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Systematic Review

Mobile Learning and Its Effect on Learning Outcomes and Critical Thinking: A Systematic Review

by
Liliana Pedraja-Rejas
1,*,
Camila Muñoz-Fritis
1,
Emilio Rodríguez-Ponce
2 and
David Laroze
2
1
Departamento de Ingeniería Industrial y de Sistemas, Universidad de Tarapacá, Casilla 7D, Arica 1020000, Chile
2
Instituto de Alta Investigación, Universidad de Tarapacá, Casilla 7D, Arica 1020000, Chile
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(19), 9105; https://doi.org/10.3390/app14199105
Submission received: 15 August 2024 / Revised: 14 September 2024 / Accepted: 17 September 2024 / Published: 9 October 2024
(This article belongs to the Special Issue Data-Driven Mobile Networks and Education)
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Versions Notes

Abstract

:
This paper explores the relationships between m-learning, learning outcomes, and developing critical thinking in university students. A systematic review of empirical articles in English published in journals indexed in the Web of Science from 2015–May 2024 was carried out. A sample of 50 articles was obtained. The results show that, in most of the analyzed articles, integrating m-learning tools can potentially to improve students’ learning outcomes and critical thinking skills. Considering the above, we recommended that educators and university managers integrate and promote the adoption of new technologies in teaching methods. Several recommendations are provided for the effective integration of m-learning into learning activities, stressing the importance of teachers becoming familiar with technology-enhanced learning environments early in their training.

1. Introduction

Over the last decade, the advancement of digital technologies has radically transformed the concept and methodology of learning [1]. Particularly, during this period, mobile devices have been progressively incorporated into educational environments, redefining how knowledge is accessed and the way educational content is interacted with [2].
A leading innovation in this area is mobile learning (m-learning), which refers to using mobile devices, such as smartphones and tablets, to facilitate anytime, anywhere learning [3]. This educational approach leverages the portability, connectivity, and accessibility of these devices to offer new opportunities for personalized, interactive, flexible and motivating learning [4].
Thanks to the powerful processing power and portability of mobile devices, coupled with wireless communication and context-sensing tools, mobile devices have been transformed into powerful educational tools with immense potential in both traditional classrooms and informal outdoor learning [2]. M-learning platforms take full advantage of these capabilities, allowing students to access vast amounts of information through search engines or specialized applications, share ideas and experiences with other students through social networks, and facilitate their learning activities in a more dynamic and interactive way [5]. These platforms include applications ranging from educational games to e-books [6] and learning management systems such as Moodle [7], which provide interactivity and flexibility in education, empowering mobile learning through wireless or networked devices [8].
The use of technology in education brings significant benefits, empowering students to develop 21st century skills, such as critical thinking and problem solving. It also opens up new opportunities for formal education and innovative learning practices. This empowerment allows students to plan their own learning autonomously and actively, transforming content production, fostering collaborative inquiry, and supporting contextualized and authentic learning [1,9,10].
Similarly, m-learning is a catalyst for enhanced student engagement and motivation [11,12,13,14,15]. By integrating multimedia, immersive elements, and gamified learning experiences, among others, m-learning makes the educational process more engaging and effective. This compelling potential positions m-learning as an instrumental tool in the transformation of the higher education sector [3].
Recognizing the importance of m-learning, this study explores how m-learning, learning outcomes, and the development of critical thinking in university students are related. In particular, it seeks to answer two main questions:
RQ1: Does the adoption of m-learning tools influence the learning outcomes of undergraduate university students?
RQ2: Does the adoption of m-learning tools influence the development of critical thinking skills in undergraduate university students?
Although numerous systematic reviews have been conducted on m-learning in the context of higher education, e.g., Refs. [5,16,17,18,19], this systematic review is distinguished by its particular focus on the relationships between mobile learning, learning outcomes, and the development of critical thinking. This article seeks to provide a deeper understanding of the potential of this type of learning, thus enriching existing knowledge on the subject.

2. Background

2.1. Learning Outcomes

When talking about learning outcomes, we generally refer to aspects such as academic performance, attitude, motivation, and higher-order thinking skills [20]. One dimension considered in measuring learning outcomes is students’ acquisition of knowledge, which involves recalling the content seen and understanding it to evaluate and address new problems [21]. The importance of assessing the achievement of learning outcomes lies in the fact that it results directly from the educational process [22] and is often linked to the quality of teaching [23].
M-learning has the potential to improve motivation and efficiency in information acquisition by facilitating more frequent interactions, providing constant feedback, and enabling more dynamic assessments, thus overcoming the limitations of conventional teaching [24]. Understanding the link between m-learning and learning outcomes could therefore critically influence education policy reform [25].

2.2. Critical Thinking

Critical thinking is a complex cognitive process recognized as essential for the 21st century [26]. It is considered a model of intelligence for addressing real-world problems [1], as it involves skills such as interpreting, analyzing, evaluating, inferring, explaining and self-regulating [27]. This cognitive process fosters the ability to reflect and reason logically and coherently, evaluating a problematic situation from different perspectives to find an adequate solution [22,28].
The success in developing critical thinking can be affected by several factors, including students’ characteristics (such as their motivation, learning style, and self-confidence), as well as methodological (such as the method and duration of instruction, tools used, and feedback) and contextual aspects [29].
Technology integration in education has revolutionized information access and retention, providing a more significant storage and a more engaging presentation of content. Consequently, it has made education more interactive and accessible, increasing enthusiasm for learning [30]. Additionally, this technology could facilitate and enhance the process of educational intervention, particularly in developing students’ critical thinking, through a series of functions that promote learning and constant self-assessment [31].
The characteristics of m-learning, such as high searchability and rich interactions, along with performance-based analytics, promote effective learning [32]. Suitably designed instructional technology tools can organize interactive courses and stimulate students to become active learners, which can help them achieve higher-order thinking skills, ranging from the basic cognitive knowledge level, such as remembering and understanding, to the advanced levels of applying, analyzing, evaluating, creating, and improving their reasoning processes [33].

3. Materials and Methods

A systematic review approach was used to answer the research questions. This method aims to identify, critically evaluate, and synthesize existing scientific research on a topic in order to answer a predetermined question [34]. This work complies with the guidelines established in the PRISMA statement [35], used as a methodological guide.

