Content uploaded by Robin Holding Kay
Author content
All content in this area was uploaded by Robin Holding Kay on Mar 21, 2019
Content may be subject to copyright.
EXAMINING THE IMPACT OF A MOBILE DEVICE PROGRAM ON
PRE-SERVICE TEACHERS’ ATTITUDES, EMOTIONS AND
LEARNING RELATED TO TECHNOLOGY USE
R. Kay
University of Ontario Institute of Technology (CANADA)
Abstract
Based on over 150 meta-analyses, technology has proven to have a significant impact on education
with an effect size of 0.34 [1]. If we assume that meaningful use of technology can have a significant
and positive impact on student learning, teacher education is a reasonable place to start with respect to
integrating technology into the classroom [2]. However, there is some evidence to suggest pre-service
education programs are not preparing new teachers to use technology effectively [3]. Potential barriers
have included insufficient access to technology and creating an environment that fully integrates
technology and learning[3]. The purpose of this study was to examine the impact of a fully-integrated,
mobile device program on pre-service teachers’ attitudes, emotions and learning related to technology
use. One-hundred eighty-four pre-service teachers (61 male, 123 female), specializing in STEM-based
subject areas in secondary schools participated in the study. Pre-service teachers used mobile devices,
the majority of which were laptops, for a period of 8 months. Mobile devices were meaningfully and
regularly integrated into course and teaching activities. Pre-post survey data were collected about
attitudes (self-efficacy, affective, cognitive, behavioural), emotions (happiness, anger, anxiety,
sadness), and learning (communications, productive software, subject-based software, programming)
related to technology. With respect to attitudes, significant increases in self-efficacy and behavioural
attitudes were observed. No significant gains were observed for affective or cognitive attitudes.
Regarding emotions, both anger and anxiety decreased significantly when using mobile devices.
Happiness and sadness remained the same. Finally, significant increases were observed in teacher’s
perceived ability to use operating systems, communicate, search the web, use productivity software
(e.g., word processing, spreadsheets, graphics, presentations), creating web pages, and coding.
Overall, the integration of mobile devices in the teacher education program was deemed successful in
helping pre-service teachers to learn and become more comfortable with technology.
Keywords: mobile devices, pre-service education, attitude, emotions, learning.
1 INTRODUCTION
Over the past 20 years, research on the use of technology in education has reported mixed or limited
results in terms of value and effectiveness [4-8]. Nonetheless, educational policy specialists and
administrators have made a persistent effort to increase the presence of technology in classrooms [9-
12]. Teacher education programs have reflected this emphasis on the use of educational technology
[13].
One of the key predictors of preservice teacher’s use of technology in the classroom is the quantity and
quality of technology-based activities experienced in teacher education programs [14]. However,
several studies have suggested that technology is underused by preservice teachers [3, 15]. Tondeur
[14] conducted a review of teacher preparation programs to identify key factors associated with the
successful integration of technology. These factors included sufficient access to technology, sufficient
opportunities for instructional design, authentic learning experiences, collaboration, modelling of
technology use and reflection [14]. These results are consistent with Kay’s [3] review of strategies used
to incorporate technology into pre-service education. To date, formal analysis of their effectiveness of
Tondeur’s [14] and Kay’s [2] suggested list of potentially impactful factors has yet to be conducted.
The purpose of this study was to assess the impact of an integrated model of technology use in a teacher
education program based on Tondeur’s [14] recommendations. Specifically, changes in pre-service
teachers’ attitudes toward technology, emotions while learning new software, and technology-based
skills were examined.
Proceedings of INTED2019 Conference
11th-13th March 2019, Valencia, Spain
ISBN: 978-84-09-08619-1
3486
2 METHODOLOGY
2.1 Participants
One-hundred eighty-four pre-service teachers (61 male, 123 female) volunteered to participate in the
study. Most preservice teachers had 6 to 10 (n=70) or 10+ (n=81) years of computer-related experience
before they entered the program. Subject areas of focus included computer science, mathematics,
biology, chemistry and physics. Geographic backgrounds of these students included North America
(n=145), Europe (n=15), Asia (n=14), Africa (n=6), and South America (n=1).
