Effects of Technology Integration Education on the Attitudes of

Journal of Research on Technology in Education 411Copyright © 2002, ISTE (International Society for Technology in Education), 800.336.5191 (U.S.& Canada) or 541.302.3777 (Int’l), [email protected], www.iste.org. All rights reserved.

Effects of Technology IntegrationEducation on the Attitudes of

Teachers and Students

Rhonda ChristensenUniversity of North Texas

AbstractMajor findings regarding the effects of technology integration education on elementary schoolteachers are presented. A study of a K–5 treatment site versus two comparison schools overone academic year indicates that teachers progress one stage in a six-stage technology adop-tion model as a result of focused, needs-based technology integration education deliveredthroughout the school year. Needs-based technology integration education is shown to havea rapid, positive effect on teacher attitudes, such as computer anxiety, perceived importanceof computers, and computer enjoyment. This type of education is shown to have a time-lagged positive effect on the attitudes of students as well. (Keywords: attitudes, computers,students, teachers, technology integration, training.)

The U.S. Congress Office of Technology Assessment (OTA, 1995) has re-ported that helping teachers “effectively incorporate technology into the teach-ing and learning process is one of the most important steps the nation can taketo make the most of past and continuing investments in educational technol-ogy” (p. 8). Most researchers agree that the successful use of computers in theclassroom is dependent on positive teacher attitudes toward computers (Lawton& Gerschner, 1982; Woodrow, 1992). Successful classroom practice also in-cludes fostering positive perceptions of information technology among students(Knezek, Miyashita, & Sakamoto, 1993). This article presents findings from ayear-long study of a large public elementary school in north Texas as it initiatedthe introduction of information technology into teachers’ daily classroom prac-tice. Changes in teacher attitudes because of ongoing technology integrationeducation are presented. Findings related to the effects on their students are in-cluded as well.

LITERATURE REVIEWTeacher Attitudes toward Technology

Loyd and Gressard (1986) showed that positive attitudes toward computersare positively correlated with teachers’ extent of experience with computer tech-nology. With familiarity, anxieties and fears tend to decrease, and confidence in-creases. The amount of confidence a teacher possesses in using computers andrelated information technologies (often referred to as simply “technology”) maygreatly influence his or her effective implementation of technology methods inthe classroom. Positive teacher attitudes toward computers are widely recog-nized as a necessary condition for effective use of information technology in theclassroom (Woodrow, 1992).

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412 Summer 2002: Volume 34 Number 4Copyright © 2002, ISTE (International Society for Technology in Education), 800.336.5191 (U.S.& Canada) or 541.302.3777 (Int’l), [email protected], www.iste.org. All rights reserved.

Gardner, Discenza, and Dukes (1993) determined that computer anxiety is amajor cause of resistance to using computers. This and other research indicatesthat increased computer experience reduces computer anxiety in many teachers.However, the ability to reduce anxiety may also depend on the type of com-puter experience to which the teachers are exposed (McInerney, McInerney, &Sinclair, 1994). Beasley and Sutton (1993) found that at least 30 hours of in-struction and practice were required just to reduce anxiety about informationtechnology. These authors reported that reducing uncertainty is just the firststep to becoming confident and competent users of technology.

Educators are often resistant to using computer technology in the classroom.Therefore, changing teachers’ attitudes is a key factor in fostering technologyintegration (Marcinkiewicz, 1993–1994). Stevens (1980, as cited in Violato,Mariniz, & Hunter, 1989) identified teachers’ attitudes as well as expertise inusing computers as major factors in the adoption of computers in the class-room. Although teachers’ attitudes were not historically considered in the intro-duction of computers into the classroom, many scholars now contend that fu-ture successful implementations will need to address teachers’ attitudes towardcomputers (Hunter & deLeeuw, 1988, as cited in Violato et al.). According toHignite and Echternacht (1992), it is critical that teachers possess both positiveattitudes and adequate computer literacy skills to successfully incorporate tech-nology into the classroom.

Effects of Attitudes and TrainingSeveral studies have suggested that attitudes are an important element in

teaching children about computers (Woodrow, 1990). For example, Todmanand Dick (1993) emphasized that an important factor affecting the quality of achild’s experience with computers in school may be the teacher’s attitude towardcomputers.

Based on an international study involving children, teachers, and computers,Pelgrum and Plomp (as cited in Collis et al., 1996) concluded that:

Teachers are the main gatekeepers in allowing educational innovationsto diffuse into the classrooms. Therefore one of the key factors foreffecting an integration of computers in the school curriculum is ad-equate training of teachers in handling and managing these new toolsin their daily practices. (p. 31)

Pelgrum and Plomp (as cited in Collis et al., 1996) also found that the “de-gree of classroom computer use was closely tied to extent of training in integra-tion techniques” (p. 32).

Other studies have shown that students’ attitudes about school and learningare likely to affect student achievement (OTA, 1995) and that appropriateteacher training in classroom computer use can be associated with higher stu-dent achievement (Educational Testing Service [ETS], 1998, as reported inPierce, 1998). In the words of Pierce, “Not surprisingly, the [ETS] study foundthat students whose teachers had been trained to teach with computers scored

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higher [in mathematics achievement] than students whose teachers lacked suchtraining” (p. 2). Demonstrating that appropriate teacher preparation in class-room use of computers could result in positive attitudes among their studentswas one missing component in the causal link.

Role of Technology Integration EducationIt is generally accepted in the literature that appropriate training of teachers

should include the ability to use the computer for personal use (i.e., word pro-cessing, gradebook, etc.) and with students in the classroom (Burkholder, 1995;Hignite & Echternacht, 1992; Hochman, Maurer, & Roebuck, 1993; Kearns,1992; Ritchie & Wiburg, 1994; Todd, 1993; Wetzel, 1993; Woodrow, 1992).The instructor who has learned to integrate technology into existing curriculamay teach differently than the instructor who has received no such training.The difference in classroom technique and the greater extent of technology useshould have a positive effect on teacher and student attitudes toward informa-tion technology.

RESEARCH RATIONALEIn the United States, significant resources have been expended to place com-

puters in the schools over the past two decades. According to a study by theETS, the total cost of technology in U.S. schools as of the late 1990s was ap-proximately $3 billion, or $70 per pupil (Coley, Cradler, & Engal, 1998).Many educators have reported their expert opinions of the effects of this influxof technology on student learning; especially since the early 1990s, scholars inthe field have pointed out the need to address the issue of accountability in asystematic sense. As plans are made for the increased use of technology, it isimportant for policy makers, educators, and researchers to understand howteachers and children relate to this technology (Martin, Heeler, & Mahmoud,1992).

