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ILEIID 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXII October 2025
The Technologies Used in Self-Directed Mathematics and Statistics
Learning
*1
Bushra Abdul Halim,
2
Siti Nursyahira Zainudin,
3
Siti Ramizah Jama,
4
Nordianah Jusoh @ Hussain,
5
Siti Fairus Fuzi,
6
Nurul Emyza Zahidi,
7
Wan Hartini Wan Hassan
1,2,3,4,5,6,7
Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Melaka
*Corresponding Author
DOI: https://dx.doi.org/10.47772/IJRISS.2025.922ILEIID0021
Received: 22 September 2025; Accepted: 30 September 2025; Published: 22 October 2025
In this digital age, digital technologies are used every time and everywhere by many people in everyday life.
The application of these digital technologies has transformed the education sector. Students have gradually
changed from learners who are teacher-centered to student-centered who are internally motivated knowledge
seekers, perhaps with the help of digital technologies usage. Within this context, self-directed learning (SDL)
has become an essential educational practice, particularly in mathematics and statistics courses, which often
require students to engage in independent problem-solving and conceptual understanding outside the
classroom. Hence, this paper examines the types of digital technologies most frequently used by university
students in self-directed mathematics and statistics learning using descriptive statistics. The study is based on
data from 478 diploma students at a Malaysian public university who enrolled in accountancy and business
management programs. Findings indicate that the most digital devices used by students as communication
tools and social networks was smartphones compared to computers either laptops, tablets, or desktops.
Communication tools, social networking platforms, and internet search engines are the dominant technologies
supporting students' independent learning among the fourteen categories of digital technologies assessed. The
result also shows that the technology familiarity among students was high for web-based technologies such as
Google Docs and Canva. These findings have important implications for educators and institutions seeking to
enhance digital literacy and strengthen the integration of academic technologies into teaching and learning
practices.
Keywords: digital technology, self-directed learning, mathematics/statistics learning, descriptive statistics
INTRODUCTION
The rise of digital technology has transformed education sectors including higher education which enable
students to learn beyond traditional classrooms environments. Even though this generation had grown up with
latest and high-end digitalized technologies, a few researchers such as Brown and Czerniewicz (2010), and
Helsper and Eynon (2010) argued that it was not the generation that matters in describing young people of
today but other more important factors such as the availability and usage of technologies, prior experience,
self-efficacy, and education using the technologies.
The widespread of technology use among university students has made them use the technology in their self-
directed learning. Self-directed learning (SDL) allows learners to take their own initiatives and the
accountability for things that will happen which permit them to choose, organize and evaluate their personal
learning activities that can be done at any time, at any place, and accordingly at their own convenience and
pace (Saeid and Eslaminejad, 2017). In self-directed learning, learners have the control of themselves to learn.
Many students gravitate digital technology as communication and social tools, but the extent to which these
tools contribute to meaningful SDL remains underexplored. Furthermore, most of the research studies have
focused on the overall self-directed learning using technologies, but this present study will focus on the digital
technologies most frequently used by students in their self-directed mathematics and statistics learning.
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Special Issue | Volume IX Issue XXII October 2025
Students were also unfamiliar with educational technologies. According to Ng (2012), students were
unfamiliar with educational technologies could be due to the inconsistent and/or lack of integration of digital
technologies into their learning at schools and at the university levels. Previous studies had shown that
students were not inclined to use digital technologies for their academic purposes but rather they used the
technologies for non-academic purposes such as for social, personal, and leisure activities (Littlejohn et. al,
2010; Corrin et. al, 2010; Yot-Domínguez and Marcelo, 2017 & Ramírez et. al, 2021).
Research Questions
What digital devices and internet services do students most frequently have unlimited access to for academic
purposes?
What types of digital technologies do university students use in self-directed learning for
mathematics/statistics courses?
How do students’ familiarity with academic digital tools differ from their familiarity with non-academic digital
tools as supporting tools learning for mathematics/statistics courses?
LITERATURE REVIEW
Christine (2017) as reported, according to “European Framework for the Digital Competence of Educators”
(DigCompEdu), digital technology is defined as any product or service that can be used to create, view,
distribute, modify, store, retrieve, transmit and receive information electronically in a digital form. Digital
technologies are divided into three main categories (i) digital devices (eg personal computers, mobile devices,
tablet PCs, digital whiteboards, projectors, cameras, electric/electronic circuits, and detectors), (ii) digital
resources that consist of computer networks (eg internet), software (eg programs, apps, virtual environments,
and online games), and online services (eg websites, social networks, online libraries), and (iii) digital content
(eg files, information/data) based on this framework. In mathematics education, students can use technical aids
such as content-specific software, digital materials, and digital devices with mathematical facilities (Cevikbas
et. al, 2023). A study by Akinoso (2018) showed that the use of multimedia positively influenced the academic
performance of students in mathematics.
