Strategies for Training Teachers to Integrate Technology in the classroom
A systematic review
Sujata N Gamage, [email protected] (with Amrita Khakurel , Achala Abeykoon, Chivoin Peou,Sandalika Weerasuriya and Tushar Tanwar )
ICT4D, Singapore, March 15, 2015
This work was carried out with the aid of a grant from the International Development Research Centre, Canada.
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RESEARCH QUESTIONWhat makes teachers integrate technology into the teaching–learning process?
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BACKGROUND
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Integration of technology (ICT) in education promised higher learning outcomes
Technology INTERVENTION
Student learning
outcomes (BEFORE)
Classroom
Student learning
outcomes (AFTER)
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Gains are modest but expectations remain high
*Cheung, Alan C.K. and Slavin, Robert E. (2013). The effectiveness of educational technology applications for enhancing mathematics achievement in K-12 classrooms: A meta-analysis. Educational Research Review 9 (2013) 88–113.)
Educational technology is making a modest difference in learning of mathematics. It is a help, but not a breakthrough. However, the evidence to date does not support complacency. New and better tools are needed to harness the power of technology to enhance mathematics achievement for all children.”
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“The evidence suggests that teachers went through the motions as prescribed but did not master the innovation in a way that would have allowed students to get the most of it.”
Source: RCT by ADB in Costa Rica (2014)
Reason: teacher and teaching-learning inside the classroom ignored?
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What happens inside the classroom black-box?
Technology INTERVENTION
Student learning
outcomes (BEFORE)
Classroom
Student learning
outcomes (AFTER)
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Unpacking the classroom black-box in ICT4ED
Technology INTERVENTION
Student learning
outcomes (BEFORE)
Classroom with
Teachers
Student learning
outcomes (AFTER)
Teacher training or support
INTERVENTIONS
Teacher
Acceptance of
technology by teacher
Technology using teacher
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Training/support for teachers
TeacherAcceptance of technology by
teacher
Technology using
teacher
ICT use/integration in the classroom
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Theory of change
Technology use OUTCOMES
Teacher training/supportINTERVENTIONS
Student learningOUTCOMES
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Interventions
• Behavioral Perceived usefulness and ease of use• Normative Perceptions of those important to you• Functional Training, support, infrastructure & other
SOURCES: Technology acceptance model (TAM), Davis, 1986Theory of planned behavior, Icek Ajzen 1989Unified Theory of Technology Acceptance ad use (UTTAU), Venkatesh, 2003
Technology, Pedagogy & Content (TPACK)Innovation diffusion (Complexity, compatibility, relatedness, observabiity)
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Outcomes
• Frequency of use– Never to daily
• Level of use– Teacher use for preparation/presentation/follow-up– Teacher guided student use– Student use for independent learning in or our of class
• Frequency and level of use
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POPULATION K-12 in-service teachersINTERVENTION Behavioral
NormativeFunctional
CONTROL Experimental (RCTs)Quasi-exptl. (comparables/statistical)
OUTCOMES Frequency/Level of useCONTEXT Year, Technology/Use & Other
PICOCs
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METHOD
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• SEARCH XYZ databases with specific search string*• SCREEN (1) to include empirical studies concerning
technology use in K-12 classrooms and exclude all others
• SCREEN (2) to include exptl. or Quasi –exptl. studies and exclude all others
• EXTRACTION Extract PICOCs for each study• CODING Code predictors/outcomes into few categories
as possible• APPRAISAL Appraise for Risk of bias and• SYNTHESIS Calculate effect sizes for category of predictor
Systematic review process
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- Selection bias- Confounding variables bias- Motivation bias- Performance bias- Reporting bias- Type 1/Type II errors- Other biases
Types of bias
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RESULTS
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30,000+ hits 2000+ empirical studies on technology use in K-12
classroom 100+ Quasi experimental
o [10] Treatment (with or with out comparison group)o [90] Natural experiment (with or with out comparison
group)
Most are observation studies of ICT use employing multivariate regressions to ease out effects of different factors
Search results (1990-2014)
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ANALYSIS
Quantitative (~100)
Extracting PICOCs and related statistics
Effect size calculation
Synthesis (quant.)
Qualitative (~100+1900)
Extracting PICOCs
Coding
Synthesis (qualit.)
via two linked tracks
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Improved theory of change
OUTCOMESTechnology use
INTERVENTION
SPECIFIC INTERVENTION(school systems)
GENERIC INTERVENTION(Colleges of education, e.g.)
BEHAVIORAL UsabilityUsefulness
ICT proficiency & attitudesPedagogical attitudes
NORMATIVE School policy Influence by important others
FUNCTIONAL Support from the school Availability of resource, technical support etc.
