Strategies for Training Teachers to Integrate Technology in the classroom

A systematic review

Sujata N Gamage, gamage@gmail.com (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

-

Teo teachers’ computer efficacy Teachers’ attitudes toward computer use

-

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

-

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)