3.1. Document Selection and Search Approach

A search was conducted on 1 May 2024 in the Web of Science (WoS). This database’s selection was explained by its wide international recognition for including prestigious journals in different areas of knowledge and for its extensive coverage [36]. Moreover, as a selective, structured and balanced database, its use facilitates a variety of informative purposes [37] and ensures a consistent comparison of studies within a sound methodological framework.
A search protocol was delineated for the database that included the definition of key descriptors, inclusion and exclusion criteria for all relevant papers, and methods for retrieval and evaluation of articles. The descriptors used were: “mobile learning” or “m-learning” (All fields). The search was also guided by the definition that the concepts of “higher education” or “university*” should be found in the abstract. The inclusion criteria are defined as follows:
  • Type of Document: Articles as they are the primary means of scientific communication.
  • WoS Index: Social Sciences Citation Index (SSCI), Science Citation Index-Expanded (SCI-E), and Arts & Humanities Citation Index (A&HCI). Emerging Sources Citation Index (ESCI) is excluded as it mainly incorporates journals of regional importance [38].
  • Language: English is recognized as the language of international science, and continues to dominate scientific activities to this day [39].
  • Time period: 2015–2024. The reason for this is because m-learning is a field that is continuously and rapidly evolving, and findings from older work may no longer be applicable today. This ensures the relevance and currency of our research [5].
  • Study type and participants: Empirical (primary/participatory) research focusing on undergraduate students.
  • Context: Research should not have been conducted during the COVID-19 pandemic since, in general, the use of technologies in teaching was mandatory and sudden for remote course delivery [40,41], which may have consequences for design, acceptance by students, and evaluation of the real effects (this condition does not apply to distance education universities).
  • Concerning RQ1, studies that objectively measure the achievement of learning outcomes are included (self-perceived achievements are excluded). As an inclusion criterion, it is also defined that those studies with experimental or quasi-experimental designs must have a control group (same cohort) to measure the real effects of m-learning initiatives.
  • Concerning RQ2, studies that objectively (e.g., test) and subjectively (opinion, self-reflection, and others) measure changes in the levels of critical thinking are included.
Figure 1 shows the steps involved in selecting the sample. The initial search identified a total of 1980 papers. However, this number was reduced to 399 when the WoS filters were applied according to the defined inclusion/exclusion criteria (type of document, index, language, and year of publication). During the second stage, the abstracts of all these papers were read, and 122 moved on to the stage that involved reading the full papers to assess their eligibility. Finally, the sample consisted of 50 articles.
It is important to note that the selection process was carried out in two phases. In the first phase, the team members conducted an independent reading of the articles to minimize possible biases. Then, continuous communication was maintained among all team members to ensure a solid consensus on the inclusion of each article in the analysis.

3.2. Concepts Associated with the Analysis

In addition to what is involved in m-learning, learning outcomes and critical thinking there are other concepts that need to be explained as they are referred to in the paper. Each of these concepts is described in detail below.
Experiment, Randomized Experiment, and Quasi-experiment: An experiment refers to a study in which a variable is manipulated under controlled conditions to observe its effects. A randomized experiment, for instance, is one in which students are randomly assigned to the group that will receive the treatment. At the same time, a quasi-experiment implies that students will be assigned to the same group by convenience or by the researcher’s judgment (with no randomization component) [42].
Experimental group (EG) and control group (CG): The EG receives the designed treatment, which may consist of a single or multiple sessions. The CG, meanwhile, does not receive any intervention and serves to determine whether any change can be attributed to the treatment or not [43].
Pre-test, post-test, and delayed post-test: Pre-test is intended to measure, in this case, students’ knowledge or skills prior to any intervention and to ensure comparability of the two groups (EG and CG). The post-test allows researchers to determine whether or not the treatment had an immediate effect on the outcomes (using the CG as a comparison). At the same time, the delayed post-test also seeks to assess the effect, but in the long term [43].

3.3. Data Analysis

Using Microsoft Excel 2021 (Microsoft Corporation, Redmond, WA, USA), the data were extracted on the same day as the search, and the results were organized. The analysis process included reading and re-reading the selected papers by the co-authors, in which the thematic categories emerged through both inductive and deductive approaches [41]. To ensure consistency in coding, the coauthors held periodic meetings in which the emerging categories were discussed and independent codings were compared. In cases of discrepancies, consensus was reached through discussion and fine-tuning of the categories.
For the first analysis, all 50 articles in the sample were considered. The year of publication and the journal where the papers were published were assessed at this stage.
Meanwhile, 41 articles were considered to address RQ1. The region where the study was conducted, the area where the initiative was carried out, and the number of participants were assessed. The experimental design was also analyzed (number of groups compared, assignment of these groups, and tools for quantifying the effects), and the main findings were highlighted. Further details of these papers can be found in Appendix A.
Finally, 12 papers were analyzed in RQ2. Here, the focus was on the context in which the study was conducted, the participants, the data collection tools, the main findings and the conclusions.

4. Results

Table 1 shows some general characteristics of the selected articles. It shows that most articles were published in 2021 and are from the British Journal of Educational Technology (Q1) and Interactive Learning Environments (Q1).

4.1. Learning Outcomes

Particularly, Table 2 highlights that most of the research was conducted in Asia, focusing mainly on teaching English as a foreign language and in fields related to health sciences, human/animal biology, and computer science and programming. It was also found that most of the initiatives involved the participation of numerous students (over 50).
Two primary outcome comparison groups were used in 92.7% of the studies, while the remaining 7.3% used three groups. The groups were assigned in 46.3% of the cases by randomization, either of the individual participants or of the courses to which the intervention was to be delivered, and in 17.1% of the cases by non-randomized procedures (order of course enrollment, student decision, and others). A repeated measures design was used in one study, where the same students acted as CG and EG in different periods.
Of all studies, 26.8% did not specify the assignment procedure. In three papers, the comparison groups were based on the behavior of using the m-learning tool or on their level of achievement in the exercises proposed by the application.
Regarding the techniques for comparing learning outcomes between the groups, and as can be seen in Appendix A, most studies implemented a pre-test and post-test to measure the knowledge or skills of the participants before and after the m-learning intervention. In a minority of cases a delayed post-test (also called retention test) was used. Other instruments for comparison of learning achievement in the studies were final tests, mid-course tests and average grades in the subjects. Table 3 summarizes the main findings of the analyzed papers.

4.2. Critical Thinking

Table 4 summarizes the study context, participants, data collection tools and main findings of the analyzed papers. It is worth mentioning that most studies addressing the relationship between m-learning and critical thinking have been conducted in Asia, which is in line with the trend observed in the previous analysis. Regarding participation, only one study had fewer than 50 students.

5. Discussion

The discussion section has been organized following the proposed research questions.

