International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923 – Vol  17 No  13 (2023) Paper—Effects on Saudi Female Student Learning Experiences in a Programming Subject Using Mobile… Effects on Saudi Female Student Learning Experiences in a Programming Subject Using Mobile Devices: An Empirical Study https://doi.org/10.3991/ijim.v17i13.38439 Afrah Alanazi1(), Alice Li2, Ben Soh1 1 Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia 2 Department of Management, Sport and Tourism, La Trobe University, Melbourne, Australia aoalenzy@ju.edu.sa Abstract—There is a dearth of research on how mobile learning (m-learning) could help Saudi female students perform better in computer programming courses. This paper measures the educational experiences of Saudi female stu- dents using an empirical study based on a framework for m-learning in a com- puter programming subject. The empirical study sample was 21 female students who used the ViLLE software tool in their quizzes in a programming class. This study used a quantitative survey, including some open-ended questions, to collect data. The study results showed that students were satisfied with their m-learning experience. The degree of satisfaction demonstrates how mobile devices can en- hance learning in a computer programming subject. Keywords—programming, mobile learning, learning experience, Saudi Arabia 1 Introduction Traditional teaching methods, such as taking notes on paper, are used by Saudi fe- male computer science students. These methods are less engaging than learning with technology. In contrast, Saudi male students are permitted to use gadgets like comput- ers and mobile phones, allowing greater interactions and better comprehension of the material [1]. This has led to a digital divide in computer science between the two gen- ders [2]. Papadakis [3] emphasises that sexism in education can be reduced with proper teaching methods. To encourage better classroom setups, Saudi Arabian institutions have recently started permitting female students to bring their devices to class1 to make learning topics like computer science and programming more fascinating and engaging for them. As the vast majority of Saudis own mobile devices, mobile-based learning and teaching methodologies have become easier to use [4], [5]. Since both lecturers and students can access these tools for better communication, integrative strategies are used in colleges where women 1 https://english.pravda.ru/news/world/138816-saudiwomen/ 44 https://www.i-jim.org https://doi.org/10.3991/ijim.v17i13.38439 https://english.pravda.ru/news/world/138816-saudiwomen/ Paper—Effects on Saudi Female Student Learning Experiences in a Programming Subject Using Mobile… predominate, supporting a hands-on approach to improve learning comprehension [6]. Many studies [7], [8], [47] have demonstrated the various benefits of mobile learning (m-learning) in computer programming. Female-only universities in Saudi Arabia must adopt cutting- edge teaching and learning strategies, such as m-learning. No studies have been pub- lished on Saudi female computer programming students’ exposure to m-learning. Thus, this study is guided by the following question: • Has the proposed evaluation framework, Mobile Learning for Computer Program- ming (MoLeCoP), helped to improve female student performance in computer pro- gramming subjects by the end of the semester? The primary objective of this paper is to assess the learning experience of students in the treatment group and evaluate the adoption of the MoLeCoP framework on the degree of satisfaction among Saudi female programming students. The remaining sections in the paper provide a literature review, the proposed theoretical framework, the study method, the results and a discussion. 2 Literature review The influence of a program, a conversation or any other activity that involves learn- ing is often referred to as a ‘learning experience’. Most experts agree that non-traditional teaching and learning approaches, including mobile education, can improve understand- ing because they put the learner, rather than the teacher, at the centre of the process. Mobile learning using mobile devices, known as m-learning, has been discovered to be more dynamic and interactive than traditional methods. Students can use applications, games and software to follow their instructors. A study by Levin and colleagues at Long Field Academy at the University of California showed that mobile device use in educa- tional settings is advantageous [42]. The respondents recognised the advantages of m- learning and its impact on their teamwork. 