Examining the Role of Emotions in Learning with Technology - Journal of Digital Life and Learning

Abstract

Many theorists suggest that emotions and learning are highly interconnected, however, research on the impact of emotions is limited. This study explored the emotions of 220 pre-service teachers while they learned new technology tools and the relationship of these emotions with technology experience and preferred learning strategies. Happiness was most often expressed while learning with technology, followed by anxiety, anger and sadness. Technology experience was positively correlated with happiness and negatively correlated with anxiety, sadness and anger. Experimental and authentic learning strategies were positively correlated with happiness and negatively correlated with anger, anxiety and sadness. Direct instruction was positively correlated with happiness, negatively correlated with anger and unrelated to anxiety and sadness. Finally, a social learning strategy was positively correlated with anxiety and unrelated to happiness, anger and sadness. Implications for and for practice and suggestions for future research are discussed.

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Examining the Role of Emotions in Learning
with Technology
ROBIN KAY1
1 Ontario Tech University
Abstract
Many theorists suggest that emotions and learning are highly interconnected,
however, research on the impact of emotions is limited. This study explored the
emotions of 220 pre-service teachers while they learned new technology tools and
the relationship of these emotions with technology experience and preferred
learning strategies. Happiness was most often expressed while learning with
technology, followed by anxiety, anger and sadness. Technology experience was
positively correlated with happiness and negatively correlated with anxiety, sadness
and anger. Experimental and authentic learning strategies were positively
correlated with happiness and negatively correlated with anger, anxiety and
sadness. Direct instruction was positively correlated with happiness, negatively
correlated with anger and unrelated to anxiety and sadness. Finally, a social
learning strategy was positively correlated with anxiety and unrelated to happiness,
anger and sadness. Implications for and for practice and suggestions for future
research are discussed.
Keywords: Emotion, Academic emotions, Learning, Technology
INTRODUCTION
echnology use is ubiquitous in Western society, and users need to adjust to the constantly
and rapidly changing landscape of software and apps (Berman, 2015). Many individuals
attempt to learn new technology tools independently (Bartholomé et al., 2006). Given the
pressure of having to adapt so often and so quickly, it is reasonable to assume that people will
experience emotional reactions such as anger, desperation, anxiety, or relief while learning
(Camacho-Morles et al., 2021; Lee & Chei, 2020; Oatley & Johnson-Laird, 1987; Putwain et al.,
2022; Tang et al., 2021). To date, the primary emotion studied with respect to computer-related
behaviour has been anxiety or fear (e.g., Gaudron & Vignoli, 2002; Wilfong, 2006). Higher anxiety
experienced before or during learning is generally associated with lower technology skill
development and performance (Hong & Koh, 2002).
Several theorists maintain that emotions and cognition are inextricably linked and
integrated with systems in the brain (LeDoux, 1989; O’Regan, 2003). Emotion is critical for
adapting to unpredictable environments or juggling multiple goals (Oatley & Johnson-Laird,
T