5.1. M-Learning and Learning Outcomes

The present study found that research relating m-learning to learning outcomes is focused on specific geographic regions, with Asia accounting for more than 50% of the research. In particular, Taiwan conducted 45.5% of the studies from the Asian continent and 24.4% of all the studies analyzed in this section. Previously identified in earlier reviews [18], this trend can be attributed to the country’s excellent and reliable mobile telecommunications infrastructure, which has led to increasing adoption of m-learning in educational institutions [85] and, consequently, a preponderance of contextualized studies in Taiwan [86].
Three main areas where m-learning initiatives were integrated and evaluated stand out from the articles analyzed: in learning English as a foreign language, in the field of health sciences and human/animal biology, and in courses related to computer science and programming.
Due to their multiple uses, mobile devices are powerful tools for learning a second language [67]. The potential of integrating m-learning to improve various aspects of English language learning was evidenced, ranging from vocabulary [67,69,75,76] to reading comprehension [74,76], writing [76], grammar [67], conversational comprehension [67,75], speaking fluency and accuracy [67,75], and receptive knowledge of the form–meaning connection and productive knowledge of collocations [71].
In health sciences and human/animal biology, improvements in students’ knowledge of anatomy [58,59,65], physiology [58], nasotracheal suction [60], along with pharmacology and drug administration [60,61] were highlighted. Integrating m-learning was also found to help improve knowledge in dental education [52]. In the veterinary medical education setting, the benefits of using an interactive iBook needed to be clarified, although students’ low use of the tool was highlighted [62].
Finally, in teaching courses related to computer science and programming, the use of m-learning tools has been shown to improve students’ knowledge in JAVA programming [44,64], algorithms [51], information technologies [72], web design [54], and systems analysis and design [53].
At general levels, the analyzed articles highlight the benefits of using m-learning tools to achieve students’ learning outcomes. These findings are consistent with previous studies such as the one conducted by Zheng et al. [87]. Here the authors, through a meta-analysis, concluded that technology-facilitated personalized learning had significant and positive effects on learning achievement, and that this effect was moderated by the personalized learning methods and software used.
Undoubtedly, m-learning provides a variety of possibilities to the education sector, as it delivers an unrestricted form of learning that can occur in various contexts, times, subjects, people, and through different technological tools [18]. Furthermore, personalizing the educational experience through these tools facilitates acquiring and understanding knowledge, thus improving the achievement levels of learning outcomes. This positions m-learning as a crucial strategy in the modern educational environment.

5.2. M-Learning and Critical Thinking

The review carried out through this research found some significant trends around the generation of knowledge linking m-learning with critical thinking. Similarly to the previous case, most of the analyzed articles were contextualized on the Asian continent, reinforcing the idea that m-learning is a topic of great interest to local researchers.
Overall, the articles support the idea that the use of educational applications and mobile devices can improve students’ critical thinking skills. Specifically, it was found that strategies such as peer assessment [79], inquiry [9], concept mapping [1], gamification [83,84], and social and cooperative interaction [56,81,82] assisted by mobile devices contribute to significantly improve students’ skills regarding their judgment, reasoning, comprehension, and evaluation, contributing to more meaningful learning.
Similarly, using mobile devices improved students’ self-regulation levels [32,74]. For Facione [27], self-regulation is one of the fundamental skills of critical thinking. The self-direction process involves active control by learners of their cognitive, motivational, and behavioral engagement in learning [88]. For example, it involves reconsidering the interpretation or judgment on a controversial topic, monitoring and modifying their motivational and affective states, managing time, setting learning goals, making plans, and selecting appropriate strategies [27,74,88]. In literature, various authors posit that m-learning and self-regulation enhance learning more effectively when intentionally integrated into the curriculum but they warned that, for proper implementation of the initiative, students must be guided by technology-savvy educators who provide appropriate support and scaffolding [89].
At global levels, the articles in the sample highlight how their m-learning initiatives can contribute to the development of higher-order skills. However, one has to be cautious with the results as most of the papers used self-perception questionnaires, which, and as Asiri et al. [31] raised, is not necessarily a true indication of improvements as sometimes students overestimate the acquisition of critical thinking skills. This area is undoubtedly relevant and deserves further research.
Finally, it is remarkable that the volume of scientific production relating m-learning to critical thinking is significantly lower than that relating m-learning to learning outcomes. This low number may be because higher-order thinking skills are more challenging to measure than classroom learning achievements [90].

6. Conclusions and Recommendations

The present study analyzed 50 empirical studies that provided evidence of the impacts of m-learning integration. Overall, it is concluded that m-learning has the potential to improve both learning outcomes and critical thinking skills of students.
In terms of learning outcomes, the analysis revealed that most of the research on m-learning is concentrated in Asia, with Taiwan standing out as the leader in scientific production in this area. The studies reviewed evidence successful application of m-learning in fields such as English language learning, health and biological sciences, and computer science. These findings reinforce the claim that m-learning provides effective tools for improving various academic competencies.
In terms of critical thinking, the results indicate that strategies facilitated by mobile devices, such as peer assessment, inquiry, and gamification, have a positive impact on the development of critical skills. Also, m-learning has been found to foster self-regulation, which is crucial for critical thinking. However, most studies rely on self-perception questionnaires to measure these skills, which may not accurately reflect actual improvements. Therefore, more research is needed to more accurately assess the impact of m-learning on the development of critical thinking.
Information and Communication Technologies (ICT) have profoundly transformed teaching and learning, facilitating a more dynamic and interactive education through digital media that allow the active participation of students [91]. In this context, it is first suggested that university educators and managers not only integrate these technologies into their teaching methods, but also foster an environment that favors pedagogical innovation, adapting to the specific needs of students and the educational context.
Successful implementation of m-learning, however, requires more than mere technology adoption; it is the result of strategic planning and careful consideration of multiple factors. As Sophonhiranrak [5] points out, educators play a key role in ensuring students’ readiness, infrastructure, course content, learning objectives, the learning environment, internet connectivity, and the suitability of learning applications. Moreover, the alignment of these activities with the context and content, and the establishment of a robust feedback system are essential for monitoring and evaluating the impact of m-learning.
Second, it is critical that teachers become familiar with technology-enhanced learning environments early in their training. M-learning has proven to be a valuable approach, with a positive impact on both students’ experiences and teachers’ professional development [92]. Therefore, it is essential that teacher training includes the teaching of specific strategies for the use of mobile devices, in order to optimize the pedagogical advantages that this technology provides and create more dynamic and effective learning experiences.