3 Proposed theoretical framework 3.1 The MoLeCoP framework M-learning and its related tools have been integrated into teaching and learning pro- cesses all around the world. With an emphasis on student involvement in the curricu- lum, active student participation in lectures and the addition of variety to the learning and teaching environment, the m-learning approach has evolved as an interactive inter- face in digital teaching methods and is adapted to millennial needs [10]. The MoLeCoP framework suggests that researchers investigate the following four aspects of computer- based learning environments: • Promoting engagement in the learning system • Enhancing students’ learning experience iJIM ‒ Vol. 17, No. 13, 2023 45 Paper—Effects on Saudi Female Student Learning Experiences in a Programming Subject Using Mobile… • Making perceived usefulness (PU) positive • Understanding learners’ behaviour. 3.2 Aspects of the MoLeCoP framework This study used the four-factor MoLeCoP framework to pinpoint the factors influ- encing students’ use of mobile devices during lectures for programming classes. The char- acteristics of each factor and how they relate to students’ use of mobile devices to study more effectively are as follows: Promoting engagement in a learning system (based on attitude towards com- puter use). The views of teachers on promoting the use of m-learning have had a significant impact on how much students are using technology [11]. Student engagement is the pro- cess that encourages students to take part in their education and values intellectual atti- tudes [12], [13]. This factor is related to students’ satisfaction and attitude toward m- learning and whether that attitude is positive or negative. According to Graham et al. [14], the significance of improving student learning through involvement is not new. The degree of student engagement has been proven to impact the success of the learning process, as well as student retention in schools or program enrolment [14]. As a result, active participation in the learning process is one of the crucial factors in students’ academic performance [48]. Enhancing students’ learning experience (based on the constructivism of seven principles). According to Chickering and Gamson [15], the constructivism attribute and the seven principles of effective learning for mobile-based education align with the learning process embracing m-learning. Most teachers think that teamwork feedback is an essential factor. Mobile devices make it possible to receive immediate feedback. M-learning facili- tates feedback, assisting students in completing their assignments. Programming educa- tion tools may provide feedback in the form of correct and incorrect responses, points or extrinsic rewards (such as animation, sound effects and increased power) [16]. Students exert more effort and perform better when they can track their progress toward their in- tended goals. M-learning is very helpful in large classrooms because it enables teachers to communicate with students by delivering messages about the course they are teaching, even when they are doing so from a distance [17]. Making perceived usefulness positive (based on Technology Acceptance Model). The technology acceptance model developed and theorised the notion of per- ceived usefulness as an essential element for students adopting information communica- tion technology. Perceived usefulness complements the variables that influence how tech- nology is used in situations that can be examined in terms of intents and behaviour, lend- ing support to the rationale for this theory. Most studies on m-learning assume that people will use perceived usefulness-aligned technologies [18]. Student perception is that the use of mobile devices enhances learning [19]. For in- stance, students can learn indoors or outdoors and quickly access all the information they need, whenever they need it, wherever they are and without limitations [20]. 