A major corollary of the accountability issue has to do with the proportion fortechnology funds that should be spent on training. Data from a 1995 nationalsurvey of school district technology budget allocations revealed that approxi-mately 55% of technology money was being spent on hardware and 30% onsoftware. Teacher education accounted for only 15% of the allocated funds(OTA, 1995). The U.S. Department of Education has recommend that districtsallocate 30% of their technology budgets to staff development activities (OTA),but a recent CEO Forum (1999) report suggests that progress toward this goalhas been slow:

Available data on computer training, which is only the most basic com-ponent of overall technology training and education for educators,suggests that the news is not good. In 1998–1999, schools projectthey will spend $5.65 per student on computer training of teachers. Incomparison, schools expect to spend $88 on instructional hardware,software, and connectivity in 1998–1999. Matched against the U.S.Department of Education’s recommendation that schools allocate at

jrte_344_guts.p65 9/17/02, 9:31 AM413


least 30 percent of their technology budgets to professional develop-ment, current spending is inadequate. (p. 6)

The 1999 CEO Forum study also estimated that the $3 billion spent on tech-nology in U.S. schools to date represents slightly more than 1% of total educa-tion spending and that it will cost approximately $15 billion to make all ourschools “technology rich.” This is approximately $300 per student, or approxi-mately 5% of total education spending, and approximately five times what wenow spend on technology (CEO Forum). If the recommended 30% of this$300 per student is allocated to teacher training, then approximately $90 perpupil, or $1,800 per elementary school teacher with a class of 20, should bebudgeted for training. Such a large projected increase in funds allocated to tech-nology staff development necessitates studies to determine what types of teachereducation and re-education lead to effective use of technology in the classroom.

What are the relationships among technology integration education of teach-ers, their attitudes toward information technology, and their students’ attitudes?Previous research has shown that positive attitudes are a precursor to effectiveutilization. Verification of a positive effects of needs-based technology integra-tion education on the attitudes of teachers and their students could influencethe way teachers are educated to use computers in the classroom. The resultcould be more effective use of funds for teacher education.

METHODSubjects

Sixty teachers in a suburban, public elementary school in north Texas receivedneeds-based instruction in the integration of computers into classroom learningactivities during the school year. The education consisted of two days of inten-sive training at the beginning of the school year with follow-up trainingthroughout the year (approximately once every six weeks). Two similar publicelementary schools in the same school district were used as comparison groups.Educators at these schools received the normal district-level technology inser-vice training, but not the needs-based technology integration education deliv-ered at the treatment school.

The treatment elementary school provided education for approximately 900PK–5 students at the time of the study. The population was 82% minority:65% Hispanic, 10% African American, and 7% from other ethnic groups.Among these students, 76.8% were eligible for free or reduced lunches underU.S. Government Title I funding. Comparison group schools drew their stu-dent populations from the same district. The primary comparison group hadsimilar ethnic and socioeconomic demographics, while the secondary compari-son group, which was closer in proximity, had a student population that was ap-proximately 65% white, 17% Hispanic, 12% African American, and 6% fromother ethnic groups. The secondary comparison site did not qualify as a Title I–funded school because only 44.2% of their students were eligible for free or re-duced lunches.

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InstrumentationThe Teachers’ Attitudes Toward Computers Questionnaire (TAC Ver. 2.21)

was the primary instrument used to gather attitudinal data from the teachers atthe treatment and comparison sites. The TAC was developed to measure teach-ers’ attitudes in this study. It was originally constructed as a 10-part compositeinstrument that included 284 items spanning 32 Likert subscales and was ad-ministered to 621 educators during the construct validation stage. A factoranalysis of the 284 individual items on the questionnaire indicated that between4 and 22 different attributes were actually measured by the items collected fromthe 32 previously published subscales. Examination of the factor structures forall 4 to 22 feasible solutions resulted in selections of 7-factor, 10-factor, and 16-factor structures as the most meaningful representations of the domain(Christensen & Knezek, 1996). Internal consistency reliabilities for the 16-factor structure of the TAC selected for use in this study, based on the previ-ously reported 621-educator data set, ranged from .75 to .96.

Four construct indices for Loyd and Gressard’s (1986) Computer AttitudeSurvey (CAS) were also included in the analysis of teacher data in order to linkfindings to the numerous studies that had previously used this instrument(Woodrow, 1991). One of the constructs, Loyd and Gressard Confidence, hadbeen carried forward to the 16-factor structure of the TAC (see F14 in Table 1),but not all items from the original Confidence subscale were carried forward.Therefore, the original Computer Confidence construct (CASC), as defined onthe CAS, was included for completeness. The other three original subscales ofLoyd and Gressard’s CAS were retained as attitude indicators for historical pur-poses. These subscales were CASA (Anxiety), CASC (Confidence), and CASL(Liking). Loyd and Gressard’s reliability estimates for these subscales rangedfrom .82 to .90 and a total score reliability of .95 (Kluever, Lam, Hoffman,Green, & Swearingen, 1994). Christensen and Knezek (2000a) found that thesubscale reliabilities were comparable, ranging from .81 to .91, for the 621-edu-cator data set referenced in the previous paragraph. These reliability estimatesare listed in Table 1.

Three constructs from Knezek and colleagues’ (1993) Young Children’s Com-puter Inventory (YCCI) were included as measures for teachers as well: Com-puter Importance (I), Computer Enjoyment (J), and Computer Anxiety (A).Knezek and Miyashita (1993) reported reliabilities ranging from .82 to .84 forstudents on these scales. Reliability estimates for teachers range from .81 to .91,as shown in Table 1. Because two of the measures (I and J) were also gatheredfrom all students in the study, these indices enabled direct comparisons ofteacher and student attitudes toward computers. A complete description of theattitudinal instruments for teachers as well as the actual items employed can beobtained from Christensen (1997) and in the electronic booklet Instruments forAssessing Educator Progress in Technology Integration (Knezek, Christensen,Miyashita, & Ropp, 2000).