7. Limitations and Future Studies

The study’s primary limitation is related to its scope. Although the characteristics of m-learning may vary in different contexts, this work only focused on higher education and did not address other educational levels. Additionally, the search was restricted to a single database and several parameters, such as language, and time period, which could have excluded some relevant studies.
Future studies could extend these findings by addressing the relationship between m-learning and learning outcomes in various educational levels and cultural contexts. Specifically, it would be interesting to explore whether in the Latin American context, the integration of m-learning tools in classrooms also has the potential to improve learning outcomes and the development of critical thinking skills or whether variables such as technological infrastructure, digital competencies, and others would limit its benefits.
Another aspect to consider is the influence of the COVID-19 pandemic, which may have introduced unique and significant factors into the relationship between m-learning, learning outcomes, and critical thinking. Future research focusing exclusively on this context could provide valuable additional insights.
Also, to obtain a more accurate and robust estimate of the impact of m-learning, it would be beneficial to adopt analytical approaches such as meta-analysis, which allows combining the results of different studies in the area in a quantitative and statistically supported way, this technique can provide a more accurate and robust estimate of the effect of m-learning on learning outcomes and the development of critical thinking skills.
Overall, technology is constantly and rapidly evolving, so the study of m-learning becomes relevant in updating knowledge about its use. Studies in this field address the challenges of its integration into classrooms, the associated benefits, and the best practices for its effective implementation. Likewise, they analyze how m-learning can improve learning outcomes, foster critical thinking, and adapt to the changing needs of students and educators.

