1. Introduction
Mobile technology (e.g., mobile/smart phones, laptops, tablets) has attracted research interest for over a decade and can be considered a learning tool with educational potential [
1,
2,
3,
4]. Mobile devices are light enough, are equipped with communication capabilities, and may influence how learners learn. Indicatively, university students use their smartphones for various educational tasks, such as to search for educational resources and content or for information via the web, to communicate with fellow students and tutors, to access e-class, to download books, and to manage assignments [
5,
6]. Recently, during the COVID-19 pandemic, mobile technology-mediated learning was upscaled by many university students and it supported online learning [
7,
8,
9]. Mobile learning refers to the educational use of mobile technology with the aim to support, facilitate, and extend the teaching and learning process anytime and anywhere [
1]; e.g., to support information collection and exchange, collaborative learning, knowledge construction, as well as independent and lifelong learning. The educational affordances of mobile technology (communication possibilities, access to information, etc.) have the potential to support educational practices. Mobile learning usage and effectiveness may be influenced by different factors, and students’ perceptions can play a role in mobile technology utilization [
10].
The utilization of mobile technology in universities and higher education institutions is associated with benefits such as flexibility in learning, broadening learning beyond the physical classroom anytime and anywhere (e.g., by employing online platforms as applied during the pandemic), and supporting personalization [
11]. Potential educational benefits include improvement of students’ motivation and achievement [
12], possibility to expand collaborative learning and communication [
2,
3], stimulation of interest/motivation, and facilitation of students’ engagement [
13]. The sense of ownership and the freedom to define the activities/tasks might increase students’ motivation to study [
14] via, for example, applications’ utilization to assist engagement, collaboration, interaction, or handling resources and material. Mobile technology use by university students is also linked with drawbacks/barriers such as technological (internet connectivity, barriers associated with the usability of the hardware/software) and instructional barriers (difficulties in locating learning material, unsuitable material for use on mobile devices) [
5,
6,
8]; however, these do not constitute the focus of this study. University students are also mature and more autonomous in making learning decisions utilizing mobile technology. The use of mobile devices among university students is increasingly more common, while research on student mobile technology/learning perceptions is still relevant and can contribute evidence to this ongoing research issue.
This pilot study aimed to explore Greek postgraduate students’ perspectives on the benefits and learning possibilities of mobile devices’ usage in their postgraduate studies; this is an under-researched area within the Greek context. Studying university students’ perceptions of whether using mobiles raises standards [
15] increases their awareness of the educational use of mobile technology. When students perceive that mobile technology/learning has benefits and learning possibilities, they are more likely to integrate it into their academic studies. Positive perceptions may influence students’ interest, motivation, or performance in mobile-based environments, and such perceptions may also contribute to the adoption of mobile-mediated learning in universities in the post-pandemic era.
The rest of the paper is organized as follows:
Section 2 regards the background of the study,
Section 3 presents materials and methods,
Section 4 indicates the results, and
Section 5 presents the discussion and implications.
4. Results
4.1. Postgraduate Students’ Perspectives on the Learning Possibilities and Benefits of Using Mobile Devices in Their Studies
To investigate students’ perceptions of mobile technology benefits and learning possibilities, we initially performed a descriptive analysis.
Table 2 indicates the students’ response percentage frequencies on the 10 items of the questionnaire (N = 34 students). The last column of the table presents the percentages of those who “agree” and “strongly agree”, in descending order. The reliability of the questionnaire is excellent, Cronbach-a = 0.919 (>0.7). The sample size is small (this is discussed in the Limitations section), and this affects Cronbach-a.
It was revealed that over 70% of the sample agreed and strongly agreed with items S3, S4, S5, S2, S9, S8, S10, and S1. The items with the highest percentage of agreement correspond to the possibilities of mobile learning. For example, students reported that “Mobile technology should be used to connect postgraduate students with people, content and resources” and “Mobile devices (learning) bring new opportunities for learning in a postgraduate programme” (agreement for S3 and also for S4: 94.1%). The items with lower percentages of agreement are S6 (agreement 64.7%) and S7 (agreement 61.7%); these are associated with the benefits of mobile technology usage to increase postgraduate students’ motivation and engagement with their studies.