Self-directed learning (SDL), defined as the ability of learners to plan, implement and evaluate their own
learning activities, is increasingly facilitated through technological tools (Knowles, 1975; Candy, 1991).
Mathematics and statistics subjects often perceived as difficult, hence digital technologies provide learners
with opportunities to access resources, collaborate with peers and reinforce understanding through interactive
platforms. Rashid and Muhammad Asghar (2016) showed that use of technology has a direct positive
relationship with students’ engagement and self-directed learning and according to Al Zahrani et. al (2021),
self-directed learning using technology has become the new normal for college and university students. Fahnoe
and Mishra (2013) reported that self-directed learners became knowledgeable about related resource selection
as well as the management and appropriate usage of the information from opportunities and abilities of
technology-enhanced learning environment. This was supported by Rashid and Muhammad Asghar (2016)
who found that learners using technologies such as email, smartphone, internet, and social media had positive
impacts on their levels of self-directed learning. Ng (2012) showed that by making use of unfamiliar
technologies in their learning, students could learn easily to create useful artefacts provided they had some
degree of digital literacy and the opportunity to use the technologies for meaningful purposes. This suggests
that students’ self-directed learning skills could be enhanced by exposing them with unfamiliar technologies.
METHODOLOGY
This study involved 478 diploma students enrolled in accountancy and business management programs at a
Malaysian public university. A structured online survey was distributed to participants who taking either
mathematics or statistics courses, randomly using cluster sampling technique. The survey included 14
categories of digital technologies measured on a five-point scale (1 = never, 5 = very often). Descriptive
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Special Issue | Volume IX Issue XXII October 2025
statistics were used to analyze the frequency of technology use, with categories defined as high (3.51–5.00),
moderate (2.51–3.50), and low (1.00–2.50) adapted from Yot-Domínguez and Marcelo (2017). This method
allowed for the identification of the most frequently used technologies supporting self-directed mathematics
and statistics learning.
RESULTS AND DISCUSSION
Access to digital technologies for academic purposes
As shown in Table 1, 80.5%, 63.6%, and 28.5% students had unlimited access to smartphones, laptops, and
tablets respectively. Result showed that they had access to laptops and tablets more than desktops. Majority of
the students (59.4%) have unlimited access to the internet via wi-fi while only 15.1% and 23.2% students have
unlimited access via fixed broadband and mobile broadband respectively.
From the findings, it indicates that the use of smartphones as communication tools and social networks was
more frequent than the usage of computers either laptops, tablets, or desktops. The possible explanation is
smartphones are more accessible and of unlimited access to students. Earlier study by Rung et. al (2014)
showed that smartphone skills significantly helped students to learn more independently while Shooriabi and
Gilavand (2017) found that students surfed course-related websites on the internet and shared notes with each
other were the most frequent used activities when using smartphones for learning purposes. However, the
findings of this study did not coincide with Yot-Domínguez and Marcelo (2017) who studied on university
students’ self-regulated learning using digital technologies and found that of all technologies analyzed, internet
information search and instant communication tools were used more frequently compared to social networks.