46 https://www.i-jim.org Paper—Effects on Saudi Female Student Learning Experiences in a Programming Subject Using Mobile… Mobile devices used in programming classrooms is believed to improve student learn- ing, boost student motivation and make it easier for teachers to foster and develop stu- dents’ fundamental skills [13], [21], [22]. M-learning makes learning basic functions easier and helps students accurately understand programming principles [21]. Computer programming has become more engaging and accessible for teachers and students as m- learning has emerged as an effective tool. Helping to understand learner behaviour (based on Saudi social norms). Since cultural attitudes and values impact how people use technology, this study investigates how cultural factors affect Saudi Arabian women who want to use m-learning to study computer programming [23] [24]. In Saudi Arabia, the nation’s culture and norms pre- sent several obstacles for women to overcome before they can participate in and gain access to the field of programming. Culture impacts people’s attitudes toward technology since it shapes how they view modernisation [24]. Before introducing any new technology into a country or region, it is essential to acquire knowledge beforehand about the target population’s culture-spe- cific behaviours [25]. If there is a prior understanding of that country’s culture, new tech- nology can be adapted to fit that nation’s customs. This way, cultural obstacles that could prevent people from adopting new technologies can be removed, or at least minimised. 3.3 Summary This section explained how m-learning and teaching methods have been acknowl- edged as a more adaptable and practical approach to higher learning, which may be used successfully in contexts where women are taking computer programming courses. Student retention and success are both thought to be significantly influenced by student involvement. This study assesses the degree of satisfaction toward the MoLeCoP ap- proach, which could provide a clear indication of how m-learning can enhance the pro- cess of learning. 4 Method The study was conducted at Saudi Arabia’s Aljouf University’s computer science and information technology school, where the infrastructure did not support the use of mobile devices and where laptops were not required in lectures. The study concentrated on female students taking introductory Java programming courses. There were a total of 42 students enrolled for the first semester of the 2021 academic year. 4.1 Population and sample The treatment group used a mobile-based learning approach using laptop computers with ViLLE visualisation software during in-class quizzes. To determine the sample size when the population size is known, the Krejcie and Morgan sampling method [26] was used. The 42 students were randomly divided into two groups of 21 each. The treatment group used the mobile-enabled ViLLE visualisation software application as the iJIM ‒ Vol. 17, No. 13, 2023 47 Paper—Effects on Saudi Female Student Learning Experiences in a Programming Subject Using Mobile… learning technique, while the traditional group used the conventional learning approach. In this study, we focus on the treatment group only. 4.2 Procedure A survey with two sections was used to evaluate the validity of the students’ learning experience in computer programming. The first section collected quantitative data on seven questions using a five-point Likert scale. The survey questions were pertinent to Saudi Arabia, where the study was conducted. The second section contained two quali- tative open-ended questions that asked students to explain their performance and their likes and dislikes of using laptops for quizzes. Open-ended questions were used to elicit thorough responses [27], [28]. Online survey software called RedCap was used for the electronic distribution and collection of questionnaires and students’ consent. Ethical approval was obtained be- fore conducting the study from the University Human Ethics Committee (UHEC) of La Trobe University (Phone: +61 3 9479 1443, E: humanethics@latrobe.edu.au Approval number: HEC19520). The students were not at risk from this study. Before starting the surveys, students were informed about the study being conducted. After giving their consent, students received the survey and took about 10 to 15 minutes to complete it. 4.3 Survey instrument The survey had seven items from previous studies, and two qualitative questions. Three of the survey’s items were from Alghtani [29], three from Sawaan [30], and one each from Alzamil [31] and Alarfaj [32]. Table 1 lists the items and the source. Table 1. Survey items and sources Survey Item Source 1. M-learning in computer programming eases the process of quizzes. Sawaan [32] 2. M-learning in computer programming encourages me to learn more. Algahtani [31] 3. My results in M-learning in computer programming were better compared to