A skills checklist and stages of adoption form (see Appendixes A and B online atwww.iste.org/jrte) were also administered to the educators in this study, as were ex-

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Table 1. Internal Consistency for 16-Factor Structure of the TAC

Subscales Alpha Number of Variables

F1 (Enthusiasm) .96 15F2 (Anxiety) .98 15F3 (Acceptance) .75 4F4 (E-mail) .95 11F5 (Negative Effect on Society) .84 10F6 (Classroom Learning Productivity) .90 14F7 (Kay Semantic) .94 10F8 (Vocation) .92 13F9 (Prestige) .75 7F10 (Teacher Productivity) .94 14F11 (Aversion) .74 6F12 (Gender Bias) .81 6F13 (K&M Importance) .83 8F14 (L&G Confidence) .83 6F15 (Relevance) .89 10F16 (P&P Enjoyment) .90 9CASA (CAS Anxiety) .91 9CASC (CAS Confidence) .81 10CASL (CAS Liking) .89 10CASU (CAS Understanding) .85 10K&M Importance .81 7K&M Enjoyment .84 9K&M Anxiety .91 8

perimental items aimed at gathering teacher attitudes toward teaching with tech-nology (see Appendix C online at www.iste.org/jrte). A training needs-assess-ment instrument was also administered to the teachers at the beginning of thestudy. Information from this survey was used to design the appropriate technol-ogy instruction for these teachers. Teacher attitudes were gathered during threetesting periods for the study: time 1, September; time 2, January; time 3, May.

Students completed the YCCI during the first and third testing periods of theschool year. The YCCI is a 59-item Likert scale with 18 paired comparisonitems also included. The Likert items measure six learning dispositions: Com-puter Importance, Computer Enjoyment, Motivation/Persistence, Study Hab-its, Empathy, and Creative Tendencies (Knezek & Miyashita, 1993). An addi-tional subscale was added to measure students’ attitudes toward school. A morecomplete description of the YCCI can be obtained online at www.iittl.unt.edu/.

RESULTSThree major hypotheses were explored in this study:

Hypothesis 1: Needs-based technology-integration education fosters positive at-titudes toward technology among elementary school classroom teachers.

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Hypothesis 2: Teacher education in needs-based technology integration, com-bined with significant classroom use, fosters positive student attitudes towardinformation technology.

Hypothesis 3: Positive teacher attitudes toward information technology fosterpositive attitudes in their students.

The findings for each are addressed in the sections that follow.

Analysis of Hypothesis 1All teacher subjects from the three schools were combined for the initial

analysis of the data regarding Hypothesis 1. Teachers were divided into two cat-egories: those who reported having received integration training (IT) and thosewho reported having received no integration training (No-IT).

A one-way analysis of variance was performed using January data for thetreatment school and the two comparison schools combined and separating ITand N-IT teachers. Teachers in the IT group scored significantly higher, p < .05,than the No-IT group on 13 of 22 factors. These data are provided in Table 2(p. 418).

A one-way analysis of variance comparing IT and No-IT teacher attitudes wasalso performed using May data. Teachers who reported they had received inte-gration training (IT) had significantly more positive, p < .05, attitudes on all ofthe teacher attitude subscales measured. These data are provided in Table 3 (pp.419–420).

Treatment Group Comparisons over TimeOnly the treatment group teachers received the technology integration educa-

tion provided by me. Using their August data (gathered prior to the treatment),a series of one-way ANOVAs were performed comparing IT (Group 1, n = 6)and No-IT (Group 2, n = 16). No significant, p < .05, differences were found be-tween the two groups on any of the 22 attitudinal indices as measured by the TAC.

For the time period January to May, among teachers who reported integrationtraining, 13 out of 22 factors changed significantly, p < .05, in a positive direc-tion for the teachers who received the treatment (Table 4, pp. 421–422),whereas only 4 of the 22 factors changed in a positive direction for comparisongroup teachers (Table 5, 423–424). A binomial test was performed using 4/22as the expected probability of success. The two groups were found to be signifi-cantly different at the � = .0001 level (Weast, 1969, p. 600).

Effects of Teacher Integration Education for Treatment Versus Comparison GroupsA dummy-coded multiple regression analysis was performed to determine

whether type of teacher integration education had a differential effect onteacher attitudes for the treatment versus comparison groups. Basically, this wasa test to determine whether the teacher integration education delivered by mehad a greater effect than the standard school district training and workshopsavailable to all teachers in the treatment and comparison groups. Frequency ofuse measures (usenow 1 and howoft 2) were also included in the regressionmodel to statistically control for frequency of use.

jrte_344_guts.p65 9/17/02, 9:31 AM417


Tabl

e 2.

Tre

atm

ent

Gro

up a

nd C

ompa

riso

n G

roup

Tea

cher

s: I

nteg

rati

on T

rain

ing

Vers

us N

o In

tegr

atio

n Tr

aini

ng, J

anua

ry 1

997

Subs

cale

Gro

upn

MSD

Fdf

pSS

Tot S

SM

S

CA

SA (

CA

S A

nxie

ty)

IT26

4.09

.53

11.1

348

.00

11.0

113

.62

.23

No-

IT23

3.62

.43

CA

SC (

CA

S C

onfid

ence

)IT

263.

70.4

33.

7447

.06

7.31

7.9

1.1

6N

o-IT

223.

47.3

5C

ASL

(C

AS

Liki

ng)

IT26

3.82

.52

7.76

47.0

112

.15

14.2

0.2

6N

o-IT

223.

40.5

1F1

(E

nthu

sias

m)

IT26

4.05

.51

3.63

46.0

69.

5210

.29

.21

No-

IT21

3.80

.39

F2 (

Anx

iety

)IT

264.

01.6

49.

0045

.00

16.4

219

.78

.37

No-

IT20

3.46

.57

F4 (

E-m

ail)

IT27

3.67

.80

5.14

49.0

319

.43

21.5

1.4

0N

o-IT

233.

26.3

6F6

(C

lass

room

Lea

rnin

g Pr

oduc

tivi

ty)

IT26

4.18

.51

5.55

46.0

27.

94 8

.92

.18

No-

IT21

3.89

.27

F8 (

Voc

atio

n)IT

244.

07.4

94.

2739

.05

6.56

7.30

.17

No-

IT16

3.79

.27

F9 (

Pres

tige

)IT

273.

85.5

38.

7347

.00

9.64

11.4

7.2

1N

o-IT

213.

46.3

4F1

0 (T

each

er P

rodu

ctiv

ity)

IT25

4.11

.52

4.49

44.0

49.

009.

93.2

1N

o-IT

203.

81.3

5F1

1 (A

vers

ion)

IT27

4.09

.53

5.29

46.0

38.