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1987). An emotional reaction is typically experienced when one is in danger of harm, threatened,
or challenged (Lazarus, 1991). Emotions can also be activated by motivation, engagement or
success (Rolls, 1990). Furthermore, emotions can influence a wide range of cognitive processes,
such as attention, memory storage and retrieval, and problem-solving (Lee & Chei, 2020; Pekrun
& Stephens, 2012). Finally, emotions influence the self-regulation of learning – goal setting,
monitoring the learning process, deploying strategies, and evaluating outcomes (Pekrun &
Stephens, 2012).
Historically, researchers have sidestepped emotions when examining the learning process.
However, since 2002, theorists have recognized that emotions are essential to academic
achievement (Camacho-Morles et al., 2021; Henritius et al., 2019; Lee & Chei, 2020; Pekrun &
Stephens, 2012). Nonetheless, research on the role of emotions in learning is somewhat limited
and complex (Artino & Jones, 2012; Camacho-Morles et al., 2021; Henritius et al., 2019; Pekrun
& Stephens, 2012). Predictably, positive emotions can lead to positive achievement outcomes and
negative emotions can result in disengagement and boredom (Pan et al., 2022). However,
sometimes, positive emotions can have a negative impact on learning (Wu & Yu, 2022), and
negative emotions can lead to positive learning outcomes (Hilliard et al., 2020). Most research,
though, has focused on emotions associated with discrete mood states as opposed to emotions
experienced in the moment of learning (Butz et al., 2015; Loderer et al., 2020; Pekrun & Stephens,
2012).
Izard et al. (1984) argued that one’s emotional state before learning might affect cognitive
results. Emotions, though, often emerge throughout the learning process. Most learning involves
a series of goals, and emotions typically occur when the process of achieving these goals is
interrupted (Mandler, 1984). Depending on the severity, these interruptions can result in
frustration, anxiety or even despair. Positive emotions are typically associated with success, and
negative emotions are linked to challenge and difficulty (Butz et al., 2015; Loderer et al., 2020). It
is reasonable to assume, then, that emotions could play a role in determining how people learn and
how much knowledge they acquire. For example, excessive negative emotions may slow down or
hinder the learning process. On the other hand, positive emotions might build confidence and self-
efficacy and encourage users to attempt and persist in new learning opportunities (Bandura, 1982).
A wide range of emotions has been observed when students are learning with technology,
including enjoyment, hope, pride, anger, anxiety, shame, guilt, helplessness, and boredom (Butz
et al., 2015; Loderer et al., 2020; Wu & Yu, 2022). Satisfaction, enjoyment, and engagement are
the most common positive emotions experienced (Henritius et al., 2019). Anxiety is the most
frequently experienced negative emotion, although research on negative emotions is somewhat
limited (Loderer et al., 2022; Pekrun & Stephens, 2012).
At least two systematic reviews have been conducted on the role of emotions and learning
with technology (Loderer et al., 2020; Wu & Yu, 2020). Loderer et al. (2020), in a systematic
review of the literature and meta-analysis, explored emotions and learning in a technology-based

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environment. They supported a universal approach to examine the role of emotions, regardless of
the learning environment. Wu & Yu, (2022) conducted a systematic review of the role of emotions
in learning but focused on motivation, engagement, satisfaction and performance in online
environments. Neither review emphasized the direct impact of emotions during the learning
process.
At least four studies have explicitly explored how emotions impact technology learning
and use (Kay, 2007, 2008; Kay & Loverock, 2008; Pekrun & Stephens, 2012). Kay & Loverock
(2008) established the reliability and validity of the Computer Emotion Scale and observed
significant correlations among emotions, computer knowledge and use. Specifically, positive
emotions were significantly and positively correlated with better technology skills and increased
use of computers. Negative emotions were significantly and negatively correlated with higher
technology skills. Kay (2007) reported that increases in positive emotions and decreases in
negative emotions were significantly related to teachers’ use of computers in the classroom. Kay
(2008) observed that happiness and anxiety (but not anger and sadness) were significantly
associated with changes in computer knowledge. Finally, Pekrun & Stephens (2012) noted that the
quality and design of instruction could help mitigate negative emotions, however, this area of
research needs to be investigated in more detail.
In summary, previous research suggests that emotions are integral to learning, regardless
of the mode of delivery. However, the role of emotions in learning is complex, and there is a clear
gap regarding emotions expressed during the learning process and how these emotions relate to
instruction.
PURPOSE OF STUDY
The purpose of the current study was to further examine the role of emotions in learning
with technology. Specifically, this study examined the range of emotional expression while
learning new software, the relationship between previous technology experience and the
manifestation of emotions while learning, the relationship between emotions and preferred
learning strategies, and the connections between emotions and technology-related skills.
METHOD
Participants
The participants in this study consisted of 220 pre-service teachers (176 females, 44 males)
teaching grades 1 to 6 (n=143) or grades 7 to 12 (n=77). Technology experience ranged from 0 to
26 years (M =10.0, SD = 4.1). All pre-service teachers had previously attained a Bachelor of Arts
or Bachelor of Science degree. Each student had a laptop or mobile device with ubiquitous, high-
speed internet access throughout the course.