those I received in traditional learning. Alarfaj [7]; Alzamil [34] 4. M-learning in computer programming met my needs. Algahtani [31] 5. M-learning in computer programming met my expectations. Algahtani [31] 6. M-learning in computer programming has increased my confidence. Sawaan [32] 7. I want to take other courses using m-learning. Sawaan [32] 5 Data analysis To assess the adoption of the MoLeCoP framework, this study examined the educa- tional experiences of the treatment group’s students. All students in the treatment group were targeted for the survey. This stage of data analysis involves analysing the quanti- tative and qualitative survey data and determining the treatment group’s satisfaction 48 https://www.i-jim.org Paper—Effects on Saudi Female Student Learning Experiences in a Programming Subject Using Mobile… with the experience. SPSS Version 27 was used to analyse the quantitative data. The qualitative responses were transcribed into QSR NVivo 12 Plus data management pro- gram to identify the students’ satisfaction with m-learning. The demographic data of the students was gathered at the beginning of the first term, including their age, laptop ownership status, readiness to bring their computers to school and programming skills. These profiles are summarised in Table 2. Table 2. Summary of demographics of treatment group Treatment Group N 21 Age 19 66.7% 18-20-21 33.3% Laptop ownership No 42.9% Willing to buy - Yes 57.1% Willing to bring laptop to university Never 71.4% Occasionally 28.6% Programming skill Intermediate 14.3% Novice 85.7% In the treatment group, the average student age was 19. Over half the students (57.1%) had laptops, but 71.4% of students never or rarely bring laptops to class. Most students (85.7%) rated their programming ability as novice, while 14.3% rated them- selves as intermediate. 5.1 Reliability test Cronbach’s alpha was used to calculate and analyse the seven items. Cronbach’s alpha ranges from r = 0 to 1, with r = 0.7 or above considered adequately reliable [33]. All items had a high correlation with item totals ranging from 0.608 to 0.817. Table 3 sum- marises the reliability and correlation results. Table 3. Reliability and item-total statistics Scale Mean if Item Deleted Scale Vari- ance if Item Deleted Corrected Item-Total Correlation Cronbach’s Alpha if Item Deleted 1. M-learning in computer programming eases the process of quizzes. 23.0952 10.090 .735 .868 2. M-learning in computer programming encourages me to learn more. 23.3810 10.348 .656 .877 3. My results in M-learning in computer programming were better compared to those I received in traditional learning. 23.0952 10.190 .817 .859 4. M-learning in computer programming met my needs. 23.3333 10.233 .707 .871 iJIM ‒ Vol. 17, No. 13, 2023 49 Paper—Effects on Saudi Female Student Learning Experiences in a Programming Subject Using Mobile… Scale Mean if Item Deleted Scale Vari- ance if Item Deleted Corrected Item-Total Correlation Cronbach’s Alpha if Item Deleted 5. M-learning in computer programming met my expectations. 23.3810 10.848 .620 .881 6. M-learning in computer programming has increased my confidence. 23.3810 10.648 .673 .875 7. I want to take other courses using M- learning. 22.9048 10.390 .608 .884 5.2 Results of learning experience in computer programming The mean and standard deviation (SD) were used to describe the students’ responses statistically. A five-point Likert scale was used for all the questions, ranging from 1 for ‘strongly disagree’ to 5 for ‘strongly agree’. The five-point Likert scale’s equivalent in- terval length is 0.80. When the mean score is 1–2.60, ‘strongly disagree’ and ‘disagree’ responses are at a low level; mean scores of 1–2.60 and 2.60–3.40 indicate a moderate agreement level, while a mean score of 3.40–5 indicates ‘strongly agree’ and ‘agree’ [34]. According to Table 4, all items had a high level of agreement with a mean score over 3.40, and none of the items received a ‘strongly disagree’ or ‘disagree’ answer. The overall m-learning mean score was 3.87 with a SD of 0.532, indicating that students had a high level of m-learning experience. The item ‘I want to take other courses using m-learning’ had the highest level of agreement, with a mean score of 4.19 (SD 0.750), while the item ‘M-learning in computer programming encourages me to learn more’ had the lowest level of agreement, with a mean score of 3.71 (SD 0.717). Table 4. Descriptive statistics for participants’ responses Strongly Disagree Disa- gree