Table 1 Type of digital devices and internet services access to for academic purposes
Type of digital devices
and internet services
Access to technology (%)
Not
sure
No access
Limited
access
Unlimited access
Desktop
5.4
42.5
16.7
24.5
Smartphone
-
0.8
14.9
80.5
Laptop
-
1.9
25.3
63.6
Tablet
5.0
39.5
16.9
28.5
Fixed broadband
14.2
49.2
11.7
15.1
Mobile broadband
13.2
42.7
11.9
23.2
Wireless Fidelity (Wi-Fi)
2.7
13.2
18.8
59.4
Type of technologies used in self-directed mathematics/statistics learning
Types of digital technologies used
Mean
SD
Mode
Degree of
frequency
usage
Minimum
Maximum
Internet (eg search engines)
4.18
0.830
5
High
1
5
Social networks (eg Facebook,
Instagram, X, TikTok, etc)
4.24
0.916
5
High
1
5
Communication tools (eg
WhatsApp, Telegram, etc)
4.59
0.672
5
High
2
5
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Storage tools (eg Google Drive,
Dropbox)
3.70
1.039
4
High
1
5
Repositories (eg UFuture, Google
Classroom)
4.15
0.902
5
High
1
5
Online discussion tools (eg
Webex)
3.21
1.181
3
Moderate
1
5
Assessment tools (eg Quizizz)
3.65
1.050
4
High
1
5
Real-time chat (eg Facebook
Messenger, Microsoft Teams)
2.96
1.295
3
Moderate
1
5
Online calendar tools (eg Google
Calendar, Doodle)
2.91
1.103
4
Moderate
1
5
Mobile apps
3.59
0.642
3
High
1
5
Multimedia resources (e.g. videos
on lecturers’ presentations,
mathematics/statistics apps on
related contents, podcasts)
3.27
0.926
3
Moderate
1
5
Computer (e.g. laptop, tablet)
3.59
0.914
4
High
1
5
Digital camera
3.24
1.378
5
Moderate
1
5
File/data/information (eg
Wikipedia)
3.51
1.194
4
High
1
5
Overall
3.63
0.657
High
1
5
Table 2 Descriptive statistics of digital technologies used for self-directed learning
As shown in Table 2, it is revealed that on average, students do use digital technologies in self-directed
learning and the level of usage is at high level (mean = 3.63, SD = 0.657).
Among the 14 technologies assessed, nine of these reached an average level of high frequent use, five of these
reached an average level of moderate frequent use, and none reached a level of low frequent use. The top three
most frequently used technologies were: communication tools such as WhatsApp or Telegram (mean = 4.59,
SD = 0.672), social networks such as Facebook, Instagram, Twitter, or TikTok (mean = 4.24, SD = 0.916), and
internet search engines such as Google or Bing (mean = 4.18, SD = 0.830). Other high-frequency tools
included repositories such as UFuture (the university learning management system) or Google Classroom
(mean = 4.15, SD = 0.902), cloud storage platforms such as Google Drive or Dropbox (mean = 3.70, SD =
1.039), and assessment tools such as Quizizz (mean = 3.65, SD = 1.050).
Digital technologies with level of moderate use were storage tools such as multimedia resources like videos on
lecturers’ presentation, mathematics/statistics apps on related contents (mean = 3.27, SD = 0.926), digital
camera (mean = 3.24, SD = 1.378), online discussion tools such as Webex (mean = 3.21, SD = 1.181), real-
time chat (mean = 2.96, SD = 1.295), and online calendar tools such as Google Calendar (mean = 2.91, SD =
1.103). These data suggest that students use high frequency of smartphones compared to laptops, tablets, or
desktops
and among the computers used, laptops ranked the first, followed by tablets, and then only desktops (refer to
Table 2). On average, frequency use of all types of digital technologies was higher among students attending
statistics classes (mean = 3.66, SD = 0.625) compared to students attending mathematics classes (mean = 3.57,
SD = 0.708), however the differences were not significant (t = -1.440, p > 0.05).
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From Table 2, students reported that on average the frequency use was 3.63 (SD = 0.657), a high level of
frequency use implying that students frequently used some types of digital technologies for their self-directed
learning.
Table 3 Students’ familiarity with some selected digital technologies
Digital
technologies
Mean
SD
Mode
Minimum
Maximum
Familiarity
with the
technology
Category of
digital
technologies
Mean
(Familiarity
level)
SD
Google docs
4.23
0.807
5
2
5
High
Web-based
technology
2.78
(Moderate)
1.080
Mathematical
websites
2.27
1.168
1
1
5
Low
Web-based
technology
Graphing
calculator
2.32
1.200
2
1
5
Low
Web-based
technology
Jamboard
2.18
1.116
1
1
5
Low
Web-based
technology
Canva
4.46
0.778
5
1
5
High
Web-based
technology
Prezi
1.94
1.077
1
1
5
Low
Web-based
technology
Dropbox
2.05
1.099
1
1
5
Low
Web-based
technology
Mathematical
apps
3.15
1.126
3
1
5
Moderate
Non-web-based
technology
2.85
(Moderate)
0.230
Statistical
apps
2.85
1.175
3
1
5
Moderate
Non-web-based
technology
Movie maker
2.59
1.232
2
1
5
Moderate
Non-web-based
technology
Photoshop
2.81
1.137
2
1
5
Moderate
Non-web-based
technology
Web Quest
2.02
1.065
1
1
5
Low
Technological
concept
2.51
(Moderate)
0.315
E-portfolio
2.52
1.212
2
1
5
Moderate
Technological
concept
Podcast
2.69
1.128
2
1
5
Moderate
Technological
concept
Wiki
2.83
1.203
2
1
5
Moderate
Technological
concept
Blog
2.74
1.132
2
1
5
Moderate
Technological
concept
Cloud
computing
2.25
1.113
2
1
5
Low
Technological
concept
Overall
2.70
0.752
2.24
1.24
5
Moderate
One of the purposes of the study was to determine the digital technologies use in self-directed learning. It was
revealed that students did use digital technologies in their self-directed learning, and the level of usage is at
high level.