849.

89.2

0N

o-IT

203.

79.2

8F1

4 (L

&G

Con

fiden

ce)

IT26

3.54

.65

3.30

48.0

817

.11

18.3

1.3

6N

o-IT

233.

22.5

4F1

6 (P

&P

Enj

oym

ent)

IT26

3.11

.57

7.72

48.0

112

.56

14.6

2.2

7N

o-IT

232.

70.4

5

* Tw

o-ta

iled

signi

fican

ce re

port

ed, p

< .0

5.

jrte_344_guts.p65 9/17/02, 9:31 AM418


Tabl

e 3.

Tre

atm

ent

Gro

up a

nd C

ompa

riso

n G

roup

Tea

cher

s: I

nteg

rati

on T

rain

ing

Vers

us N

o In

tegr

atio

n Tr

aini

ng, M

ay 1

997

Subs

cale

Gro

upn

MSD

Fdf

pSS

Tot S

SM

S

Tch

iIT

263.

40.4

5 3

.60

47

.06

7.40

8.00

.34

No-

IT22

3.18

.34

Tch

jIT

273.

37.3

5 6

.66

47

.01

5.48

6.28

.12

No-

IT21

3.11

.34

Tch

anx

IT26

3.25

.42

5.6

5 4

6.0

27.

258.

16.1

6N

o-IT

212.

97.3

7C

ASA

IT25

4.18

.55

8.4

1 4

6.0

110

.24

12.1

6.2

3N

o-IT

223.

78.3

8C

ASC

IT26

3.77

.41

8.4

2 4

7.0

15.

93 7

.01

.13

No-

IT22

3.46

.29

CA

SLIT

273.

93.5

1 8

.42

48

.01

10.7

612

.68

.23

No-

IT22

3.53

.44

CA

SUIT

274.

21.4

714

.10

49

.00

8.60

11.1

2.1

8N

o-IT

233.

76.3

6F1

IT25

3.86

.42

5.7

9 4

7.0

25.

966.

71.1

3N

o-IT

233.

61.2

8F2

IT25

4.05

.63

5.4

2 4

6.0

213

.94

15.6

1.3

1N

o-IT

223.

67.4

6F3

IT27

4.47

.47

3.9

8 4

7.0

59.

089.

83.2

0N

o-IT

214.

21.4

1F4

IT25

3.92

.71

10.0

7 4

6.0

016

.66

20.4

0.3

7N

o-IT

223.

36.4

7co

ntin

ued

on p

. 420

* Tw

o-ta

iled.

jrte_344_guts.p65 9/17/02, 9:31 AM419


Tabl

e 3,

con

t.

Subs

cale

Gro

upn

MSD

Fdf

pSS

Tot S

SM

S

F5IT

253.

73.4

7 2

.80

46

.10*

9.77

10.3

8.2

2N

o-IT

223.

50.4

6F6

IT25

4.24

.53

6.4

0 4

7.0

19.

0410

.30

.20

No-

IT23

3.91

.31

F7IT

265.

76.8

2 3

.86

46

.06

36.4

739

.60

.81

No-

IT21

5.24

.99

F8IT

254.

15.5

6 5

.91

46

.02

9.86

11.1

5.2

2N

o-IT

223.

82.3

2F9

IT24

3.83

.68

4.0

8 4

6.0

513

.91

15.1

7.3

1N

o-IT

233.

51.3

9F1

0IT

254.

18.5

5 4

.85

46

.03

9.33

10.3

4.2

1N

o-IT

223.

89.3

2F1

1IT

254.

10.5

6 2

.50

47

.12*

11.7

212

.36

.25

No-

IT23

3.87

.44

F13

IT26

3.51

.39

4.1

8 4

7.0

55.

666.

17.1

2N

o-IT

223.

31.3

0F1

4IT

273.

59.6

0 2

.73

48

.11*

15.8

716

.79

.34

No-

IT22

3.31

.55

F15

IT25

4.46

.46

5.5

6 4

7.0

26.

467.

24.1

4N

o-IT

234.

21.2

4F1

6IT

273.

09.6

0 5

.65

49

.02

14.4

916

.20

.30

No-

IT23

2.72

.48

* Tw

o-ta

iled.

jrte_344_guts.p65 9/17/02, 9:31 AM420


Tabl

e 4.

Tre

atm

ent

Gro

up T

each

ers

Janu

ary

to M

ay o

n In

tegr

atio

n Tr

aini

ng V

ersu

s N

o In

tegr

atio

n Tr

aini

ng

Teac

her

subs

cale

Tim

eM

eans

IT

Mea

ns N

o-IT

Diff

eren

ce b

etw

een

IT a

nd N

o-IT

Diff

eren

ce b

etw

een

Janu

ary

and

May

p

I-Ja

n2

3.43

3.24

–.19

.40

I2-M

ay3

3.49

3.07

–.42

.23

.02

J2

3.41

3.40

–.01

.92

33.

443.

12–.

32.3

1.0

5A

nxie

ty2

3.19

3.10

–.09

.70

33.

413.

03–.

38.2

9.0

4C

ASA

24.

243.

67–.

57.0

13

4.40

3.88

–.52

–.05

.03

CA

SC2

3.81

3.44

–.37

.05

33.

913.

49–.

42.0

5.0

2C

ASL

23.

933.

43–.

50.0

13

4.05

3.50

–.55

.05

.02

CA

SU2

4.39

3.92

–.47

.01

34.

413.

72–.

69.2

20.

00F1

(E

nthu

sias

m)

24.

183.

89–.

29.0

83

4.00

3.67

–.33

.04

.02

F2 (

Anx

iety

)2

4.27

3.71

–.56

.03

34.

233.

78–.

45–.

11.0

9F3

(A

ccep

tanc

e)2

4.57

4.03

–.54

0.00

34.

754.

10–.

65.1

10.

00F4

(E

-mai

l)2

3.77

3.28

–.49

.12

33.

793.

33–.

46–.

03.1

0

cont

inue

d on

p. 4

22

Janu

ary:

IT

n =

10.

No-

IT n

= 1

1. M

ay: I

T n

= 1

0. N

o-IT

n =

11.

jrte_344_guts.p65 9/17/02, 9:31 AM421


Tabl

e 4,

con

t.

Teac

her

subs

cale

Tim

eM

eans

IT

Mea

ns N

o-IT

Diff

eren

ce b

etw

een

IT a

nd N

o-IT

Diff

eren

ce b

etw

een

Janu

ary

and

May

p

F5 (

NI)

23.