Neu- tral Agree Strongly Agree Mean Std. De- viation Leve l Ran k 1. M-learning in computer program- ming eases the process of quizzes. - - 5 11 5 4.00 0.707 High 3 - - 23.8 52.4 23.8 2. M-learning in computer program- ming encourages me to learn more. - - 9 9 3 3.71 0.717 High 6 - - 42.9 42.9 14.3 3. My results in M-learning in com- puter programming were better compared to those I received in tra- ditional learning. - - 4 13 4 4.00 0.632 High 2 - - 19.0 61.9 19.0 4. M-learning in computer program- ming met my needs. - - 8 10 3 3.76 0.700 High 4 - - 38.1 47.6 14.3 5. M-learning in computer program- ming met my expectations. - - 8 11 2 3.71 0.644 High 5 - - 38.1 52.4 9.5 6. M-learning in computer program- ming has increased my confidence. - - 8 11 2 3.71 0.644 High 5 - - 38.1 52.4 9.5 7. I want to take other courses using M-learning. - - 4 9 8 4.19 0.750 High 1 - - 19.0 42.9 38.1 Overall experience of M-learning 3.87 0.532 High - 50 https://www.i-jim.org Paper—Effects on Saudi Female Student Learning Experiences in a Programming Subject Using Mobile… 5.3 One-sample T-Test results The following hypothesis (one-tail test) was tested using a one-sample t-test to confirm whether the proposed framework was beneficial in enhancing students’ m-learning ex- periences: • H0 Overall mean score of m-learning = 3.40. • Hα Overall mean score of m-learning > 3.40. A one-sided significant difference (P < 0.05) was found in the one-sample t-test re- sults, suggesting that the null hypothesis H0 was rejected and that the overall mean score of m-learning > 3.40 denotes a high level of agreement. Additionally, Cohen’s d [35] was used to determine the size of the effect of the framework. The effect size is de- scribed by Cohen [35] as small at d = 0.2, medium at d = 0.5 and large at d = 0.8 or more (see Table 5). Table 5. One-sample t-test results Test Value = 3.40 t Df Significance Mean Differ- ence 95% Confidence Interval of the Difference One- Sided p Two- Sided p Lower Upper Cohen’s d 1. M-learning in computer programming eases the pro- cess of quizzes. 3.888 20 <.001 <.001 .60000 .2781 .9219 .70711 (medium) 2. M-learning in computer programming encourages me to learn more. 2.008 20 .029 .058 .31429 -.0122 .6407 .71714 (medium) 3. My results in M-learning in computer programming were better compared to those I received in traditional learning. 4.347 20 <.001 <.001 .60000 .3121 .8879 .63246 (medium) 4. M-learning in computer programming met my needs. 2.368 20 .014 .028 .36190 .0431 .6807 .70034 (medium) 5. M-learning in computer programming met my expec- tations. 2.238 20 .018 .037 .31429 .0213 .6073 .64365 (medium) 6. M-learning in computer programming has increased my confidence. 2.238 20 .018 .037 .31429 .0213 .6073 .64365 (medium) 7. I want to take other courses using M-learning. 4.832 20 <.001 <.001 .79048 .4493 1.1317 .74960 (medium) Overall 4.052 20 <.001 <.001 .47075 .2284 .7131 .53243 (medium) iJIM ‒ Vol. 17, No. 13, 2023 51 Paper—Effects on Saudi Female Student Learning Experiences in a Programming Subject Using Mobile… 5.4 Ease of learning In the second part of the survey, the students were asked to describe their perfor- mance after using mobile devices. Out of 21 students, 15 (71.4%) claimed that mobile learning had improved their performance compared with traditional learning. Table 6 shows the frequency distribution of the reasons that students provided. Table 6. Reasons given by the students for higher performance due to m-learning Reason Frequency count (%) Rank Desktop/laptop familiarity 3 14.2 4 Coding practice 4 19 3 Feedback 4 19 3 Easy to learn 5 23.8 2 Understanding 12 57.1 1 Quality learning\save time 2 9.5 5 ‘Understanding’ was cited as the main factor in better performance by 12 students (57.1%), while ‘easy to learn’ was the second most cited factor (5 students, 23.8%). ‘feedback’ and ‘coding practice’ were cited by 4 students each as the cause of their improved performance. Desktop/laptop familiarity was cited by 3 students (14.2%) and 2 cited ‘quality learning/save time’. 