2.2 Description of Program
The Bachelor of Education degree at this university consisted of an eight-month consecutive program,
focusing on Computer Science, Math, or Science (Physics, Chemistry, Biology, and General Science)
at the intermediate-secondary school level (grades 7 to 12). All students were required to have an
undergraduate degree with of five full university courses in their first teachable area and three full
university courses in their second teachable area. Every student had a mobile device with a wide range
of educational and application-based programs and ubiquitous wireless high-speed internet access.
An integrated model was followed to incorporate technology into pre-service education. Students used
their mobile devices in all courses but did not take a stand-alone course in technology use. With the
exception of a four-hour introductory workshop attended by all students at the beginning of the year (4
hours) and two workshops on designing web pages (optional attendance) (1 hour each), there was no
formalized instruction on how to use computer technology. All faculty members created assignments
and projects that required students to mobile devices to solve meaningful, practical, and useful
problems. A majority of the activities used were based on well-grounded, learning theory including
cooperative learning [16], constructivism [17, 18], facilitation and coaching [19], problem-based learning
[20], higher-level thinking skills [21], connecting concepts to real-world knowledge [22], and actively
applying knowledge [23].
2.3 Data Collection and Analysis
The pre- and post-surveys, which were identical, consisted of four sections: demographics, attitudes,
emotions and learning. The demographics section collected information gender, computer experience,
subject areas of focus and geographic origins. The second section was comprised of 37 seven-point
Likert scale items focusing on behavioural attitudes (n=8 items), cognitive attitudes (n=10 items),
affective attitudes (12 items) and self-efficacy (n=7 items) with respect to using computers. The third
section consisted of 12 four-point Likert scale items measuring happiness (n=3 items), anger (n=3
items), anxiety (n=3 items), and sadness (n=3 items) while learning new computer software. The fourth
and final section consisted of 77, five-point Likert-scale items focusing on operating systems (n=10
items), communication (n=10 items), browsing the web (n=9 items), word processing (n=10 items),
spreadsheets (n=6 items), graphics (n=6 items), presentations (n=6 items), creating web pages (n=10
items), and coding (n=10 items). The validity and reliability of the attitude, emotion and ability scale
were established by Kay & Knaack [2] and Kay & Loverock [24].
2.4 Procedure
At the beginning of the of the Bachelor of Education program, participants in this study filled in the pre-
survey which took about 20 to 25 minutes to complete. At the end of the eight-month program using an
integrated approach to implementing technology inside and outside of the classroom (see description
of program above), participants filled in a post-program survey that was identical to the pre-survey.
3 RESULTS
3.1 Attitudes
Behavioural attitudes toward technology (e.g., intentions to use technology/software in the future)
increase significantly between pre- and post-tests, however, the effect size of 0.22 is considered small
according to Cohen [25]. Self-efficacy or perceived control over using technology also increased
significantly between pre- and post-tests and the effective size was considered medium [25]. Affective
3487
(negative and positive) and cognitive attitudes toward technology did not change significantly over the
eight-month program (Table 1).
Table 1. Pre-Post Test for Attitude toward Technology (n=149).
Pre-Test
Mean (SD)
Post-Test
Mean (SD)
t-value
Cohen’s d
Behavioural
15.0 (3.6)
15.7 (2.7)
2.1 **
0.22
Affective (Negative)
5.1 (4.3)
4.7 (4.6)
1.2
NA
Affective (Positive)
18.5 (3.4)
18.5 (3.3)
0.1
NA
Cognitive
18.3 (3.7)
17.9 (3.8)
0.6
NA
Self-Efficacy
19.5 (6.7)
23.3 (5.2)
9.2 *
0.63
* p < .001
** p < .05
3.2 Emotions
Anger expressed while learning new software decreased significantly between pre- and post-tests,
although the effect size is small according to Cohen [25]. Anxiety also decreased significantly between
pre- and post-tests, and the effect size is considered moderate [25]. Happiness and sadness expressed
while learning new technology did not change significantly over the eight-month program (Table 2).