903.

80–.

10.6

43

3.77

3.65

–.12

.02

.51

F6 (

Prod

-cla

ss)

24.

323.

88–.

44.0

23

4.60

3.83

–.77

.33

0.00

F7 (

Kay

Sem

)2

5.47

5.97

.50

.14

35.

845.

88.0

4.4

6.8

8F8

(V

ocat

ion)

24.

243.

71–.

53.0

23

4.45

3.79

–.66

.13

0.00

F9 (

Pres

tige

)2

4.01

3.40

–.61

.01

34.

043.

45–.

59–.

02.0

4F1

0 (P

rod-

tchr

)2

4.25

3.79

–.46

.02

34.

513.

87–.

64.1

80.

00F1

1 (A

vers

ion)

24.

303.

87–.

43.0

73

4.32

3.92

–.40

–.03

.07

F13

(K&

M I

mp)

23.

523.

41–.

11.6

03

3.63

3.23

–.40

.29

.01

F14

(L&

G C

onf)

23.

593.

21–.

38.1

33

3.34

3.38

.04

–.42

.28

F15

(P&

P R

el)

24.

614.

11–.

50.0

13

4.77

4.13

–.64

.14

0.00

F16

(P&

P E

nj)

23.

122.

68–.

44.0

73

3.05

2.70

–.35

–.09

.11

Janu

ary:

IT

n =

10.

No-

IT n

= 1

1. M

ay: I

T n

= 1

0. N

o-IT

n =

11.

jrte_344_guts.p65 9/17/02, 9:31 AM422


Tabl

e 5.

Com

pari

son

Gro

up T

each

ers

Janu

ary

1997

and

May

199

7

Teac

her

subs

cale

Tim

eM

eans

IT

Mea

ns N

o-IT

Diff

eren

ce b

etw

een

IT a

nd N

o-IT

Diff

eren

ce b

etw

een

Janu

ary

and

May

p

I-Ja

n2

3.29

3.24

–.05

.80

I2-M

ay3

3.32

3.26

–.06

.01

.70

J2

3.33

3.13

–.20

.19

33.

323.

10–.

22.0

2.1

2A

nxie

ty2

3.13

2.92

–.21

.29

33.

132.

92–.

210.

00.1

8C

ASA

23.

973.

58–.

39.0

43

4.04

3.69

–.35

–.04

.05

CA

SC2

3.61

3.51

–.10

.49

33.

683.

44–.

24.1

4.0

8C

ASL

23.

743.

38–.

36.1

43

3.84

3.55

–.29

–.07

.14

CA

SU2

4.02

4.12

.10

.55

34.

073.

79–.

28.3

8.1

0F1

(E

nthu

sias

m)

23.

963.

72–.

24.2

53

3.76

3.55

–.21

–.03

.18

F2 (

Anx

iety

)2

3.81

3.25

–.56

.03

33.

923.

55–.

37–.

19.1

0F3

(A

ccep

tanc

e)2

4.23

4.38

.15

.46

34.

284.

32.0

4.1

1.8

3F4

(E

-mai

l)2

3.60

3.24

–.36

.13

34.

033.

38–.

65.2

9.0

2

cont

inue

d on

p. 4

24

Janu

ary:

IT

n =

10.

No-

IT n

= 1

1. M

ay: I

T n

= 1

0. N

o-IT

n =

11.

jrte_344_guts.p65 9/17/02, 9:31 AM423


Tabl

e 5,

con

t.

Teac

her

subs

cale

Tim

eM

eans

IT

Mea

ns N

o-IT

Diff

eren

ce b

etw

een

IT a

nd N

o-IT

Diff

eren

ce b

etw

een

Janu

ary

and

May

p

F5 (

NI)

23.

583.

48–.

10.6

63

3.70

3.35

–.35

.25

.09

F6 (

Prod

-cla

ss)

24.

083.

90–.

18.3

33

4.00

3.99

–.01

–.17

.94

F7 (

Kay

Sem

)2

5.74

5.21

–.53

.16

35.

714.

66–1

.05

.52

.01

F8 (

Voc

atio

n)2

3.93

3.85

–.08

.66

33.

923.

84–.

080.

00.6

9F9

(Pr

esti

ge)

23.

743.

50–.

24.1

53

3.69

3.55

–.14

–.10

.51

F10

(Pro

d-tc

hr)

24.

013.

84–.

17.3

93

3.97

3.90

–.07

–.10

.70

F11

(Ave

rsio

n)2

3.95

3.73

–.22

.14

33.

933.

82–.

11–.

11.5

9F1

3 (K

&M

Im

p)2

3.38

3.31

–.07

.67

33.

433.

38–.

05–.

02.6

8F1

4 (L

&G

Con

f)2

3.50

3.24

–.26

.31

33.

553.

24–.

31.0

5.2

2F1

5 (P

&P

Rel

)2

4.27

4.38

.11

.48

34.

264.

28.0

2.0

9.8

6F1

6 (P

&P

Enj

)2

3.10

2.71

–.39

.07

33.

122.

74–.

38–.

01.1

1

Janu

ary:

IT

n =

10.

No-

IT n

= 1

1. M

ay: I

T n

= 1

0. N

o-IT

n =

11.

jrte_344_guts.p65 9/17/02, 9:31 AM424


As shown in Table 6, when using standardized regression coefficients (�) tocompare the effects of treatment group training and use to comparison group’straining and use, it appears that the amount of teacher computer use (usenow)is a stable predictor of the teacher’s perceptions of Computer Importance (I2)for the treatment elementary school, � = .42, p < .02, and for the comparisonschools, � = .45, p < .04. Level of teacher integration education reported forMay (trng2) is not a significant predictor of teacher Computer Importance forthe comparison group, � = –.10, NS, but it is a good predictor of teacher Com-puter Importance for the treatment group, � = .73, p < .04. This indicates thatthe integration education delivered to the teachers at the treatment groupschool significantly influenced educator perceptions of Computer Importance.

Table 6. Multiple Regression for Teacher Computer Importance in May1997 as a Function of Training and Use, Incorporating Dummy-CodedTreatment Versus Comparison Groups

TchI2 as a function of: � p

Comparison compuse .52 .24Treatment compuse .14 .84Comparison usenow .45 .04Treatment usenow .42 .02Comparison howoft .55 .06Treatment howoft .23 .47Comparison trng2 (May measure) –.10 .72Treatment trng2 (May) .73 .04

n = 50. F(8,31 df) = 3.43. Signif F = .0061.