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Context
The study occurred at a Canadian university in a suburban area with almost 650,000
residents. The Bachelor of Education degree at this university was an eight-month program using
an integrated model to incorporate technology. Pre-service teachers used their mobile devices in
all courses offered but did not take a stand-alone course in technology use. All pre-service teachers
participated in a four-hour introduction to technology workshop. In addition, two-hour technology-
learning sessions were offered throughout the program on various topics. All faculty members
created assignments and projects that required students to use technology to solve meaningful,
practical, and useful problems. Finally, one support person was available four hours per day, five
days a week, to assist students with individual problems.
Data Collection
To assess emotions while learning with technology, we assessed four theoretically distinct
constructs based on Oately & Johnson-Laird’s (1987) basic emotion categories: happiness (4
items), anger (3 items), anxiety (3 items) and sadness (4 items). A seven-point Likert scale
(Strongly Disagree, Disagree, Slightly Disagree, Neutral, Slightly Agree, Agree, Strongly Agree)
was administered to assess emotions that pre-service teachers experienced while learning with
technology. Table 1 lists the items for each emotion construct.
The internal reliability estimates for all constructs were considered acceptable (Kline,
1999; Nunally, 1978), ranging from 0.77 to 0.82. The construct validity of this scale was
established by Kay (2008) and Kay & Loverock (2008).
Table 1
Emotion Construct Scale Items
Happiness
Anger
Anxiety
Sadness
1. Satisfied
1. Irritable
1. Anxious
1. Like Crying
2. Excited
2. Frustrated
2. Insecure
2. Disheartened
3. Curious
3. Annoyed
3. Nervous
3. Sad
4. Interested
4. Dispirited
To determine strategies for acquiring new technology skills, four constructs were
established based on 17 four-point Likert scale questions (No Help, Somewhat Helpful, Helpful,
Very Helpful). Learning strategies included experimenting (4 items, r=0.77), social learning (4
items, r=0.71), direct instruction (5 items, r=0.68), and authentic practice (4 items, r=0.74). Table
2 lists the items for each learning strategy construct. Construct validity for this scale was presented
by Kay (2007).

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Table 2
Scale Items for Strategies Used to Learn with Technology
Experiment
Social Learning
Direct Instruction
Authentic Activity
1. Trial & Error
1. Learn with Peers
1. Workshops
1. Assignments
2. No Books or
Tutorials
2. Email Questions
2. Using Help
Features
2. Teaching
3. Playing
3. Working with a
Friend
3. Online Tutorials
3. Planning,
Research,
Organization
4. Searching Menu
4. Asking Questions
in Person
4. Online Videos
4. Real-world
Tasks
To measure computer knowledge and skills, nine areas were examined: operating systems
(10 items, r=0.91), communication (9 items, r=0.87), web browsing (9 items, r=0.89), word
processing (10 items, r=0.93), spreadsheets (6 items, r=0.91) databases (10 items, r=0.94),
graphics (6 items, r=0.90), presentations (8 items, r=0.81), and webpage design (10 items, r=0.94).
The internal reliability estimates for all constructs were considered acceptable (Kline, 1999;
Nunally, 1978). The construct validity and detailed descriptions of these computer knowledge
constructs are presented by Kay & Knaack (2005).
Procedure
Pre-service teachers were told the purpose of the study and then asked to provide written
consent if they wished to participate. We administered the anonymous online survey at the
beginning of the year (September) during an orientation meeting and at the end of the year (April)
culminating meeting. It took about 10-15 minutes to complete.
Research Questions
We addressed three research questions in this study:
1. What emotions do preserve teachers express when learning new technologies?
2. How are emotions expressed while learning related technology experience and skills?
3. How are emotions expressed while learning related to preferred strategies for learning new
technologies?
RESULTS
Emotions Expressed While Learning with Technology
Happiness was the most frequent emotion identified while learning, with interest and
curiosity being the highest-ranked items within that construct (Table 3). Anxiety was the next most
frequent emotion expressed, with 31% to 45% of pre-service teachers agreeing that they felt