Table 2. Pre-Post Test for Emotions Expressed when Learning New Software (n=149).
Pre-Test
Mean (SD)
Post-Test
Mean (SD)
t-value
Cohen’s d
Anger
2.04 (1.34)
1.70 (1.21)
3.60 *
0.22
Anxiety
1.99 (1.53)
1.38 (1.28)
6.15 *
0.43
Happiness
5.48 (1.58)
5.66 (1.61)
1.22
NA
Sadness
1.55 (1.26)
1.36 (1.18)
1.60
NA
* p < .001
3.3 Learning
Significant gains in learning were observed for all the technology-related skills examined (Table 1).
Effect sizes for most of these changes were considered large according to Cohen [25].
Table 3. Pre-Post Tests for Technology-Based Skills (n=148).
Pre-Test
Mean (SD)
Post-Test
Mean (SD)
t-value
Cohen’s d
Operating System
24.9 (10.1)
32.5 (6.8)
13.3 *
0.88
Communication
23.5 (10.4)
31.9 (7.0)
13.9 *
0.95
Browsing the Web
23.2 ( 8.3)
30.9 (5.4)
14.0 *
1.10
Word Processing
32.0 ( 8.4)
36.6 (5.2)
8.2 *
0.66
Spreadsheets
17.6 ( 6.7)
20.2 (4.8)
6.5 *
0.45
Presentations
10.8 ( 7.4)
19.6 (4.8)
17.4 *
1.41
Graphics
10.8 ( 7.5)
17.7 (5.7)
13.7 *
1.04
Creating Web Page
8.5 (11.4)
27.6 (8.5)
23.1 *
1.90
Coding (n=19)
24.3 (13.8)
29.8 (14.1)
3.1 **
0.40
* p < .001
** p < .005
3488
4 CONCLUSIONS
The results of this study suggest that an integrated approach to learning and using technology in an
eight-month, teacher education program results in significant increases in positive attitudes (behavioural
and self-efficacy) toward technology, a decrease in negative emotions (anger and anxiety) when
learning new software, and improvements in a wide range of technology-based skills. The main
principles guiding the use of technology in the program were cooperative learning, constructivism,
facilitation and coaching, problem-based learning, higher-level thinking skills, connecting concepts to
real-world knowledge, and actively applying knowledge. These principles and their impact are consistent
with the results reported by Tondeur [14] and Kay [2]. However, it is difficult to assess the details,
dynamics and relative impact of the specific components of the integration model used. Future research,
perhaps in the form of interview or focus groups would provide a more in-depth analysis of these
principles. Furthermore, the impact of the model used to integrate technology on how new teachers
actually implement technology in their future classrooms was not assessed. Finally, the pedagogical
beliefs associated with using technology were not evaluated, however, Tondeur et al.’s [26] review of
the literature suggests that these beliefs are intricately linked to the future and effective use of
technology in the classroom. Future research assessing the development of, change in and impact of
pedagogical beliefs associated with technology would be valuable to pursue.
REFERENCES
[1] J. Hattie & S. Clarke, Visible learning: Feedback New York: NY: Routledge, 2009.
[2] R. H. Kay, & L. Knaack, “A case for ubiquitous, integrated computing in teacher
education,” Technology, Pedagogy and Education, vol. 14, no. 3, pp. 391-412, 2005.
[3] R. H. Kay, “Evaluating strategies used to incorporate technology into preservice education: A
review of the literature,” Journal of Research on Technology In Education, vol. 38, no. 4, pp. 383-
408, 2006.
[4] L. Cuban, Oversold and underused: Computers in the classroom. Cambridge, MA: Harvard
University Press, 2009.
[5] J. S. Fu, “ICT in education: A critical literature review and its implications,” International Journal of
Education & Development Using Information & Communication Technology, vol. 9, no. (1), pp.
112-125, 2013.
[6] S. Higgins, Z. Xiao, & M. Katsipataki, “The impact of digital technology on learning: A summary for
the education endowment foundation. Durham, UK: Education Endowment Foundation and
Durham University.