Acceptance of Hypothesis 1Data gathered in this study indicate that:

1. Teachers at the treatment and comparison sites who reported having receivedcomputer integration education tended to exhibit more positive attitudes to-ward information technology than their non-integration counterparts.

2. Teachers at the treatment site changed to a greater extent in the direction ofmore positive attitudes than did their comparison group peers.

3. The integration education delivered at the treatment site had a significanteffect on perceived computer importance (after controlling for frequency ofuse), while the effects of training at the comparison site was negligible.

1 Currently I use the computer approximately __ hours per week in the classroom.2 I use computers for instruction in the classroom: __ Daily, __Weekly, or __Occasionally

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These findings, taken as a whole, led to the acceptance of the hypothesis thatneeds-based technology integration education fosters positive attitudes towardinformation technology among elementary school classroom teachers.

Analysis of Hypothesis 2Trends in Classroom Use of Computers

A background question in May asked the teachers how many hours per weekthey currently used computers and how many hours they had used computersat the beginning of the school year.

For the treatment group teachers, 18 reported an increase in use, and 1 re-ported a decrease in use. Out of the 10 teachers in Comparison Group 1 withcomplete data, 9 reported an increase in computer use, whereas 1 reported a de-crease. Out of the 12 teachers in comparison group 2 with complete data, 4 re-ported an increase in computer use, 1 reported a decrease, and 7 reported nochange. Combining Comparison Group 1 and 2 subjects, the number of teach-ers who increased in their use of technology for the comparison time period was13 out of 22, as compared to 18 out of 19 teachers in the treatment group re-porting an increase in use. Treatment group teachers were more likely thancomparison group teachers to report an increase in use of technology over thecourse of the study.

Operational Definition of Significant Classroom UseFrequency distributions were calculated to help determine what amount of

computer use would be classified as significant for the purpose of categorizingteachers to be included in the analysis. Looking across all classroom teachersfrom the three schools, it was found that roughly 50% used computers fourhours or less per week, and roughly 50% used them five hours or more perweek. Therefore, teachers who used computers five hours or more per weekwere classified as significant users, and those reporting four or fewer were classi-fied as not having significant use.

Operational Definition of Technology Integration EducationThe determination of technology integration education for teachers from Hy-

pothesis 1 was also used for Hypothesis 2.

Regression Analysis of Student and Teacher Attitudes Because of Training and UseA regression analysis was used to examine the effects of computer use and

technology integration education on student attitudes. The rationale for thisprocedure was to take advantage of the ordered nature of computer use and in-tegration education for added precision in measurement. Student ComputerImportance (I) and Enjoyment (J) were assessed as a function of teacher train-ing and classroom use in May 1997. As shown in Table 7, only the amount ofteacher use (usenow and howoft) significantly influenced student Importance(I) and Enjoyment (J) in a positive direction, p < .05.

jrte_344_guts.p65 9/17/02, 9:31 AM426


Time-Lag Regression Analysis of Student and Teacher AttitudesA time-lag regression analysis for student attitudes as a function of teacher

training was carried out to determine if teacher integration education had atime-delayed impact on student attitudes. After removing outliers that werethree standard deviations or greater from the mean, a regression analysis wasrun using the treatment group teachers. Student Computer Importance (I) inMay was found to be a function of reported teacher training in January,� = .14, p < .03. There appears to be a three-month lag in student perceivedimportance because of teacher training.

Conditional Acceptance of Hypothesis 2Based on the data gathered in this study, the hypothesis that teacher instruc-

tion in needs-based technology integration, combined with significant class-room use, fosters positive student attitudes toward information technology wasaccepted. Regression techniques confirmed the strong effects of the extent ofteacher computer use on the attitudes of their students. Time-lag regressionconfirmed the existence of a probable causal path from the January level ofteacher integration education to the May Computer Importance (I) for theirstudents.

Analysis of Hypothesis 3Panel Analysis for Directional Effects of Student and Teacher Attitudes

Panel analysis (Markus, 1979) was used to determine probable causal relationsamong student and teacher attitudes. Panel analysis is a form of time-lag regres-sion analysis in which attitudes at one time are used to predict attitudes at a

Table 7. Student Importance and Enjoyment as a Function of HowOft,Trng, and UseNow, May 1997 Treatment Group Data

Teacher and Student Factors � p

I2(student) = f(howoft + trng)HowOft .27 .00Trng –.094 .05Sig of F = .0000

I2 (student) = f(usenow+trng+howoft)UseNow .11 .05Trng –.06 .21HowOft .23 .00

J2 (student) = f(HowOft + Trng)HowOft .17 .00Trng –.02 .66

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subsequent time in order to determine the probable direction of causality. Be-cause of a skew in the student data, an outlier test was run, and the outliers(> +/– 3 SD) were removed.

Teacher information technology attitudes were compared to student informa-tion technology attitudes using panel analysis. Teacher Computer Importance(Tchi2) was run as a function of student Computer Importance (I). Student Iat time 3 was run as a function of Tchi at time 2. Using time-lag regressionJanuary to May, it was found that teacher Importance (Tchi) in January (time2) is a strong predictor of student Computer Importance (I2) in May (time 3)(Figure 1).

Acceptance of Hypothesis 3A series of panel analyses using time-lag regression analyses, similar to the

one diagrammed in Figure 1, confirmed the following with respect to probabledirectional influences for teacher and student attitudes toward informationtechnology:

1. Positive teacher perceptions of computer importance influence student per-ceptions of computer importance in a positive manner, � = 1.03, p < .005.

2. Positive teacher computer enjoyment influences student perceptions ofcomputer importance in a positive manner, � = 0.82, p < .005.

3. Positive teacher enthusiasm (F1) influences student perception of ComputerImportance in a positive manner, � = 0.33, p < .005.

4. Lack of teacher anxiety (TchAnx) influences student perception of Com-puter Importance in a negative manner, � = –.37, p < .02.

January May(time 2) (time 3)

Student StudentImportance Importance

(I) (I2)

Teacher TeacherImportance Importance

(TchI) (TchI2)� = 1.65 (p < .00)

� = 0.10

(p < .0029)

� = 1.

03

(p <

.00)

� = 0.31 (p < .00)

Figure 1. Panel analysis for directional effects of teacher and student ComputerImportance.