5.5 Positive and negative aspects of m -learning Table 7 lists some likes and dislikes given by the students for using mobile learning to complete the quizzes. Table 7. Positive and negative aspects of m-learning Like Frequency count (%) Dislike Frequency count (%) Instant feedback 6 28.5% Losing battery charge when doing quizzes 5 23.8% Desktop familiarity 3 14.2% Distraction 5 23.8% Comfortable with de- vices 4 19% Desktop unfamiliarity 3 14.2% Breaking routine 3 14.2% Lack of physical interaction 2 9.5% Saves time 6 28.5% Limited power sockets in the lecture theatre, chance of losing battery power quickly 4 19% Using devices is fun 9 42.5% Cannot manage time\Insufficient\not comfortable 3 14.2% Of the 21 students, 9 (42.5%) indicated they had fun using mobile devices for learn- ing, which was the most common positive factor. In contrast, the least liked factors were ‘desktop familiarity’ and ‘breaking routine’ (3 students, 14.2% each). 52 https://www.i-jim.org Paper—Effects on Saudi Female Student Learning Experiences in a Programming Subject Using Mobile… Regarding dislikes, the most frequent drawbacks, each cited by 5 students (23.8%), were distractions and worry of losing battery power when doing tests in the lecture hall. Because battery life of the device could run out at any time, 19% of students cited limited power outlets in the lecture hall as a dislike. Of the 21 students, 14.2% disliked being uncomfortable, were unable to manage their time and were unfamiliar with desk- tops, while 9.5% cited a lack of physical interaction as a drawback. 6 Discussion This study measured the experience of using mobile learning tools in studying program- ming. Students’ preferences for using mobile learning tools to complete coursework had the highest mean satisfaction at 4.19 out of 5. This result was consistent with the Alferaihi study [36], which found that over 50% of the respondents wished to sign up for m-learning courses again. The results demonstrated that students thought they did better in m-learn- ing than in traditional learning. This is consistent with Alhelih’s study [9] and the meta- analysis by Allen et al. [37], which found statistically significant differences in favour of students who used technology to learn. These studies showed that students who used technology to learn performed slightly better on exams and received slightly higher course grades than those taught using traditional methods. In addition, due to the ease of the learning process, m-learning encouraged students to learn more. This may have occurred for many reasons, including the capacity of the m-learning environment to present in- formation in various formats [38]. Also, m-learning satisfied programming students’ needs, consistent with Algahtani’s [29] conclusion. Direct feedback and time-saving measures also met students’ needs, according to remarks in the open-ended responses. Ac- cording to a study by Greener and Wakefield [39], using mobile technology for teaching and learning has effectively boosted students’ self-confidence and ability to meet expecta- tions. According to Algahtani’s study [29], m-learning effectively satisfied most stu- dents’ needs, including their desire for an immediate response from teachers. The find- ings also demonstrated that mobile instruction aided learning and motivated students to study more. In this study, most students claimed that various factors had improved their perfor- mance. P r ogramming courses that used an m-learning strategy helped students build their programming skills due to understanding the subject and how programming func- tions. This was in line with Alsaggaf’s findings [40] that the constructivist mobile-based teaching strategy aided students in understanding the order in which program code oper- ates. Other factors cited by students contributing to their improved performance included the simplicity of learning, practice with program coding, getting fast feedback and famili- arity with mobile devices. Klimova’s study [41] demonstrated that mobile apps that give learners instant feedback help enhance students’ performance and benefit learning out- comes. The majority of students had favourable opinions toward using mobile devices in lectures. The most noteworthy and positive comment was how much students enjoyed using technology in computer programming classes. This supports the research that mobile de- vices can increase students’ satisfaction and fun [4] [42] [43]. Students also agreed that iJIM ‒ Vol. 17, No. 13, 2023 53 Paper—Effects on Saudi Female Student Learning Experiences in a Programming Subject Using Mobile… using mobile technologies helped them receive immediate feedback and saved time, and Alsaggaf’s research [40] supports this finding. However, students emphasised that the primary drawback was the worry that they may run out of battery power while taking their quizzes and a lack of available power outlets for mobile devices in the lecture hall. Concerns regarding using devices in pro- gramming sessions also included the possibility of distracting students. The overall item rating was 3.87 out of 5, indicating that computer programming students were quite satisfied with their mobile learning experience. This degree of satis- faction demonstrates the efficacy of the MoLeCoP framework and presents a clear pic- ture of how m-learning can be beneficial. 