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insecure, nervous, or anxious while learning with technology. Anger was experienced as often as
anxiety, with 21% to 44% of pre-service teachers agreeing that they felt frustrated and, to a lesser
extent, irritable and annoyed while learning. Finally, sadness was the least experienced emotion,
with 3% to 16% of pre-service teachers agreeing that they felt dispirited, disheartened, or like
crying while learning with technology.
Table 3
Frequency of Emotions Expressed While Learning with Technology (N=212)
Emotion
Mean (SD)1
Percent Disagree2
Percent Neutral
Percent Agree3
Happiness
Interested
5.9 (0.8)
1%
3%
97%
Curious
5.8 (0.9)
1%
6%
93%
Excited
5.1 (1.1)
6%
21%
73%
Satisfied
5.1 (1.2)
12%
16%
72%
Anger
Frustrated
3.8 (1.6)
36%
20%
44%
Irritable
3.3 (1.7)
53%
16%
31%
Annoyed
3.0 (1.6)
60%
18%
21%
Anxiety
Anxious
3.8 (1.6)
40%
15%
45%
Nervous
3.4 (1.7)
51%
12%
37%
Insecure
3.2 (1.7)
53%
16%
31%
Sadness
Like Crying
2.7 (1.5)
68%
16%
16%
Disheartened
2.6 (1.3)
68%
24%
8%
Dispirited
2.1 (1.2)
82%
15%
3%
Sad
1.8 (1.0)
91%
9%
0%
1 Based on a seven-point Likert Scale.
2 Disagree = Strongly Disagree, Disagree, and Slightly Disagree responses.
3 Agree = Strongly Agree, Agree, and Slightly Agree responses.
Emotions, Experience, and Technology Skill Level
Experience. Years of experience using technology correlated significantly and positively
with the expression of happiness (r(210) =0.17, p <.05). In other words, the more experience a pre-
service teacher had with technology, the more likely they would feel positive emotions while

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learning new software tools. Years of experience using technology was significantly and
negatively correlated with the expression of anxiety (r(210) =-0.30, p <.05), sadness (r(210) =-
0.29, p <.05) or anger (r(210) = -0.21, p <.05). Less technology experience, then, was related to
more frequent expressions of anger, anxiety, or sadness.
Technology Skill. The expression of happiness while learning new technology correlated
significantly and positively with higher knowledge ratings for all nine technology skills assessed.
On the other hand, anger and sadness correlated significantly and negatively with higher ratings
for all nine technology skills assessed. Anxiety correlated significantly and negatively with eight
of nine measured technology skills (Table 4).
Table 4
Correlation between Emotions and Technology Skills
Construct
Happiness
Anger
Anxiety
Sadness
Operating System
0.48 ***
-0.42 ***
-0.40 ***
-0.47 ***
Communication
0.33 ***
-0.35 ***
-0.30 ***
-0.44 ***
Web Browsing
0.32 ***
-0.34 ***
-0.33 ***
-0.39 ***
Word Processing
0.22 **
-0.19 *
-0.14
-0.18 *
Spreadsheets
0.39 ***
-0.31 ***
-0.33 ***
-0.27 ***
Databases
0.34 ***
-0.41 ***
-0.37 ***
-0.40 ***
Graphics
0.39 ***
-0.33 ***
-0.35 ***
-0.39 ***
Presentations
0.38 ***
-0.30 ***
-0.32 ***
-0.28 ***
Webpage Design
0.31 ***
-0.16 *
0.37 ***
-0.42 ***
*** p < .001
** p < .005
* p < .05
Emotions and Learning Strategies
Experimental Strategy. Experimental learning correlated significantly and positively
with the expression of happiness. This approach to learning was also significantly and negatively
correlated with all three negative emotions. The correlation coefficients for all emotions were
relatively high, indicating that pre-service teachers experienced a wide range of emotions with an
experimental approach to learning technology (Table 5).
Authentic Strategy. Learning new technology while completing authentic tasks correlated
significantly and positively with the expression of happiness. The authentic approach correlated
significantly and negatively with all three negative emotions. The correlation coefficients for
happiness were relatively high and similar to those observed in the experimental approach.