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5. Higher semantic perception of computers (F7) on the part of teachers influ-ences perception of Computer Enjoyment in a negative manner, � = –0.27,p < .04.

6. No strong relationships were found in the direction of student attitudes in-fluencing those of their teacher. However, there emerged a consistent trendof student Computer Importance negatively influencing numerous teacherdispositions related to information technology (e.g., F1, Teacher Enthusi-asm), � = –0.06, p < .12.

These findings, taken as a whole, led to the acceptance of the hypothesis thatpositive teacher attitudes toward information technology foster positive atti-tudes in their students. However, further research is needed to determine whycertain Likert scales (such as teacher anxiety) are in the opposite direction ofwhat might have been anticipated.

DISCUSSIONDirectional Effects of Teacher and Student Attitudes

Students’ attitudes were affected by teacher attitudes toward informationtechnology in this study. Some of the linkages are direct, while other student at-tributes appear to be indirectly impacted by teachers’ attitudes. For example,there is a strong positive path from teacher training to teacher use, � = .20,p < .02, and from teacher use to student Computer Importance, � = .32,p < .003 (Figure 2). Though some effects are not directly measured, there islittle doubt that the way teachers view technology affects the attitudes of theirstudents.

There were also instances in which it appeared that student attitudes affectedtheir teacher’s attitudes. For example in the area of anxiety, higher ComputerImportance on the part of the students in January apparently fostered higherlevels of teacher anxiety in May. This finding deserves to be studied in more de-tail with a larger sample of teachers and students.

Stages of AdoptionRubinyi (1989) reported the importance of a well thought-out training pro-

gram and the availability of follow-up support are crucial for successful adop-tion. Russell (1995) developed a classification scheme that was used as the basisof the Stages of Adoption form constructed for the current research.

Teacher Teacher StudentTraining Use Computer

Importance

� = 0.20

(p < .02)

� = 0.32

(p < .003)

Figure 2. Probable causal path from Teacher Training to Teacher Use to Stu-dent Computer Importance.

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Christensen and Knezek (1999) have demonstrated that progression throughstages of adoption can be regarded as a linear scale (see Appendix B, online atwww.iste.org/jrte/, for a description of stages). This type of classification systemappears to have great potential as a simple outcome measure to be used by cur-riculum planners for diagnosing initial educator levels of technology integrationand properly sequencing training activities in order to move teachers to higherlevels.

In this study, most of the teachers moved toward higher stages of adoptionduring the ongoing, on-site technology integration education. The average in-crease was one stage from August to May. Although the exact changes that takeplace as a teacher moves from one stage to the next are not precisely specified bythe scale, there is little doubt that advancing one stage is typically accompaniedby an increase in an educator’s sense of confidence and competence in the useof technology. For example, knowledge of how to make Kid Pix (1990–2001)slide shows to use in the classroom significantly increased, p < .005, from Au-gust (M = 1.35, SD = .57, n = 23) to May (M = 2.17, SD = .78, n = 23), on ascale of 1, low competence; 2, medium; 3, high competence 3 (instrumentfound in Appendix A). Recent research (Christensen & Knezek, 2000b) hasshown that educators generally advance toward higher competence and morepositive attitudes as self-reported stages of adoption increase.

The relevance of higher stages of adoption to student learning was also ex-plored in this study. Using regression analysis, a probable causal path was foundrelating teacher’s stage of adoption to their students’ attitudes toward comput-ers. As shown in Figure 3, amount of previous technology training was found tobe a good predictor of teacher stage of adoption, β = .49, p < .0001, thoughstage of adoption was, in turn, found to be a good predictor of teacher com-puter use, � = .36, p < .0001. An additional link in the chain is that teachercomputer use was found to be a good predictor of student computer impor-tance, � = .32, p < .003, which one can infer should lead to greater studentlearning through increased student use. Additional research is needed to verifythe validity of this hypothesized causal model.

Teacher Teacher Teacher StudentTraining Stage Use Computer

Importance

� = 0.49

(p < .001)

� = 0.36

(p < .0001)

Figure 3. Probable causal path from Teacher Training to Student Computer Im-portance via Stage of Adoption and Teacher Computer Use.

� = 0.32

(p < .003)

3 Of the 17 skills measured, 13 were selected to analyze because they were introduced in the staffdevelopment sessions delivered at the treatment school. Of those 13 skills, 10 were significantlyhigher at the posttest than the pretest. Two of the non-significant skills were only mentioned andnot thoroughly covered. When looking at all 17 skills, 12 were significant. All 17 of the skillsmoved toward more competence.

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CONCLUSIONTechnology integration education appears to strongly influence teachers’ atti-

tudes toward computers. The direct effect of teacher technology integrationeducation on students is weaker although also present. Training appears to fos-ter meaningful use by teachers in the classroom, which, in turn, fosters studentComputer Enjoyment and later a perception of importance of computers. Inaddition, however, it also appears that greater positive perception of ComputerImportance among the students in a classroom also fosters higher ComputerAnxiety in their teachers. This implies that teachers need some mechanism attheir disposal—ongoing education, for example—that continues to reduce theiranxiety more rapidly than the advancing skill level of their students, whichtends to put pressure on them, causing teacher anxiety levels to increase. Find-ings from this study support the contention that funding ongoing technologyintegration education for teachers is a crucial component for having technologymake a difference in the education of our students. �

ContributorBefore earning her doctorate in information science at the University of

North Texas (UNT), Rhonda Walker Christensen earned her BS in elementarycurriculum and instruction from Texas A & M University and her MS in com-puter education and cognitive systems from UNT. She was previously an el-ementary school teacher and is interested in technology integration develop-ment of inservice and preservice teachers. Dr. Christensen is currently aninstructor of preservice educators in the Department of Technology and Cogni-tion at the UNT. She is currently an evaluator for a U.S. Department of Educa-tion Technology Innovation Challenge grant and also a U.S. Department ofEducation Preparing Tomorrow’s Teachers to Use Technology (PT3) grant. (Ad-dress: Rhonda Christensen, University of North Texas, PO Box 311337,Denton, TX 76203; [email protected].)

ReferencesBeasley, W., & Sutton, R. (1993). Integration of computers in schools: Three lev-

els of teacher expertise. Journal on Computing in Teacher Education, 9(4), 11–15.Burkholder, J. (1995). An annotated bibliography of the literature dealing with

teacher training in the uses of the computer in education. (ERIC No. ED 260 696)CEO Forum. (1999). Professional development: A link to better learning [On-

line document]. Available: www.ceoforum.org/reports.cfm?RID=2.Christensen, R. (1997). Effect of technology integration education on the at-

titudes of teachers and their students. Unpublished doctoral dissertation, Uni-versity of North Texas. Available: http://courseweb.tac.unt.edu/rhondac/.