7 Conclusion This study measured the educational experiences of the treatment group students to examine the adoption of the MoLeCoP framework. The study sample consisted of 21 female students in the treatment group who took quizzes in programming classes using the ViLLE software application. A mixed-method technique was used. According to the study’s findings, the students in computer programming courses were content with their mobile learning environment to a certain extent. This level of satisfaction indicates the effectiveness of the MoLeCoP framework and provides a clear example of how m- learning can improve the learning process. The results of this study are useful to Saudi Arabian computer programming teach- ers. The study can act as a guide for teachers by implementing an m-learning strategy in a programming course. 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Eng., 15(08), 120–134. https://doi.org/10.3991/ijoe.v15i08. 10530 9 Authors Afrah Alanazi is currently a graduate student, pursuing Doctor of Philosophy de- gree in in the School of Engineering and Mathematical Sciences from La Trobe Uni- versity. Her PhD program at La Trobe University is specialized in the information se- curity track offered by the College of Computer Science and Information Technology offered at Bundoora (Melbourne) Campus, Australia, hoping to complete her research and get the PhD degree within 2023. Dr Alice Li is a Senior Lecturer in the Department of Management, Sport & Tourism and Director of the Human Resource Management Programs at La Trobe Business School. She has had a wide range of industrial, research and teaching experience both in Australia and internationally. Prior to joining La Trobe University, Alice was at the University of New South Wales. She has also been a visiting fellow at the University of Melbourne and Nankai University. Alice coordinates and teaches on postgraduate and undergraduate programs, delivering in blended, online and face-to-face modes sub- jects such as Human Resource Management, International Management, Business Communications, and International Employment Relations. She researches and super- vises graduate research students in knowledge management and innovation, interna- tional management, human resource management, e-learning in higher education, cross-cultural management and Asian business. Associate Professor Ben Soh obtained his PhD in Computer Science & Engineering (in the area of Secure and Fault-Tolerant Computing under the tutelage of Prof TS Dil- lon) from La Trobe in 1995. Since then, he has successfully supervised to completion 11 PhD students and published more than 180 peer-reviewed research papers. He has made significant contributions in the following research areas: Fault-Tolerant and Se- cure Computing, Cloud Computing, Information Systems Research, Pervasive Wireless Network Communications, and Business Process Management. Associate Professor Ben Soh held various senior positions in the Department of CS & IT: Acting Head of Department (Jun-Dec 2007), Deputy Head of Department (2003- iJIM ‒ Vol. 17, No. 13, 2023 57 https://doi.org/10.3390/educsci9020090 https://doi.org/10.14355/jitae.2014.0304.10 https://doi.org/10.14355/jitae.2014.0304.10 https://doi.org/10.31327/jomedu.v3i2.813 https://doi.org/10.3991/ijep.v13i2.35971 https://doi.org/10.3991/ijep.v13i2.35971 https://doi.org/10.3991/ijoe.v15i08.10530 https://doi.org/10.3991/ijoe.v15i08.10530 Paper—Effects on Saudi Female Student Learning Experiences in a Programming Subject Using Mobile… 2007), and Director of Undergraduate and Diploma Studies (2001-2010). Currently he acts as Postgraduate IT Coursework Coordinator/Adviser. His current interests in Fault- Tolerant Computing (Cyber Security, Reliability and Availability), Cloud Computing, Data Security and Privacy Preservation Query Management, Business Process and Workflow Management, Pervasive Computing, SNS and Educational Technology. Article submitted 2023-01-28. Resubmitted 2023-05-12. Final acceptance 2023-05-12. Final version pub- lished as submitted by the authors. 58 https://www.i-jim.org