Author Contributions

The authors L.P.-R., C.M.-F., E.R.-P. and D.L. collaborated in all parts of the study. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by ANID-Chile, Fondecyt project 1210542.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to thank ANID (Chile) for its support and funding.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Characteristics of articles addressing the relationship between m-learning and learning outcomes.
Table A1. Characteristics of articles addressing the relationship between m-learning and learning outcomes.
Main FocusAuthor(s)Participants *Data Collection **Main FindingsConclusions
Pre-TestPost-Test ***
Adaptive mobile learning systemsEnnouamani et al.
[44]		64 s-year students in the field of Informatics at Ibn Zohr University (Morocco)Pre-tests, intermediate tests and final tests to evaluate the students’ knowledge of JAVA programming.CG
0 ≤ score < 40%: 43.75%
40 ≤ score < 80%: 56.25%
score ≥ 80%: 0%
EG
0 ≤ score < 40%: 53.13%
40 ≤ score < 80%: 46.88%
score ≥ 80%: 0%		CG
0 ≤ score < 40%: 31.25%
40 ≤ score < 80%: 62.50%
score ≥ 80%: 6.25%
EG
0 ≤ score < 40%: 6.25%
40 ≤ score < 80%: 25.00%
score ≥ 80%: 68.75%		There is a noticeable difference between the two groups after the final test, compared to the beginning of the experiment. The initiative contributes to improving the students’ level of knowledge.
ChatbotVázquez-Cano et al. [45]103 students at the Universidad Nacional de Educación a Distancia (Spain)Initial scoring test in Spanish similar to the entrance exam and final scoring test with more complex content.Mean CG: 23.57
Mean EG: 22.90		Mean CG: 28.47
Mean EG: 32.13		Significant differences (p < 0.01) in final test scores between CG and EG.
Cloud-based m-learningChang et al.
[46]		123 students from four different classes at a university in Taipei (Taiwan)Pre-test (scores obtained from a previous task) and post-test.-Usefulness
Mean CG: 5.02
Mean EG: 6.79
Novelty
Mean CG: 4.00
Mean EG: 5.25
Creative performance
Mean CG: 6.42
Mean EG: 7.24		The EG scored significantly higher (p < 0.01) than the CG in terms of overall creative performance, as well as the usefulness and novelty of the products evaluated.
Chang et al.
[47]		62 students from two different classes at a university in Taipei (Taiwan)Pre-test (scores obtained from a previous assignment) and post-test.-Total creativity
Mean CG: 23.14
Mean EG: 29.49
Novelty
Mean CG: 4.12
Mean EG: 5.63
Value
Mean CG: 4.62
Mean EG: 4.83
Elaboration
Mean CG: 3.67
Mean EG: 5.32		A significant difference in overall creative performance was observed between the groups (p < 0.01). The use of cloud-based mobile learning had a positive impact on students.
Educational application/platform with multiple functionalitiesArain et al. [25]212 students on a Communication Skills course (Pakistan)Pre-test and post-test to evaluate learning achievements.Mean CG: 7.83
Mean EG: 7.75		Mean CG: 13.75
Mean EG: 16.69		Learning achievement improved in both groups. However, post-test averages indicate that the EG performed better compared to the CG (p < 0.01).
Chung et al.
[48]		119 students on the Electric Welding Practice course, Department of Engineering Science.Pre-test and post-test on knowledge of electric welding technology and safety.-Technology
Mean CG: 11.08
Mean EG: 11.91
Safety
Mean CG: 11.20
Mean EG: 12.15		The EG achieved greater learning effects than the CG in knowledge of electric welding technology and safety after the intervention (p < 0.05).
Fonseca et al.
[49]		84 students who sat for the final exam of the organic chemistry course at the University of Algarve (Portugal).Final exam grades.--The scores of students who used the app were higher than the scores of those who did not use it (p < 0.05).
Hu & Wang
[50]		106 students on an ethnomusicology course at Zhejiang University (China)Final evaluation and formative assessment to measure students’ knowledge.-2018/2019 cohort
Mean CG: 58.1
Mean EG: 71.9
2019/2020 cohort
Mean CG: 54.5
Mean EG: 62.5		EG students showed a higher mean score on the final evaluation compared to CG in both cohorts.
Kayaalp & Dinc
[51]		30 students from the Computer Engineering Department of Muş Alparslan University (Turkey)Average final exam grades.-Mean CG: 51.91
Mean EG: 68.71		The application positively impacted students’ knowledge of algorithms.
Mergany et al.
[52]		67 dental students from two universities (Sudan)Pre-test and post-test to assess knowledge of dental surgical forceps.Mean CG: 5.87
Mean EG: 5.94		Mean CG: 5.42
Mean EG: 8.34		The mean scores of the pre-test and post-test of the EG revealed significant differences (p < 0.01).
Oyelere et al.
[53]		142 students from the computer science program of the Modibbo Adama University of Technology Yola (Nigeria)Pre-quiz and post-quiz to assess knowledge of systems analysis and design.Mean CG: 13.83
Mean EG: 13.80		Mean CG: 44.75
Mean EG: 50.65		The EG outperformed the CG in post-quiz performance scores (p < 0.01).
Tezer & Çimşir
[54]		70 students from Giresun University (Turkey)Pre-test and post-test to assess web design knowledge.Mean CG: 41.35
Mean EG: 40.21		Mean CG: 60.10
Mean EG: 68.30		A significant difference (p < 0.01) is observed in the means of the final test. The application is effective in terms of increasing academic performance.
Zhonggen et al.
[55]		340 students enrolled in College English IV in the university (China)TOEFL test. Four testing items: reading comprehension, listening comprehension, speaking, and writing.-Mean CG: 86.31
Mean EG: 87.49		Proficiency in English as a foreign language in the EG is significantly higher than in the CG (p < 0.05).
Zhu
[56]		119 Offshore Oil and Gas Engineering students (China)Final test aimed at measuring theoretical knowledge and basic applications.-The average score and excellence rate of students who used the application are 14% and 600% higher than those who did not use it.The application helps students understand theoretical concepts more clearly.
Immersive experiencesChin et al.
[13]		63 students from a liberal arts course at Aletheia University (Taiwan)Pre-test and post-test to measure the level of knowledge about historic buildings.No significant difference (p > 0.05) between the two groups.Comprehension
Mean CG: 14.69
Mean EG: 27.93
Average score
Mean CG: 58.12
Mean EG: 73.98		Augmented reality (AR) technology improved the learning performance of students in the EG, especially in terms of comprehension and assimilation of the instructional content (p < 0.01).
Chin & Wang
[57]		72 students from two different classes of the cultural heritage course of the Aletheia University (Taiwan)Pre-test and post-test to gauge knowledge levels regarding historic buildings.Retention
Mean CG: 28.34
Mean EG: 26.81
Total score
Mean CG: 38.60
Mean EG: 36.86		Retention
Mean CG: 40.68
Mean EG: 45.17
Total score
Mean CG: 70.05
Mean EG: 76.54		EG students achieved better learning outcomes on the post-test compared to CG (p < 0.05). The analysis also revealed significant differences in retention questions (p < 0.01) between the two groups.
Moro et al.
[58]		38 students from a biomedical or health sciences program (Australia)Pre-test and post-test to measure students’ level of knowledge of brain anatomy and physiology.Mobile-based AR
Mean: 3.84
HoloLens
Mean: 4.16		Mobile-based AR
Mean: 11.05
HoloLens
Mean: 11.89		There was no significant difference observed in the test scores between the two groups (p > 0.05). The use of mobile device-based AR can be just as good at helping to improve outcomes as other more complex technological equipment.
Briz-Ponce et al.
[59]		30 students from the Faculty of Medicine at the University of Salamanca (Spain)Pre-test and post-test to measure participants’ knowledge of anatomy.Mean CG: 2.67
Mean EG: 2.20		Mean CG: 2.4
Mean EG: 3.6		The performance of students using the interactive 3D application was superior to those using traditional methods (p < 0.05).
Chang et al.
[60]		100 students from a nursing school (Taiwan)Pre-test and post-tests were conducted on the nursing skills required for nasotracheal suctioning and medication administration, in addition to psychomotor assessments.Knowledge
Mean CG: 69.3
Mean EG: 69.4		Knowledge
Mean CG: 74.4
Mean EG: 80.9
Skill: Medication administration
Mean CG: 5.29
Mean EG: 6.33
Skill: Nasotracheal suctioning
Mean CG: 5.79
Mean EG: 6.70		Learning clinical practice through a virtual simulation environment in a mobile application would allow students to obtain higher scores in knowledge and skills (p < 0.01).
.
Hanson et al.
[61]		225 students taking a pharmacology course (Australia)Pre-test and post-test to measure students’ knowledge.CAVE2
Mean: 2.43
Handheld device
Mean: 2.26		CAVE2
Mean: 3.83
Handheld device
Mean: 3.75		No evidence was found that students experienced disadvantages in knowledge acquisition when using either method. In both cases, the average increase in scores was statistically significant (p < 0.01).
Interactive tool versus traditional text-booksJeno et al.
[15]		71 students on a mandatory biology course (Norway)Test to measure student achievement levels in species identification.-Mean CG: 5.95
Mean EG: 7.78		Students who used the app achieved higher test scores compared to those who used the textbook, with significant differences observed between the groups (p < 0.05).