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However, coefficients for the negative emotions were lower than those seen for the experimental
learning approach (Table 5).
Direct Instruction Strategy. Direct instruction correlated significantly and positively with
the expression of happiness, although the magnitude of the coefficient was relatively low. In
addition, direct instruction correlated significantly and negatively with the expression of anger;
however, the coefficient was small. The presentation of anxiety and sadness did not significantly
correlate with using a direct instruction strategy (Table 5).
Social Interaction Strategy. Using a social interaction strategy for learning new
technology was significantly and positively correlated with anxiety. This approach did not
significantly correlate with happiness, anger, or sadness (Table 5).
Table 5
Correlation between Emotions and Learning Strategies Used
Construct
Happiness
Anger
Anxiety
Sadness
Experimental
0.40 ***
-0.33 ***
-0.45 ***
-0.35 ***
Authentic
0.43 ***
-0.27 ***
-0.24 ***
-0.24 ***
Direct Instruction
0.19 **
-0.16 *
0.03
-0.07
Social
0.01
0.01
0.26 ***
0.08
*** p < .001
** p < .005
* p < .05
DISCUSSION
Expressions Expressed While Learning
In this study, happiness (interest and curiosity) was the emotion experienced most often by
pre-service teachers when learning with technology. Anger and anxiety were also felt, but not
nearly as frequently, and sadness was experienced least often. These findings are consistent with
the claim that emotions are linked to cognition and learning (LeDoux, 1989; O’Regan. 2003).
However, with this group of pre-service teachers, positive feelings and presumably success were
experienced far more often than negative feelings and challenges. The ratio of positive and
negative emotions while learning when learning with technology has not been examined
previously and would be worth exploring in the future to assess their relative impact. For example,
do users acknowledge, feel, or respond to negative feelings more than positive feelings, as
predicted by Peeters & Czapinski (1990)?
Emotions and Experience with Technology

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Emotions, Experience, and Technology Skills. While one would expect that both positive
and negative emotions are regularly expressed when learning with technology, it is interesting that
these emotions are significantly and systematically related to technology experience and
knowledge acquired on a wide range of technology skills. Positive emotions are associated with
higher scores on technology skills, and negative emotions are associated with lower scores. At first
glance, the results seem reasonable: successful learning experiences lead to a happier state of mind,
and unsuccessful learning experiences lead to a more negative reaction. Recall, though, that
negative emotions emerge when there are challenges or interruptions to achieving one’s learning
goal (Mandler, 1984). These reactions alert the learner to a problem, increase focus, and eventually
overcome the challenge. However, in this study, negative emotions might have somehow impeded
the learning process. Future research is needed, perhaps in the form of observational analysis or
think-aloud protocols (e.g., where participants talk aloud while the learning process is recorded),
to determine whether emotions caused or were a reaction to lower performance.
Emotions and Learning Strategies. Participants who identified happiness as the primary
emotion they experienced when learning with technology were significantly more comfortable
with experimental learning and engaging with authentic tasks than pre-service teachers who
expressed anger, anxiety or sadness. The more open-ended nature of an experimental learning
approach or gaining knowledge through authentic tasks may require confidence and a certain
mindset associated with positive emotions like interest or curiosity. It is possible that these pre-
service teachers who experienced happiness had a growth mindset associated with effort and
building knowledge as opposed to a fixed mindset related to performance and fear of failure
(Dweck, 2006). It is also reasonable to surmise that students who experience more positive than
negative emotions might be more capable of using technology and, therefore, more able to handle
the cognitive load of a less structured learning approach. Future research could investigate growth
mindset and emotions expressed while learning with new technology. In addition, observing and
asking questions while individuals are learning a new software tool could provide a more in-depth
understanding of how emotions connect with the learning process.
LIMITATIONS AND FUTURE RESEARCH
This study offers a preliminary analysis of the role of emotions in learning with technology.
However, there are a number of limitations leading to opportunities for future research. First, while
the scale used to assess emotions was reliable and valid, a wider range of emotions could be
evaluated in future research endeavours. For example, Pekrun et al. (2011) developed the
Achievement Emotions Questionnaire focusing on enjoyment, hope, pride, relief, anger, anxiety,
shame, hopelessness, and boredom during class, while studying, and when taking tests and exams.
While this scale is related to performance more than learning, it offers a broader range of emotional
constructs leading to a more in-depth analysis of academic or learning emotions.
Second, surveys were used in this study to assess emotions expressed while learning. While
this approach offers a general impression of emotions displayed while learning, it relies on a