The questionnaire was divided into two groups/factors of five items each, namely “Possibilities” (items: S1–S5) and “Benefits” (items: S6–S10). To confirm this grouping, the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) reliability index was applied; KMO is an indication of the suitability of the data for factor analysis and must be >0.5. More specifically, for the factor “Possibilities” KMO = 0.828 and for the factor “Benefits” KMO = 0.698. Therefore, the grouping of the questions was done correctly.
Then, a Total Variance Explained investigation for the factors “Possibilities” and “Benefits” was carried out. This check indicates that the total percentage of the variance of the variables was explained by the selected factors; it is an indicator of the data suitability for factor analysis (the closer to 100%, the better the respective factors interpret most of the data, with a minimum acceptable value of 50%). The check showed that for the factor “Possibilities”, the top percentage of variance explained is 78.607% (eigenvalue = 3.93), while for the factor “Benefits”, it is 63.274% (eigenvalue = 3.164). Therefore, the data in both cases are suitable for factor analysis. The check showed that for the factor “Possibilities” (S1–S5), the mean is greater than that of “Benefits” (S6-S10); 4.38 (SD = 0.707) and 3.89 (SD = 0.878), respectively (
Table 3). This means that student responses for “Possibilities” are closer to “strongly agree”, in comparison to responses for “Benefits” which are closer to “agree”; i.e., stronger perceptions appear for the factor “Possibilities”.
Finally, the Pearson r linear correlation test, between the factors “Possibilities” and “Benefits”, showed that there is a strong positive linear correlation; r = 0.734 and Sig. = p < 0.01. Therefore, as the values of the “Possibilities” factor increase, so do the values of the “Benefits”.
4.2. Confirmatory Factor Analysis
In order to confirm the allocation of the ten items to the two factors “Possibilities” and “Benefits”, the following checks were carried out. There are no missing values, so we will not have different sample sizes. The test showed that there were no ambiguous variables. No factors emerged, with fewer than three items/variables. Moreover, all factor loadings are well above 0.30. Therefore, it is confirmed that the factor analysis was correctly done; the first factor (F1: “Possibilities”) is associated with items S1, S2, S3, S4, and S5 and the second factor (F2: “Benefits”) is associated with items S6, S7, S8, S9, and S10.
Table 4 displays the loadings for each factor (F1, F2), as well as the mean and standard deviation per item.
The relationship was tested for each pair of variables (correlation). No variable is very strongly correlated (r > 0.8). In addition, the determinant is 6.192 × 10–5 > 10–5 and the level of statistical significance is p < 0.05. Therefore, the analysis can clearly distinguish them.
A KMO test of sphericity was performed to test whether the analysis yielded distinct and reliable factors. The test showed that KMO = 0.796, an indicator characterized as good.
In order to establish whether the number of factors (“Possibilities” and “Benefits”) are actually two, a Total Variance Explained (TVE) test (>50%) and the eigenvalue criterion were performed. The check showed that the number of factors is two, while two eigenvalues are greater than 1 (6.191 and 1.081), as indicated in
Table 5.
4.3. Factors That Influence Student Perspectives
Then, we explored the possible influence of the factors/variables “Gender”, “Age”, “Employment”, and “Mode of Study” on students’ perspectives. A normality check was carried out for the variables “Gender”, “Age”, “Employment”, and “Mode of Study”. The test was based on Shapiro-Wilk (sample < 50) to check the probability that the variables in question follow a normal distribution. Based on the results, the probability that the aforementioned variables follow a normal distribution is, in all cases, less than 5%. Therefore, we discuss a non-normal distribution (for “Age”, Sig. = 0.001, for each of the “Gender”, “Employment”, and “Mode of Study”, Sig. = 0.000). For this reason, a non-parametric test was applied, the Mann-Whitney U test for the factors “Gender” and “Mode of Study”, and the Kruskal Wallis test for the factors “Age” and “Employment”.