[7] M. Russell, D. Bebell, L. O’Dwyer, & K. O’Connor, “Examining teacher technology use:
Implications for preservice and inservice teacher preparation,” Journal of Teacher Education, vol.
54, no. 4, pp. 297-310, 2003.
[8] J. Young, F. Gorumek, & C. Hamilton, “Technology effectiveness in the mathematics classroom: A
systematic review of meta-analytic research,” Journal of Computers in Education, vol. 5, no. 2, pp.
133-148, 2016.
[9] B. Berrett, J. Murphy, & J. Sullivan, “Administrator insights and reflections: Technology integration
in schools,” The Qualitative Report, vol. 17, no. 1, pp. 200–221, 2012.
[10] D. Nigel, “Spending on instructional tech to reach $19 billion within 5 years”, The Journal, 2014.
Retrieved from https://thejournal.com/articles/2014/06/11/spending-on-instructional-tech-to-reach-
19-billion-within-5-years.aspx
[11] N. Emmanuel, “Education technology is a global opportunity “, Tech Crunch, 2017, Retrieved from
http://www.govtech.com/education/navigator/numbers/2017-education-it-spend-k-12-vs-higher-
ed_70.html
[12] B. Herold, “Technology in education: An overview,” Education Week, 2016, Retrieved from
https://www.edweek.org/ew/issues/technology-in-education/index.html
[13] L. Bennett, “Technology standards for the preparation of teachers,” International Journal of Social
Education, vol. 15, no. 2, pp. 1–11, 2000-2001.
3489
[14] J. Tondeur, J. Van Braak, G. Sang, J. Voogt, P. Fisser, & A. Ottenbreit-Leftwich, “Preparing pre-
service teachers to integrate technology in education: A synthesis of qualitative
evidence,” Computers & Education, vol. 59, no. 1, pp. 134-144, 2012.
[15] A. Ottenbreit-Leftwich, K. Glazewski, T. Newby, & P. Ertmer, “Teacher value beliefs associated
with using technology: addressing professional and student needs,” Computers & Education, vol.
55, pp. 1321–1335, 2010.
[16] D. W. Johnson & R. T. Johnson, Learning together and alone. Cooperation, Competition, and
Individualization (5th ed.). Englewood Cliffs, NJ: Prentice-Hall, 1998.
[17] J. Bruner, Actual minds, possible worlds. Cambridge, MA: Harvard University Press, 1986.
[18] L. S. Vygotsky, Mind in society. Cambridge, M.A.: Harvard University Press, 1978.
[19] A. L. Brown & A. S., & Palinscar, “Guided, cooperative learning and individual knowledge
acquisition,” in Knowing, learning, and instruction (L. B. Resnick eds.), pp. 393-451, Hillsdale, NJ:
Erlbaum Associates, 1989.
[20] M. A. Albanese & S. A. Mitchell, “Problem-based learning: A review of the literature on its
outcomes and implementation issues,” Academic Medicine, vol. 68, pp. 52-81, 1993.
[21] L. B. Resnick, “Introduction,” in Knowing, learning, and instruction (L. B. Resnick eds.), pp. 1-24,
Hillsdale, NJ: Erlbaum Associates, 1989.
[22] R. Sternberg, “Domain-generality versus domain-specificity: The life and impending death of a
false dichotomy, Merrill-Palmer Quarterly, vol. 35, no. 1, pp. 115-130, 1989.
[23] J. B. Carroll, The Nurnberg Funnel. Cambridge, MA: MIT Press, 1990.
[24] R. H. Kay & S. Loverock, “Assessing emotions related to learning new software: The computer
emotions scale,” Computers in Human Behavior, 24(4), pp. 1605-1623, 2008.
[25] J. Cohen, “A power primer,” Psychological Bulletin, vol. 112, no. 1, pp. 155–159, 1992.
[26] J. Tondeur, J. Van Braak, P. Ertmer, & A. Ottenbreit-Leftwich, “Understanding the relationship
between teachers’ pedagogical beliefs and technology use in education: A systematic review of
qualitative evidence,” Educational Technology Research and Development, vol. 65, no. 3, pp.
555-575, 2017.
3490