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student’s memory and does not examine emotions in real-time. One promising alternative is using
a multimodal wearable device such as a band or watch to assess and diagnose emotional states
while learning (Mehmood & Lee, 2017). A review of the research on the benefits and challenges
of using wearable devices suggests that students can benefit from feedback on their emotions while
learning (Jovanovic & Kay, 2021). Wearable devices could also help improve the quality and lens
of research by examining how emotions impact learning in real-time.
Third, learning in this study was evaluated based on self-report data about technology
experience and skills. While this approach has proven reliable and valid (e.g., Kay, 2007, 2008),
it does not offer a window into the learning process. Future research using wearable devices in
conjunction with think-aloud protocols would provide a more detailed and thorough analysis of
academic emotions when learning new technological tools.
Fourth, only four learning strategies were examined in this study. A more precise data
collection approach (e.g., wearable devices and think-aloud protocols) could reveal alternative
learning approaches, including searching the web and watching YouTube videos.
Finally, this study examined pre-service teachers. A broader range of participants could be
explored, including K-12 students. Furthermore, individual differences in emotional expression
could be assessed based on factors such as gender and the subject area where technology is used.
EDUCATIONAL IMPLICATIONS
As stated earlier, this study is a preliminary investigation of the role of emotions in learning
with technology. Therefore, any proposed educational implications should be treated cautiously.
Nonetheless, the results of this study, in combination with research using wearable devices, could
lead to significant changes in learning and educational practice. First, with wearable devices and
appropriate artificial intelligence data, learning could be altered and augmented by real-time
feedback when learning new technology. A recent review of the literature on wearable devices
revealed that feedback on emotional reactions encourages self-reflection, makes learning more
engaging and relevant, improves the quality of learning, and supports differentiated instruction
(Jovanovic & Kay, 2021).
Second, feedback on emotions could lead to greater self-awareness and provide guidance
on which learning strategies are most beneficial. For example, if students become aware of
increased anxiety while learning in social groups, they could use emotional feedback to switch
strategies. Alternatively, if a student is stuck in a fixed loop where they keep trying the same
strategy repeatedly, real-time emotional feedback could help them break out of the cycle of
repetition and try something new.
Third, increasing self-awareness of emotional reactions while learning new technology
could shift the learning mindset. For example, when students experience increased anxiety when
using a trial-and-error exploratory learning approach, they might have a fixed mindset of learning.

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This mindset may inhibit learning due to a fear of making mistakes and feeling not good enough.
Emotional feedback from a wearable device could bring about awareness of this fixed mindset.
Appropriate scaffolding and guidance from a teacher or artificial intelligence avatar could help
shift the student to a growth mindset.
Fourth, a closer examination of the effect of negative emotions could lead to a deeper dive
into how learners develop resilience. to mistakes, errors and failures (Johnson et al., 2017).
Negative emotions could offer a window into how resilience or emotional distress evolves during
the learning process. Understanding this evolution could bring about awareness that allows a
learner to cope with and grow when challenges emerge.
Finally, some research suggests that emotional feedback data can help instructors modify
and revise instructional strategies (Jovanovic & Kay, 2021). Emotional feedback recorded and
stored at the class level could provide valuable guidance on which teaching strategies increase or
decrease anxiety, engagement, and focus. This feedback could be used in face-to-face classrooms
or in the design of online modules.
SUMMARY
Based on self-reported survey data, this study examined pre-service teachers' emotional
reactions while learning new technology tools. Happiness while learning was experienced most
often, followed by anxiety, anger, and sadness. Technology experience and skills level was
positively correlated with happiness and negatively correlated with anxiety, sadness, and anger.
Different instructional strategies yielded unique emotional reactions. While the results of this study
indicate that emotional responses are linked to technological ability and preferred learning
strategies, a more precise and accurate measure of emotions and learning is required to further
understand the impact of emotions on learning new technology.
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AUTHOR
Dr. Robin Kay is the Dean of and a Full Professor in the Faculty of Education at Ontario Tech
University. He has published over 160 articles, chapters, and conference papers in pedagogy
technology in education. As an educator, Robin has over 25 years of experience teaching
computer science, mathematics, learning and development, and educational technology at the
high school, college, undergraduate, and graduate levels. His research focuses on factors
influencing student learning and digital technology, including wearable tech, web-based
learning tools, learning environments, scale development, and BYOD learning. Dr. Kay received
his M.A. in Computer Applications in Education at the University of Toronto and his Ph.D. in
Cognitive Science (Educational Psychology) at the University of Toronto.
Corresponding Author:
Dr. Robin Kay
robin.kay@ontariotechu.ca