Comparison of students’ familiarity with academic and non-academic digital tools as supporting tools in self-
directed mathematics/statistics learning
Table 3 shows a list of some selected digital technologies categorized as web-based technology, non-web-
based technology, and technological concept. These technologies were selected from a list of items that were
discussed and used in mathematics/statistics courses. The web-based technologies surveyed were google docs,
mathematical websites, graphing calculator, jamboard, Canva, Prezi, Dropbox, and non-web-based
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technologies were mathematical apps, statistical apps, movie maker, and Photoshop. The technological
concepts covered were web quest, e-Portfolio, podcast, wiki, blog, and cloud computing.
Familiarity was based on whether they have heard about the technology and they have very frequently or
frequently used it (high familiarity: mean score 3.51 – 5.00), whether they have heard the technology but
sometimes used it (moderate familiarity: mean score 2.51 – 3.50), or whether they have heard the technology
but never used it or never heard the technology (low familiarity: mean score 1.00 – 2.50).
Only two technologies with high familiarities, eight technologies with moderate familiarities, and seven
technologies with low familiarities. Digital technologies with high familiarities were google docs (mean =
4.23, SD = 0.807) and Canva (mean = 4.46, SD = 0.778) while the technology with moderate familiarities
were mathematical apps (mean = 3.15, SD = 1.126), statistical apps (mean = 2.85, SD = 1.175), Photoshop
(mean = 2.81, SD = 1.137), podcast (mean = 2.69, SD = 1.128), wiki (mean = 2.83, SD = 1.203), blog (mean =
2.74, SD = 1.132), movie maker (mean = 2.59, SD = 1.232), and e-Portfolio (mean = 2.52, SD = 1.212).
Digital technologies with low familiarities were graphing calculator (mean = 2.32, SD = 1.200), mathematical
websites (mean = 2.27, SD = 1.168), cloud computing (mean = 2.25, SD = 1.113), jamboard (mean = 2.18, SD
= 1.116), Dropbox (mean = 2.05, SD = 1.099), web quest (mean = 2.02, SD = 1.065), and Prezi (mean = 1.94,
SD = 1.077). The overall mean familiarity of the technologies was 2.70 (SD = 0.752) indicating the
familiarity of the technologies was at moderate level ranging from 1.24 to 5.00. Overall, students were
moderately familiar with the surveyed web-based technologies (mean = 2.78, SD = 1.080), non-web-based
technologies (mean = 2.85, SD = 0.230), and technological concepts (mean = 2.51, SD = 0.315). Table 3
presents the results.
Hence, technology familiarity among students was high for web-based technologies such as Google Docs and
Canva. The plausible explanation is these technologies were being used regularly by their lecturers in the class
learning sessions and they tried to use the technologies in their self-directed learning. However, for other
surveyed web-based technologies such as mathematical websites, graphing calculator, jamboard, Prezi, and
Dropbox, the familiarity for these technologies were low. For example, students were more familiar with
Microsoft PowerPoint than Prezi for preparing presentations. Lecturers should use mathematical websites and
graphing calculator in the class learning session for mathematics and statistics to encourage and to scaffold
students’ usage of these technologies in their self-directed learning. According to Ng (2012), if students were
given the opportunity to engage with a purpose for adopting digital tools, they were able to use the tools to
create meaningful products following their needs.
CONCLUSION
As a conclusion, this study found that communication tools, social networks, and internet search engines are
the most frequently used technologies in self-directed mathematics and statistics learning. These findings
highlight students' reliance on accessible, everyday tools for academic purposes, while the use of formal
academic platforms remains secondary. Recommendations include integrating popular communication tools
into formal instruction, strengthening students' digital literacy and designing interventions that encourage the
use of academic technologies. Future research could explore the strategies of self-directed learning using
digital technologies, difference of socio-demographic effect the strategies of self-directed and learning
outcomes.
ACKNOWLEDGEMENTS
The authors would like to thank Universiti Teknologi MARA (UiTM) Cawangan Melaka for supporting this
article.
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