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Effect size for five intervention categories PERSONAL BEHAVIORAL
(technological)BEHAVIORAL(pedagogical)
NORMATIVE FUNCTIONAL
1 Abdullah2013
-IT knowledgeabilitySMD 2.81 - - Student attitudes,
Ergonomics2 Brunk
2008Gender, age, advanced degree, exp.
Personal computer useSMD 0.85
Instructional practices
Poverty, school culture, and principal support.
3 Fordham 2004
Commitment to teaching
amount of technology trainingSMD 0.54
openness to change
- -
4 Hastings 2009
Experience Proficiency: Productivity SoftwareSMD 0.37Perceived Benefits of Using Technology.
- - -
5 Hefernnen 2012
Personal use, Self-Efficacy, Playfulness,
Skill level SMD 0.39 - - -
6 Hermans-2008
Gender Computer experienceSMD – 0.47General computer attitudes
constructivist beliefs - -
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Working Hypothesis
Normative/Functional effects > Behavioral effects
If system-wide intervention such as E-books and IWBs which are integral to the curriculum and test taking are implemented with sufficient support for teachers, negative behavioral attributes of teachers, if any, wont matter
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Thank you
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Improved theory of change
OUTCOMESTechnology use
Technology is specifiedBehavioral
Usability : Complexity, Trialability and ObservabilityUsefulness: Relatedness, Compatibility
NormativeInfluence by important others
FunctionalResources, Technical support etc.
Technology is not specifiedBehavioral
ICT skills & attitudesPedagogical skills and attitudes
NormativeInfluence by important others
FunctionalResources, technical support
PREDICTORS
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Extraction worksheet
Paper
Technology
Technology use outcomesFrequency Level
Baek_2006
(1) none, (2) rarely, (3)Moderate (4) high—almost weekly per semester
Teacher use/student use
• using the basic functions of technology,
• using the enhanced functions of technology
• deriving attention• adapting to external requests
and others’ expectations• class preparation and
management• relieving physical fatigue• (TEACHER DERIVED)
Extractors the factors underlying the lay person implicit ideas or beliefs by surveying the users would provide a more authentic and ecologically valid prospect.
Fordham_2004
-1 to 5, with participants’ survey responses according toa five-point scale, ranging from “never” to “several times a week.”
LOW: Focuses on the teacher using technology to get their job done.MODERATE: Involves teacher facilitation of large group learning activities and studentHIGH: productivity use of technology. Promotes students to be actively engaged in using tech. in individual and collaborative learning activities
• openess to change• no. of hours of technology
training• no. of hours worked beyond
contractual work week
TPCK (Technology factors/Teacher factors); Technology factors (Marcinkiewicz, 1994; Vannatta & O’Bannon, 2002),
Hastings_2009
TPCK/Tiers of Technologyby Washington State Technology Integration into the Curriculum Working Group (2005).
never, less than once per week, once per week, 3 times a week, and daily
(1) Teacher-Use of Technology for Delivering Instruction (TUTDI);(2) Teacher-Use of Technology for Class Preparation (T-UTCP) (3) Teacher-Directed Student Use of Technology to Create Products (T-DSUTCP); (4) Teacher-Directed Student Use of Technology during Class Time (T-DSUTCT).
• Teacher Proficiency: Productivity Software
• Beliefs and Behaviors about classroom technology use
• Perceived Benefits of using technology
TPCK/Tiers of Technologyby Washington State Technology Integration into the Curriculum Working Group (2005).
Hong_2009
TPCK (Teacher/Environment)
5-point Likert scale ranging from never to daily use
utilization, integration, reorientation, and evolution (Welliver, 1989)
• Attitude toward computer technology
• Attitude toward computer technology
• Computer literacy skills • Hours of teachers’ technology
education (10 hours)• Number of computers in the
classroom • Age
TPCK (Teacher/Environment)
Johnson_2006
TPCK & Welliver’s (1989) Instructional Transformation Model (utilization, integration, reorientation, and evolution).)
‘‘never (1)” to ‘‘daily (7)”. DLE, Communication, Administration, all together (smartschools.be)
• Years of teaching experience (total model score)
• Hours of professional development (familiarization)
• Level of education completed by teachers (reorientation)
• Teachers’ perception of principals’ knowledge of technology (utilization)
TPCK & Welliver’s (1989) Instructional Transformation Model (utilization, integration, reorientation, and evolution).)
Pynoo_2011
UTAUT 0-3 (i.e., never, monthly, weekly, daily).