The non-parametric testing for the influence of the variables “Gender” and “Employment” indicated that these do not influence student responses. The variable “Age” significantly influences two items: item S3 (Sig. = 0.011), which regards the possibility of mobile technology usage to connect students with people, content and resources, and item S4 (Sig. = 0.015), which is associated with new learning opportunities in postgraduate studies when mobile technology is used. For item S3, the maximum mean is observed in the age group 52+ (Mean = 4.67, SD = 0.577) and the minimum in the age group 22–31 (Mean = 3.71, SD = 0.488) (
Table 6). For item S4, the maximum mean is observed in the age group 32–41 (Mean = 4.62, SD = 0.506) and the minimum in the age group 22–31 (Mean = 3.29, SD = 1.254). The variable “Mode of studies” appears to influence student responses for the item “Mobile devices can play an important role in postgraduate education” (S1, Sig. = 0.027) (
Table 6); the Hybrid mode of studies (Mean = 4.78, SD = 0.428) has a higher mean than the face-to-face mode (Mean = 4.13, SD = 0.885). Finally, there is no impact of any of the variables “Gender”, “Age”, “Employment”, and “Mode of Study” on the factors “Possibilities” and “Benefits”.
5. Discussion and Implications
This pilot study investigated Greek postgraduate students’ perspectives on the benefits and learning possibilities of mobile devices’ usage in their postgraduate studies. Τhe study contributes to the research evidence on postgraduate students’ perspectives. Students’ perceived mobile technology learning possibilities and benefits are likely to relate to student interest and motivation when they use mobile devices, and this, in turn, may influence their academic performance in mobile-mediated educational environments.
With regard to the first research objective, postgraduate students’ perspectives on the learning possibilities and benefits of mobile devices’ utilization in their studies were positive. Most of the students expressed strong perceptions indicating their awareness of mobile technology possibilities and benefits for their studies; awareness of the mobile learning/technology benefits is also likely to increase student acceptance of mobile learning [
17]. Awareness may also facilitate students in becoming lifelong learners who adapt to new (mobile) technologies. There is agreement with studies in other countries [
16,
18,
19,
21]. Indicatively, in this study (see
Table 2), the item “Mobile devices (learning) bring new opportunities for learning in a postgraduate programme” (S4: agreement 94.1%) is in line with university students’ perceptions regarding the importance of mobile phones as educational tools for their academic activities [
19]; the view “Mobile devices can be used to improve the skills of postgraduate students” (S5: agreement 91.2%) is documented in students’ views regarding familiarization with digital technology [
6]. Participants noted that “Mobile learning devices improve communication between postgraduate students and their teachers” (S9: agreement 88.2%), which aligns with studies revealing (post)graduate student views on the role of mobile devices in communication with peers/tutors [
16,
21]. As mentioned in the results, the items which had the highest percentage of agreement are linked to the possibilities of mobile learning, while those with a relatively lower percentage of agreement (S6 and S7: over 61%) regard the role of mobile devices in enhancing student motivation and engagement in their studies. It is suggested that actual practices (learning activities in mobile technology-mediated environments) can provide opportunities to increase student engagement and motivation; this has implications for higher education pedagogies.
With regard to the second research objective, isolated significant differences were observed. “Age” influences the items “Mobile technology should be used to connect postgraduate students with people, content and resources” (for S3: students aged 52+ had higher values in comparison to the age group 22–31), and “Mobile devices (learning) bring new opportunities for learning in a postgraduate programme” (for S4: students aged 32–41 had higher values in comparison to the age group 22–31). Higher values are associated with more positive perceptions. The variable “Mode of postgraduate studies” affects item S1, which regards the role of mobile devices in postgraduate education (the hybrid mode of studies has a higher mean value in comparison to the face-to-face mode); this has implications for the adoption of the blended learning mode in the post-pandemic era. “Gender” and “Employment” had no significant effect on postgraduate students’ perspectives.
We recommend utilization of mobile devices in postgraduate education in different education modes (in-person, blended and online education). Post-pandemic, forms of teaching and learning (such as blended/hybrid education) that were previously on the margins are becoming mainstream [
38]. During the pandemic mobile technology-mediated learning was applied by many university students and it supported online learning [
7,
8,
9], while post-pandemic its usage is on the rise in higher education institutions [
6]. Post-pandemic, mobile learning is likely to play a gradually more important role in university teaching and in hybrid-blended courses [
39].