Christensen, R., & Knezek, G. (1996, January). Constructing the Teachers’ At-titudes toward Computers (TAC) questionnaire. Paper presented to the SouthwestEducational Research Association Annual Conference, New Orleans, LA.

Christensen, R., & Knezek, G. (1999). Stages of adoption for technology ineducation. Computers in NZ Schools, 11(3), 25–29.

jrte_344_guts.p65 9/17/02, 9:31 AM431


Christensen, R., & Knezek, G. (2000a). Internal consistency reliabilities for14 computer attitude scales. Journal of Technology and Teacher Education, 8(4),327–336.

Christensen, R., & Knezek, G. (2000b). Advancement of student technologyintegration skills through university preservice coursework. In D. A. Willis,J. Price, & J. Willis (Eds.), Technology and teacher education annual 2000 (Vol.2, pp. 1505–1510). Charlottesville, VA: Association for the Advancement ofComputing in Education.

Coley, R. J., Cradler, J., & Engal, P. K. (1998). Computers and classrooms: Thestatus of technology in U.S. schools. Policy information report. Princeton, NJ:Policy Information Center, Educational Testing Service.

Collis, B. A., Knezek, G. A., Lai, K. W., Miyashita, K. T., Pelgrum, W.J.,Plomp, T., Sakamoto, T. (1996). Children and computers in school. Mahwah, NJ:Lawrence Erlbaum Associates.

Gardner, D. G., Discenza, R., & Dukes, R. L. (1993). The measurement ofcomputer attitudes: An empirical comparison of available scales. Journal of Edu-cational Computing Research, 9(4), 487–507.

Hignite, M. A., & Echternacht, L. J. (1992). Assessment of the relationshipsbetween the computer attitudes and computer literacy levels of prospective edu-cators. Journal of Research on Computers in Education, 24, 381–389.

Hochman, A., Maurer, M., & Roebuck, D. (1993). Buttons and cards andfields, oh my! Tech Trends, 38(2), 25–28.

Kearns, J. (1992). Does computer coursework transfer into teaching practice?Journal of Computing in Teacher Education, 8(4), 29–34.

Kid Pix [Computer software]. (1990–2001). Novato, CA: The LearningCompany.

Kluever, R. C., Lam, T. C. M., Hoffman, E. R., Green, K. E., & Swearingen,D. L. (1994). The computer attitude scale: Assessing changes in teachers’ atti-tudes toward computers. Journal of Educational Computing Research, 11(3),251–261.

Knezek, G., Christensen, R., Miyashita, K., & Ropp, M. (2000). Instrumentsfor assessing educator progress in technology integration [Online document]. Avail-able: www.iittl.unt.edu/.

Knezek, G. A., & Miyashita, K. T. (1993). Handbook for the young children’scomputer inventory. Denton: Texas Center for Educational Technology.

Knezek, G., Miyashita, K., & Sakamoto, T. (1993). Cross-cultural similaritiesin attitudes toward computers and the implications for teacher education. Jour-nal of Information Technology for Teacher Education, 2(2), 193–204.

Lawton, J., & Gerschner, V. T. (1982). A review of the literature on attitudestowards computers and computerized instruction. Journal of Research and Devel-opment in Education, 16(1), 50–55.

Loyd, Β. H., & Gressard, C. P. (1986). Gender and amount of computer ex-perience of teachers in staff development programs: Effects on computer atti-tudes and perceptions of usefulness of computers. Association for EducationalData Systems Journal, 19(4), 302–311.

jrte_344_guts.p65 9/17/02, 9:31 AM432


Marcinkiewicz, H. R. (1993–1994). Computers and teachers: Factors influ-encing computer use in the classroom. Journal of Research on Computing in Edu-cation, 26(2), 220–237.

Markus, G. Β. (1979). Analyzing panel data. Beverly Hills, CA: Sage.Martin, C. D., Heller, R. S., & Mahmoud, E. (1992). American and Soviet

children’s attitudes toward computers. Journal of Educational Computing Re-search, 8(2), 155–185.

McInerney, V., McInerney, D. M., & Sinclair, K. E. (1994). Student teachers,computer anxiety and computer experience. Journal of Educational ComputingResearch, 11(1), 27–50.

Pierce, D. (1998). ETS study shows how computers can help or hurt mathachievement [Online document]. Available: www.eschoolnews.org/~archive/1998100501.html.

Ritchie, D., & Wiburg, K. (1994). Educational variables influencing technol-ogy integration. Journal of Technology and Teacher Education, 2(2), 143–153.

Rubinyi, R. M. (1989). Computers and the community: The organizationalimpact. Journal of Communication, 39(3), 173–178.

Russell, A. L. (1995). Stages in learning new technology: Naive adult e-mailusers. Computers in Education, 25(4), 173–178.

Todd, N. (1993). A curriculum model for integrating technology in teachereducation courses. Journal of Computing in Teacher Education, 9(3), 5–11.

Todman, J., & Dick, G. (1993). Primary children and teachers’ attitudes tocomputers. Computers in Education, 20(2), 199–203.

U.S. Congress, Office of Technology Assessment. (1995). Teachers and tech-nology: Making the connection (OTA-EHR-616). Washington, DC: U.S. Gov-ernment Printing Office.

Violato, C., Mariniz, A., & Hunter, W. (1989, Winter). A confirmatoryanalysis of a four-factor model of attitudes toward computers: A study of preser-vice teachers. Journal of Research on Computing in Education, 22, 199–213.

Weast, R. C. (Ed.). (1969). CRC standard mathematical tables. Cleveland,OH: The Chemical Rubber Co.

Wetzel, K. (1993). Models for achieving computer competencies in preserviceeducation. Journal of Computing in Teacher Education, 9(4), 4–6.

Woodrow, J. E. (1990). Locus of control and student teacher computer atti-tudes. Computers in Education, 14(5), 421–432.

Woodrow, J. E. (1991). A comparison of four computer attitude scales. Jour-nal of Educational Computing Research, 7(2), 165–187.

Woodrow, J. E. (1992). The influence of programming training on the com-puter literacy and attitudes of preservice teachers. Journal of Research on Com-puting in Education, 25(2), 200–218.

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