Arauz et al.
[62]		300 students enrolled in a systemic pathology course at Ross University (Saint Kitts and Nevis)Neuropathology test scores, where questions were classified as RQ, NRQ, and NNQ according to their linkage to iBook content. The final overall grade for the course was also evaluated.-RQ (neuropathology iBook related questions)
Mean non-users: 69.88
Mean users: 66.95
Course final grade
Mean non-users: 83.38
Mean users: 81.55		Students’ use of the interactive iBook did not have a significant impact on their neuropathology test scores or their final course grade.
Game-based learningde la Peña Esteban et al.
[63]		90 engineering students (2 cohorts) from Madrid Open University (Spain)Overall academic performance of the Industrial Systems Optimization Techniques course.-2017/2018 cohort (available game)
Mean of those who did not use the game: 57.7
Mean of those who did use the game: 68.7		Students who used the game scored 11% higher on the test.
Ramírez-Donoso et al.
[64]		294 students on the online course “Systems Programming” at Universidad Carlos III de Madrid (Spain)Final course grades.-Number of videos watched
Average of those who did not use the app: 51.2
Average of those who use the app: 127.75
Number of exercise-problems solved
Average of those who did not use the app: 36.46
Average of those who use the app: 88.87		The use of the mobile application correlates with a higher consumption of didactic resources and a higher pass rate in the course.
Wilkinson et al.
[65]		246 students (2 cohorts) of anatomy at Middlesex University (UK)Four evaluations throughout the course.
A1: Anatomical microstructure
A2: Lower limb
A3: Upper limb
A4: Trunk and nervous system		-They did not use the game (NG)
Mean A3: 46.9
Mean A3-A2: −6.3
They used the game (G)
Mean A3: 57.2
Mean A3-A2: 3.6		There were statistically significant differences in the mean A3 test score between the groups (p < 0.01). In addition, in group G there was a significant increase in A2 to A3 scores (p < 0.05), while in group NG there was a significant decrease (p < 0.05).
Troussas et al.
[66]		80 students of Computer Science (Greece)Average grades obtained in C# programming course.-Class A (app with peer collaboration and advice generator)
Mean: 6.23
Class B (Conventional app)
Mean: 7.75		A significant difference was found between the average grades of the groups (p < 0.01). However, students who used the enhanced app with new features did not outperform those who used the conventional app.
Mobile instant mes-sagingAndújar-Vaca & Cruz-Martínez
[67]		80 students on a B1 level English course at the University of Almeria (Spain)English speaking test at the beginning and end of the course.Pronunciation
Mean CG: 1.23
Mean EG: 1.10
Grammar
Mean CG: 14.25
Mean EG: 21
Vocabulary
Mean CG: 7.70
Mean EG: 9.85
Fluency
Mean CG: 4.70
Mean EG: 4.88
Comprehension
Mean CG: 10.65
Mean EG: 13.9		Pronunciation
Mean CG: 1.80
Mean EG: 2.25
Grammar
Mean CG: 15
Mean EG: 23.40
Vocabulary
Mean CG: 12.4
Mean EG: 16.8
Fluency
Mean CG: 5.45
Mean EG: 8.65
Comprehension
Mean CG: 10.4
Mean EG: 16.47		The post-test revealed statistically significant differences (p < 0.05) in the five dimensions evaluated between the groups.
So
[68]		61 students at a teacher training institute (Hong Kong)Pre-test and post-test to evaluate students’ knowledge of the course.Mean CG: 18.23
Mean EG: 18.16		Mean CG: 27.45
Mean EG: 31.01		Participants in the EG with the WhatsApp intervention performed better than those in the CG (p < 0.05).
Wang et al.
[69]		55 freshman students (China)Pre-test, post-test and delayed post-test to measure students’ English vocabulary level.Mean CG: 29.00
Mean EG: 20.11		Post-test
Mean CG: 45.30
Mean EG: 52.64
Delayed post-test
Mean CG: 42.70
Mean EG: 46.96		The EG had better results (p < 0.05) in the post-test. There were no significant differences in the delayed post-test.
Mobile peer assessmentLiu et al.
[70]		44 students (China)Pre-test and post-test to measure students’ vocal music ability.-Mean CG: 16.60
Mean EG: 17.36		There were significant differences (p < 0.01) in vocal musical performance between the two groups.
Mobile-based word cardsLi & Hafner
[71]		85 medical students (China)Pre-test and post-test to measure the level of English vocabulary knowledge.Total scores
Mean CG: 28.62
Mean EG: 28.43		Total scores
Mean CG: 60.51
Mean EG: 69.57		Significant difference (p < 0.05) in mean gain scores between the two groups.
Mobile-supported learning analytics interventionsCavus Ezin & Yilmaz
[72]		49 students on a Basic Information Technology I course (Europe)Pre-test and post-test to measure students’ knowledge of the fundamental contents of the course.Mean CG: 22.37
Mean EG: 25.44		Mean CG: 38.25
Mean EG: 43.16		EG obtained higher scores on the post-test compared to CG students (p < 0.01).
Mobile-supported reflective learningMartin & Ertzberger
[73]		103 students on pre-service teacher preparation courses (USA)Pre-test and post-test to measure students’ knowledge of artistic content.Mean
No reflection: 2.61
Self-guided reflection: 2.63
Reflection with virtual expert: 3.15 		Mean
No reflection: 5.13
Self-guided reflection: 6.42
Reflection with virtual expert: 7.85		Significant differences in post-test scores were observed among the three groups (p < 0.05). The importance of a virtual expert in here and now m-learning environment is highlighted.
Mobile-supported task-based teachingZheng et al.
[74]		60 freshman students majoring in educational technology, psychology, environment, chemistry, or management science.Pre-test (university entrance exam) and post-test (developed by teachers) to assess students’ English proficiency.Mean CG: 137.16
Mean EG: 138.40		Mean CG: 70.33
Mean EG: 82.83		The learning achievements of the EG were significantly higher than those of the CG (p < 0.01).
Fang et al.
[75]		66 students of English as a foreign language (Taiwan)Midterm scores and written post-test to measure students’ English language achievement.-Vocabulary
Mean CG: 4.00
Mean EG: 6.17
Conversation comprehension
Mean CG: 6.67
Mean EG: 8.47		The EG outperformed the CG on vocabulary and conversation comprehension tests (p < 0.01).
Location-based contextual learning systemsChin et al.
[12]		62 students in two concurrent classes at the College of Humanities, Aletheia University (Taiwan)Pre-test and post-test to assess students’ knowledge of cultural heritage.Retention
Mean CG: 28.40
Mean EG: 29.25
Comprehension
Mean CG: 12.27
Mean EG: 12.31		Retention
Mean CG: 40.99
Mean EG: 45.32
Comprehension
Mean CG: 28.27
Mean EG: 31.53		Significant differences in post-test scores between the two groups (p < 0.05).
Chang et al.
[76]		137 students on an English as a foreign language course (Taiwan)Pre-test and post-test of four parts.Vocabulary
Mean CG: 10.87
Mean EG: 11.09
Grammar
Mean CG: 6.37
Mean EG: 8.00
Reading
Mean CG: 6.57
Mean EG: 7.14
Writing
Mean CG: 5.50
Mean EG: 6.01		Vocabulary
Mean CG: 12.76
Mean EG: 17.02
Grammar
Mean CG: 11.38
Mean EG: 12.12
Reading
Mean CG: 10.41
Mean EG: 13.92
Writing
Mean CG: 13.34
Mean EG: 17.88		The initiative improved academic performance in English, showing significant differences in both groups in the post-test (p < 0.01).
M-learning (general)Chu & Kong
[77]		102 students (China)Final test on environmental knowledge--Students with m-learning had better scores on all 3 dimensions of the test (natural knowledge, problem knowledge and action strategy) compared to those who received traditional instruction (p < 0.01).
Ehsanpur & Razavi
[32]		54 primary school students from Islamic Azad University who chose the language course (Iran)Pre-test, post-test and retention test (conducted 8 weeks after the end of the course).-Learning post-test
Mean CG: 5.25
Mean EG: 5.97
Retention test
Mean CG: 4.61
Mean EG: 6.02		The learning and retention rate of the EG is higher than that of the CG.
Technology-enhanced sports instructionHung et al.
[14]		225 students on a badminton course (Taiwan)Pre-test and post-test to assess students’ badminton skills.Conventional Course Group
Mean: 4.60
Tablet Course Group
Mean: 4.47		Conventional Course Group
Mean: 5.34
Tablet Course Group
Mean: 5.65		A statistically significant difference was found in the post-test scores between the two groups (p < 0.05).
Chiang et al.
[78]		326 university students taking a Physical Education–Basketball course (Taiwan)Pre-test and post-test. The aspects evaluated were correctness of moves, maneuverability, teamwork, sense of balance, and adaptability of the students.Learning performance
Mean FCA: 8.31
Mean PT: 7.96
Mean TT: 8.39		Learning performance
Mean FCA: 20.44
Mean PT: 18.07
Mean TT: 13.65		The learning performance of students in the FCA (Flipped Classroom with app) group was better than those in the PT (Projecting Teaching) group and those in the TT (Traditional Teaching) group.
* The term “participants” refers to the total number of students who were included in the study and whose learning outcomes were measured. ** Method of data collection in relation to learning outcomes. *** The adjusted mean is presented in the studies that used analyses such as ANCOVA and MANCO