Drill & Practice, Tutorial, Simulation, Instructional Games, Problems Solving, ProductivityEntry, adoption, adaptation, appropriation, and invention Sandholtz (1997)
• Performance expectancy• Effort expectancy• Social influence• Facilitating conditions• Acceptance (Attitude,
behavioural intention, s-r use)
UTAUT
Rickman_2009
'Teacher+Env ('Teacher Personal Attribute + Environmental Variables= Variation in a Teacher’s use of Technology in the K???5 Classroom)
developed by van Braaket al. (2004). It consists of six 5-point Likert items(never, every term, monthly, weekly, daily)
Supportive use/classroom use
• Teacher variables• teaching philosophy• software proficiency• software availability• ENV variables not significant
'Teacher+Env ('Teacher Personal Attribute + Environmental Variables= Variation in a Teacher’s use of Technology in the K???5 Classroom)
Sang_2010
The study presents a relational model embracing a wide variety of internal teacher variables related to ICT integration. Building on available research (not UTAUT)
• Indirect: constructivist beliefs. Perception of ICT policy, Attitudes towards ICt in education,
• Direct ICT motivation; Supportive use of ICT
The study presents a relational model embracing a wide variety of internal teacher variables related to ICT integration. Building on available research (not UTAUT)
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Data entry & calculation
Author/Measure of acceptance or use/ Population/ Sample/ Response/ RoB
Statistics for Teachers’ skills and attitudes
ICT Skills ICT Attitude Pedagogy-Attitude
Abdullah-2013Acceptance of E-books (11 items): strongly disagree, disagree, moderate, agree, strongly agree Population: 642 Primary teacher Grade 4—6 in two DUNs representing urban and rural DUNs Sura and Rantau Abang Sample: random sample by school for 5 schools in Sura and 11 in Rantau Abang Usable Response rate: 254/300 RoB:SelectionConfounding variable
IT knowledgeability β; p; t; SE=; Sp; v ; Yt, Yc, Ys; nt =nc = ns =254; SDt, SDc and SDY ; ATT RR: 1.13 (0.01) SMD
- -
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Summary of effect sizes
StudiesPredictors
ICT Skills ICT Attitude Pedagogy AtitudeAbdullah-2013RoB-Low
IT knowledgeabilityRR: 1.13 (0.01) SMD
Askar_RoB
Complexity RR: SMD
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Brunk Personal computer useRR:SMD
- Instructional practices
Fordham amount of technology trainingRR:SMD
- openness to change
Hermans-2008 Computer experienceRR:SMD
general computer attitudes
constructivist beliefs
Hong-Table8-p.62 Computer efficacy Attitude towars computer technology
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Hua Technology literacy - -Pynoo Effort expectancy - -Rickman-tabe 24, p.109 Software proficiency - teaching philosophySanford-2007 - - Sang-2011 Computer motivation - constructivist beliefsSarfo - - Skoertz efficacy for technology
integration - -
Smeets - - Stols beliefs about their level of
technological proficiencybeliefs about the perceived usefulness
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Teo teachers’ computer efficacy Teachers’ attitudes toward computer use
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Tondeur-2010 - - Tondeur-2010 perceived expectancy of
success- -
Van Acker ICT skills was in its turn the strongest predictor of self-efficacy.
Attitudes towards ICT -
Van Braak computer experience (computer training, computer experience expressed over time, intensity of computer use)
general computer attitudes, attitudes toward computers in education, and technological innovativeness
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VanderLinde ICT competences - developmental educational beliefs
Waight-2014 Not significant Not significant Not significantWard-2010 confidence in ability to use
technology in the classroom, with self-efficacy
- -
Wisenmayer-1999 - - Wozney-2006 - - Wu-2007 - - Ying-Shao-2007 - - Yucel ICT knowledge of teachers - -
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GoalPlots for ICT skills, ICT attitude, Pedagogical attitude, School policy, ICT support (differentiated by technology specificity?); Sample plot below
McEwan, 2014
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Predictor1: ICT efficacyPaper Descriptor/Definition Code
Abdullah_2013 IT Knowledgeability
Askar_ Complexity
Brunk LOTI-Personal computer use
Fordham amount of technology training
Herman computer experience,
Hong-Table8-p.62 Computer efficacy
Hua Technology literacy
Pynoo Effort expectancy
Rickman software proficiency
Sang-2011 Computer motivation
Skoertz efficacy for technology integration
Stols beliefs about their level of technological proficiency
Teo teachers’ computer efficacy
Tondeur-2010 perceived expectancy of success
Van Acker ICT skills was in its turn the strongest predictor of self-efficacy.
Van Braak computer experience (computer training, computer experience expressed over time, intensity of computer use)
VanderLinde ICT competences
Ward-2010 confidence in ability to use technology in the classroom, with self-efficacy
Yucel 'ICT knowledge of teachers
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Theory of Planned behavior Icek Ajzen (2006)
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Unified theory on technology acceptance and useUTTAU (Venkatesh , 2003)