
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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
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Kempelen's chess playing Turk (1768)
“Intelligent Machines” - Faked Chess
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First steps in CAI
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Skinner, H. (1866). Teaching spelling. Washington, DC: United States Patent Office; No.
52,758)
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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"
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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.
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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.
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1960’s: Time of optimism
• the mid 1960s there were thousands of available
programs across a wide variety of school subjects
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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
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1970’s: First PCs
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Pong by Atari, 1971, the first working computer game
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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.
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Projects “PLATO” in 1961 and “TICCIT” in 1969.
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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.
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1978 First issue of the Journal of Special Education Technology is published
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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.
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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
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2000 ->
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boom of the mobile computing, everybody is ”online 24/7”
networked and cloud computing
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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)
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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.)
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Affordable technologies
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low-end smart phones
OLPC = One laptop per child
Electronic books (1st generation: Kindle, iPad, Android, Win7 and Meego tablets)
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electronic school books -> embedded CAI into school books
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Subgroups of SEN
Subgroups of students (%) with SEN in
Finnish and Chech primary education
severe mental impairment
moderate mental impairment
mild mental impairment
hearing impairment
visual impariment
physical and other impairments
emotional and social impairment
other impairments
speech difficulties
reading, writing and mathematical difficulties
immigrant students receiving remedial teaching
Finland
0,12
0,5
1,5
0,12
0,02
0,4
0,3
0,1
4,7
10,3
1,2
Chech
2,9
0,13
0,9
0,18
0,3
0,24
4,5
Source: Eurostat
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Subgroups of SEN
• Three main groups
1. Children with specific deficits in perception, motor or general
cognitive skills
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small groups with clearly less than 1% prevalence
2. Children with average or low average general skills, but specific
difficulties in language and/or basic academic skills (reading,
writing, arithmetic)
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large group, depending on criteria 5–15%
3. Children with difficulties in emotion or behaviour
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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
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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
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
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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
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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
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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).
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ICT as a TOOL for learning
• The most typical target of research on ICT in education
– Educational applications (CAI)
– ICT tools in education
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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
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Research results and
effectiveness
What do we really know about CAI ?
Research:
A long history of mixed results
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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
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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
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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
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Lack of controlled studies
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Edyburn, D.L. (2004). 2003 in review: A
synthesis of the special education
technology literature. Journal of Special
Education Technology, 19(4), 57-80.
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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)
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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
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The results from a mega-meta-analysis
Lipsey,M.W. & Wilson,D.B.(1993)The efficacy of psychological, educational, and behavioral treatment: Confirmation from metaanalysis. American Psychologist, 48(12), 1181-1209
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.
Effective Instructional Practices
Mean Effect Size
Mnemonic Instruction
1,62
Self-Monitoring
1,36
Reinforcement
1,17
Self-Questioning
1,16
Drill & Practice
0,99
Strategy Instruction
0,98
Feedback
0,97
Direct Instruction
0,93
Visual Displays
0,90
Computer-Assisted Instruction
0,87
Repeated Reading
0,76
Error Correction
0,72
Early Intervention
0,71
Formative Evaluation
0,70
Peer Mediation
0,64
Diagnostic-Prescriptive Teaching
0,64
Peer Tutoring
0,62
Positive Class Morale
0,60
Grouping
0,43
Cooperative Learning
0,40
Increased Time
0,38
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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
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
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Summary of research results
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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
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ICT as a TARGET
– not well studied in the population of young children with SEN
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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
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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.
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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
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)
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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)
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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 researchbased development work cycles)
– a web-based java-environment for assessments
and research
– everything is free for end-users
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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
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compare: Finland
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about 5 000 special teachers
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30 000 lower primary school teachers
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58 000 children /year
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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.
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results require replication in Finnish and other languages
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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)
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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
www.lukimat.fi
– international co-operation on research and
development possible with research institutions
and governmental agencies
– games are already translated to several languages
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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
Thank you!