Postgraduate students’ perspectives on the learning possibilities and educational benefits of mobile devices’ usage in their studies have implications for students, tutors, educational practices and university policies. Student training could highlight the effective use of mobile devices and provide opportunities for the enhancement of different skills that will prove useful in mobile technology-mediated learning environments; skills such as communication, collaboration, resilience, autonomy, and adaptability. Digital mobile technologies are changing the context of teaching-learning with increasing access to the internet and online learning environments, thus resulting in different levels of mobile technology integration within the university systems. Student perspectives need to be taken into account in the decisions made by universities and/or education policy makers. Educational policies could be (re)adjusted to improve the availability of e-resources and offer opportunities to tutors to utilize mobile technology, use different internet-based tools, and implement mobile-supported pedagogy. Innovative pedagogies, such as flexible hybrid learning and pedagogy of autonomy [
28], are suitable when mobile technology is utilized in higher education contexts. Indicatively, tutors could be better prepared to incorporate mobile pedagogy issues in face-to-face, blended, and online modes/approaches of education, thus addressing students’ needs and implementing effective communication strategies that strengthen student communication, collaboration and interactions. Enhancement of student motivation and engagement are also important. Mobile-mediated learning is suggested to be implemented in different modes of postgraduate studies’ provision (face-to-face, blended, and online modes). Potentially effective mobile learning environments are associated with the universities’ organizational and technological management, resilience, and infrastructure. For example, universities should be supported by flexible, convenient, and digital platforms [
40] that can be accessed via mobile technology and facilitate student-student/-tutor communication and collaboration. Adoption of hybrid-blended modes of education is useful to be planned by universities for future crises or situations when face-to-face engagements are difficult (e.g., for postgraduate students who work full-time). Educational policies could develop clear guidelines on how to evaluate the pedagogical benefits of implementing mobile technologies in (post)graduate studies. Latest research [
41] has highlighted the importance of hybrid events as beneficiaries of the educational process. The design of mobile applications to supplement/enhance traditional higher education teaching is also a relevant issue; mobile tools and chat applications are a potentially valuable resource for online/blended learning affecting educational interactions [
42]. For example, in the online environment, a mobile learning system enables tutors to upload educational content/activities, tests, and assignments, while students can download learning materials, access online classes, and interact with peers/tutors, using the mobile learning system [
17]. Via their own mobile devices, students engage in their own learning from their location, and this has implications for the design of content, activities, and communication [
43].
A major limitation of this study is the small sample size; therefore, the findings cannot be generalizable/transferable. However, it is a pilot study and the percentage of participation was 18.9%. Another limitation is that the survey did not include any reverse questions, and reverse coding was not used. The questionnaire items did not include possible barriers perceived or experienced by students when mobile technology is used, and this diminishes the items’ representativeness. We used a quantitative inquiry only; students’ perspectives could be further investigated via interviews that may indicate students’ views on the role of mobile technology in the blended mode of postgraduate studies. We plan to administer the questionnaire to a larger sample of postgraduate students across different universities and to also investigate student-perceived barriers when mobile technology is used. Other variables/characteristics, such as the profile/specialization of the postgraduate programme, that may influence students’ perspectives were not explored due to personal data protection; however, this constitutes an issue for future research.
Future research could investigate postgraduate students’ mobile-mediated educational practices in both face-to-face and blended modes of study, e.g., how students utilize their mobile phones for research and assignment purposes. Perceived benefits and practices in association with specific mobile applications or academic disciplines could also be explored; e.g., research reported on the use of mobile virtual labs in chemistry [
44]. It is worth investigating postgraduate students’ perspectives in relation to their university profile or support. Finally, since mobile learning research in higher education includes various issues, different factors (e.g., educational technology, social) that influence mobile technology usage [
45] or factors that may affect student perspectives (e.g., facilitating conditions) constitute issues for future research. Investigating (post)graduate students’ perspectives on mobile technology learning possibilities and benefits is an ongoing research issue.