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Applsci 14 09105 g001
Figure 1. PRISMA Flow diagram.
Figure 1. PRISMA Flow diagram.
Applsci 14 09105 g001
Table 1. General characteristics of selected articles (n = 50).
Table 1. General characteristics of selected articles (n = 50).
Categories%
Year
20168.0
201710.0
201814.0
201910.0
202016.0
202128.0
20226.0
20234.0
20244.0
Journal
British Journal of Educational Technology8.0
Interactive Learning Environments8.0
Computer Applications in Engineering Education6.0
Computers & Education6.0
Education and Information Technologies6.0
Universal Access in the Information Society6.0
Australasian Journal of Educational Technology4.0
BMC Medical Education4.0
IEEE Transactions on Learning Technologies4.0
Innovations In Education and Teaching International4.0
ReCALL4.0
Others40.0
Table 2. General characteristics of the articles addressing the relationship between m-learning and learning outcomes.
Table 2. General characteristics of the articles addressing the relationship between m-learning and learning outcomes.
Categories%
Region
Asia53.7
Europe24.4
Africa7.3
America4.9
Oceania4.9
Not specified4.9
Area
Foreign language17.1
Health sciences and human/animal biology17.1
Computer science and programming17.1
Cultural heritage/historic buildings7.3
Engineering (industrial, electric, offshore oil and gas)7.3
Language, grammar and communication7.3
Chemistry/biology4.9
Music4.9
Education (pre-service)4.9
Sports4.9
Creative performance4.9
Environment2.4
Number of Participants
Less than 5012.2
Between 50 and 10046.3
More than 10041.5
Table 3. Main focus of the intervention and relevant findings.
Table 3. Main focus of the intervention and relevant findings.
Main FocusRelevant Findings
Adaptive mobile learning systemsAdapting the content and format (video or text) according to the students’ learning style had the potential to improve their knowledge of the programming language [44].
ChatbotUsing this tool improved the scores in the final test. Significant differences exist between the scores obtained by the CG and EG [45].
Cloud-based m-learningIt had a positive effect on students’ creative performance [46,47]. It improved both the creative process (problem presentation and answer generation and validation) and the development of creative products.
Educational application/platform with multiple functionalitiesUsing educational applications with multiple functionalities (e.g., including class material, forum, chat room, multimedia material, interactive exercises, games, and others) had the potential to positively influence student learning outcomes [25,48,49,50,51,52,53,54,55,56].
Immersive experiencesUsing augmented reality (AR) technology on mobile devices improved students’ learning performance, especially in the areas of observation, including a deeper understanding of physical objects [13], and retention [57]. The use of mobile device-based augmented reality can be just as good at helping to improve outcomes as other more complex technological equipment (e.g., Microsoft HoloLens) [58]. Using 3D image-based mobile learning [59] and virtual simulation applications [60,61] also helped to improve final test scores.
Interactive tool versus traditional textbooksEmploying an interactive app to identify sedge species was found to be more effective in improving student test scores than using a conventional textbook [15]. Meanwhile, no statistically significant differences in mean test scores were found between users and non-users of an interactive iBook, although a low student use of the initiative was highlighted [62].
Game-based learningStudents who used the game/quiz app obtained higher pass rates and improved their test scores, generally outperforming those who did not use the tool [63,64,65]. However, one study [66] revealed that the conventional version of the game, without the advanced functionalities, showed better results in terms of learning achievement, suggesting that the benefits of these improvements may depend on the context and specific needs of the students.
Mobile instant messagingEG participants who used instant messaging applications (WhatsApp, WeChat) to view and share multimedia material, check doubts, and interact with peers, among others, obtained better results in the final tests than the CG [67,68,69]. However, this difference was not observed in the delayed test [69].
Mobile peer assessmentWSQ (watch–summary–question)-based peer assessment via a mobile app improved students’ vocal music skill [70].
Mobile-based word cardsBoth EG and CG improved vocabulary learning; however, EG had better test scores [71].
Mobile-supported learning analytics interventionsCustomized interventions (feedback, hints, and others) based on learning analytics helped students achieve better final test scores [72].
Mobile-supported reflective learningMobile device-supported learning with a significant component of reflection (self-guided or facilitated by a virtual expert) contributed to improved student achievement [73].
Mobile-supported task-based teachingTask completion via mobile devices improved students’ reading comprehension [74], conversational comprehension, and vocabulary [75] in English.
Location-based contextual learning systemsA mobile instructional game positively affected language learners’ learning outcomes (reading, writing, and vocabulary) [76]. Correspondingly, a ubiquitous guidance-learning system improved students’ learning achievements in the Cultural Heritage course [12].
M-learning (general)M-learning positively influenced students’ knowledge acquisition [77] and content retention [32].
Technology-enhanced sports instructionUsing mobile applications and multimedia content improved students’ sports skills and knowledge [14,78].
Table 4. Characteristics of articles addressing the relationship between m-learning and critical thinking.
Table 4. Characteristics of articles addressing the relationship between m-learning and critical thinking.
Author(s)ParticipantsContextData CollectionMain Findings Conclusions
Asiri et al.
[31]		60 third-year students from the Department of Electronics and Computer Science at the University of Southampton (UK).Using mBCI (mobile-based behavior change intervention) technology to deliver real-time feedback to engineering students in the context of a research project.Online questionnaires before and after the intervention to obtain students’ opinions.
Evaluations of the work by academic supervisors and experts.		57% of the students found the activities useful for learning about critical thinking.
Participants in both groups (CG and EG) received low ratings from academics for all nine critical thinking standards.		Students’ self-perceptions of their improvements in critical thinking skills did not match with the results of their teachers’ evaluations.
Ehsanpur & Razavi
[32]		54 primary school students from Islamic Azad University that chose the language course (Iran)Assessing the effect of traditional and mobile education.Learning and study strategies are measured using the LASSI questionnaire, which is based on students’ opinions.Self-regulation
Mean CG: 94.73
Mean EG: 95.66
Skill
Mean CG: 66.50
Mean EG: 70.50		The EG’s level of self-regulation is superior to that of the CG (p < 0.05), particularly in the areas of time management, self-testing, and concentration. Additionally, students who used m-learning perceived better information processing skills (p < 0.05).
Hu & Hwang
[1]		56 first and second-year students on a Museum Introduction Course (China).Using the self-adaptive mobile CMPP (concept mapping-based problem-posing) approach within the context of a virtual museum. The students took notes and screenshots to complete the teacher’s task sheet, then formulated a question and attempted to answer it using a self-adaptive concept map on their mobile devices.Student self-perception questionnaires were used.Critical thinking
Mean CG: 3.66
Mean EG: 3.88
Metacognition
Mean CG: 3.49
Mean EG: 4.05
Problem-solving
Mean CG: 3.56
Mean EG: 4.00
Computational thinking
Mean CG: 3.19
Mean EG: 3.58		The proposed strategy could significantly improve students’ higher order thinking skills such as critical thinking (p < 0.05), metacognition (p < 0.01), problem solving (p < 0.01) and computational tendency (p < 0.01).
Hwang et al.
[79]		36 s-year students from a nursing school.Proposing a problem, intervention, comparison, and outcome approach based on mobile device-assisted peer assessment. Nursing students can use the app to read, organize, and criticize scientific articles.Questionnaire for critical thinking ability.Mean CG: 4.12
Mean EG: 4.22		The EG who learned through the proposed approach significantly improved self-perceived critical thinking scores (p < 0.05).
Inel-Ekici & Ekici
[9]		80 students from the Science Education and Primary Education departments of Usak University (Turkey).Implementing and comparing the effects of face-to-face inquiry-based learning (IBL) and mobile inquiry-based learning (m-IBL).Participants’ experiences were measured through a questionnaire with open-ended questions.Students who participated in the activity perceived an increase in their awareness of scientific inquiry and learning retention. They further highlighted that it helped them learn meaningfully and improved their thinking skills. The mobile app augmented the effects of the traditional method.
Jodoi et al.
[80]		Two studies were conducted. The first study included 73 students from two Japanese universities, while the second study involved 114 students divided into 3 main groups (Group A = University A, Group B = University B, Group C = People who had experience in debate activities).Developing a mobile application and testing its effects (with and without gamification) on developing critical thinking skills.Study 1
Questions on reasoning and critical thinking.
Study 2
Pre-test and post-test to measure critical thinking skills.		Study 1
Average answer rates
University A: 0.76
University B: 0.85

Study 2
With gamification
Group A
Pre-test: 0.80
Post-test: 0.89
Group B
Pre-test: 0.77
Post-test: 0.91

Without gamification
Group A
Pre-test: 0.77
Post-test: 0.88
Group B
Pre-test: 0.82
Post-test: 0.96		The app helped improve students’ pre-and post-test scores (p < 0.05), which included questions related to verbal reasoning, logical reasoning, and judgment skills. However, no apparent effect of gamification was observed.
Khachan & Özmen
[81]		50 students from Introduction to Computer Networks, Discrete Mathematics, Data Mining, and English Language courses (Turkey).Introducing the IMSSAP application (interactive mobile-learning student support app) designed to integrate social interaction and education.Questionnaire to measure student perception.Critical thinking
Mean: 3.72		Students felt that the app helped them improve their critical thinking and analytical skills.
Parsazadeh et al.
[82]		67 s-year students from the Computer Science Department of a university in Kuala Lumpur (Malaysia).Featuring a mobile application that seeks to improve online information evaluation skills.A pre-test and post-test (multiple choice and writing task) on online information evaluation skills was conducted.The Mann-Whitney U test revealed significant differences (p < 0.01) in post-tests between EG and CG students in both multiple-choice tasks and written essays.The app significantly improves students’ online information evaluation skills more effectively than the traditional method.
Song & Cai
[83]		60 first-year students of Philology from the Qiqihar University (China).Using the game application Lumosity: Brain Training in Students’ Learning Process and Evaluating its Effects.A pre-test and a post-test based on the Critical Thinking Skills Success methodology were conducted.Pre-test
Mean CG: 14.92
Mean EG: 22.13
Post-test
Mean CG: 15.12
Mean EG: 24.50		The EG improved their critical thinking skills compared to their initial skills. Additionally, they significantly outperformed the CG (p < 0.05).
Wu
[84]		228 students on an Information Ethics and Law Course.Assessing the effects of using a mobile application to gamify learning activities.Objective evaluation of reviewers.Classroom with gamified app
More than 70% of the students showed a level of application or analysis of knowledge in their exercises. More than 60% performed a satisfactory analysis of the evidence. More than 70% presented a logical organization with good connections between ideas, and more than 80% achieved a logical and interesting sequence in their text.		The class that used games generally presented good levels of performance in content knowledge, analysis, synthesis and organizational skills.
Zheng et al.
[74]		60 first-year students majoring in educational technology, psychology, environment, chemistry, or management science. Examining the effects of using a mobile self-regulated learning approach. This system allows students to set goals and plans, monitor learning processes, reflect on themselves, and self-assess.Self-regulated learning questionnaire.Mean CG: 4.67
Mean EG: 5.13		The students who used this new system improved their self-regulated learning skills, showing higher levels than the CG (p < 0.01).
Zhu
[56]		119 Offshore Oil and Gas Engineering students (China).Exploring the Rain Classroom mobile application which incorporates real-time communication, records the teaching process during classes, integrates learning activities, and assesses student perceptions.Student perception questionnaire.Understanding of the theoretical knowledge
Mean: 4.65
Memory and logic thinking abilities
Mean: 4.62		Students consider that this tool improved their comprehension, memory, and logical thinking skills.
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Pedraja-Rejas, L.; Muñoz-Fritis, C.; Rodríguez-Ponce, E.; Laroze, D. Mobile Learning and Its Effect on Learning Outcomes and Critical Thinking: A Systematic Review. Appl. Sci. 2024, 14, 9105. https://doi.org/10.3390/app14199105

AMA Style

Pedraja-Rejas L, Muñoz-Fritis C, Rodríguez-Ponce E, Laroze D. Mobile Learning and Its Effect on Learning Outcomes and Critical Thinking: A Systematic Review. Applied Sciences. 2024; 14(19):9105. https://doi.org/10.3390/app14199105

Chicago/Turabian Style

Pedraja-Rejas, Liliana, Camila Muñoz-Fritis, Emilio Rodríguez-Ponce, and David Laroze. 2024. "Mobile Learning and Its Effect on Learning Outcomes and Critical Thinking: A Systematic Review" Applied Sciences 14, no. 19: 9105. https://doi.org/10.3390/app14199105

APA Style

Pedraja-Rejas, L., Muñoz-Fritis, C., Rodríguez-Ponce, E., & Laroze, D. (2024). Mobile Learning and Its Effect on Learning Outcomes and Critical Thinking: A Systematic Review. Applied Sciences, 14(19), 9105. https://doi.org/10.3390/app14199105

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