Adoption of mobile application for enhancing learning in higher education: Students’ views from the State University of Zanzibar, Tanzania

Abstract

Mobile (m)-learning is a technology-enhanced learning approach utilizing mobile technologies to facilitate learning. Despite significant attention from previous researchers, there is a lack of adequate studies about student’s usage and perceptions of m-learning in Zanzibar’s’ higher learning institutions (HLIs). In this study, the researchers explored the specific activities undertaken by students through the m-learning system, the students’ perceptions on the usefulness and ease of use of m-learning system, and the challenges they faced at the State University of Zanzibar (SUZA). The study used the survey method with descriptive statistical analysis and examined 240 randomly selected participating in the m-learning initiative. The findings indicate that 96.7% of participants easily used the system to navigate learning resources, 92.9% viewed assignments, and 94.6% took quizzes. Additionally, the majority of the respondents showed a positive attitude regarding the usefulness and ease of use of the system. However, 57% of participants experienced challenges, including unreliable internet connectivity and system incompatibility with some of their devices. The findings of this study will help HLIs like SUZA to find better strategies to improve m-learning, especially pedagogical features and awareness among students. Moreover, the study underscores the importance of more research to discover factors that influence m-learning adoption in Africa HLIs.

Keywords:

• adoption
• higher learning institution
• mobile learning
• blended learning
• usefulness
• ease of use

Introduction

In the past decade, the use of mobile technology like mobile phones has increased in the educational sector, especially in higher learning institutions (HLIs) worldwide. The use of mobile technology in education is referred to as mobile learning or m-learning. According to Yousafzai et al. (2016), m-learning is a learning process where learners are not restrained by fixed locations and can benefit from access to learning materials through mobile devices. Also, m-learning system enables students to use smartphones to access learning resources from a learning management system (LMS) without spatial and time limitations (Mtebe and Kondoro 2016; Haji, Shaame, and Kombo 2013).

Owing to the proliferation and cost-effectiveness of mobile devices, m-learning has become possible and ideal in many HLIs worldwide since many new millennium students have access to mobile devices. HLIs effectively leverage the m-learning approach to provide alternative ways for e-learning (Kaliisa and Michelle 2017). Mtebe (2015) argues that the appropriateness of using mobile technology for learning promotes access to e-learning for many students. Some studies reported that m-learning reduces the problem of a severe shortage of instructors and large classroom settings since mobile-based learning materials can be accessed and personalized at anytime and anywhere (Petersen 2020). However, despite the spread of mobile devices in sub-Saharan Africa, HLIs still lag in the adoption of mobile devices to extend education delivery, while evidence about m-learning adoption and effectiveness is scarce (Mtebe and Kondoro 2016; Kaliisa and Michelle 2017; Kaisara and Kelvin 2022). For example, it has been reported by Haji, Shaame, and Kombo (2013) that in Zanzibar, students in higher education use mobile devices for self-learning purposes, but there is no official m-learning system that has been established in any of the HLIs for supporting the effective delivery of education in a formal way. This has also been supported by the study conducted by Mwandosya (2021) in the Tanzania context. Although there are some studies which highlight the adoption and use of m-learning technology in Tanzania and Africa as a whole (Kaisara and Kelvin 2022; Ghasia et al. 2018; Kaliisa and Michelle 2017; Mwandosya 2021), there is little knowledge about m-learning usage in HLIs in the Zanzibar context.

Therefore, this study investigated students’ perceptions regarding the adoption and use of the mobile LMS at the State University of Zanzibar (SUZA). Challenges of using the m-learning application were also investigated.

To achieve the objectives of the study, the following questions were posed and addressed:

1. What are the major activities that SUZA students perform when accessing the m-learning system?

2. What are the students’ perceptions regarding the usefulness and ease of use of mobile system for supporting learning at SUZA?

3. What challenges do students face while using the m-learning system at SUZA?

Case study description

SUZA is an emerging public university in Zanzibar. Zanzibar is a semi-autonomous part of the United Republic of Tanzania that constitutes of two large islands namely Unguja and Pemba, and several other small islands. Currently, this university has nine schools found in different locations on both Unguja and Pemba islands. Due to the growth of the university in terms of the number of students and campuses, the university realized the usefulness of information and communication technology (ICT) to support the delivery of education. Hence, the university made efforts to ensure that ICT is used in providing education. Such efforts included the introduction of a web-based LMS (Moodle) in 2012 (Mgeni et al. 2019). Later, in 2018, SUZA launched a mobile system that aimed at extending the use of the LMS via mobile devices. In the initial phase, the mobile application was used to facilitate learning activities of selected courses as listed in Table 2.

Mobile learning application development and configuration at SUZA

According to Zamfirache et al. (2013), there are two methods of accessing learning systems through mobile devices: a mobile browser and a native mobile application. SUZA adopted a native m-learning application called mobile Moodle. Mobile Moodle is a native learning application that provides a quick and easy access mobile interface through mobile devices for web-based Moodle which is called Mobile LMS (Mtebe and Kondoro 2016).

The Moodle mobile application was customized for Android mobile phones as most students possess Android-based smartphones. It was customized based on SUZA’s requirements. Figure 1 shows the m-learning application interface. Then, the synchronization between the SUZA web-based Moodle LMS server and the customized m-learning system was carried out to ensure that the course materials available in the web-based Moodle LMS could also be accessed easily via the mobile system.

Figure 1: Interface of m-learning system for accessing course materials.

Theoretical framework

As far as technology adoption is concerned, both the adoption process and the adopted technology must be assessed to understand what is to be improved. Consequently, scholars have developed various theories that guide studies related to the assessment of the adoption and use of new technological innovation in different sectors, including education The traditional theories that are commonly employed in assessing technology adoption and acceptance, include diffusion theory of innovation, the theory of planned behaviour (TPB), the theory of reasoned action (TRA), the unified theory of acceptance and use of technology (UTAUT), and the technology acceptance model (TAM) (Arain et al. 2019). These theories have been used in numerous studies, depending on the context and the needs of the problem that must be addressed (Abuhassna et al. 2023; Mwandosya 2021; Almaiah, Alamri, and Al-Rahmi 2019), (Arain et al. 2019).

Technology acceptance model (TAM)

The technology acceptance model (TAM), which was employed in this study, is one among a number of technology acceptance theories. TAM, which was developed by Davis in 1989, is currently a very common theory for assessing technology innovation acceptance in education (Mwandosya 2021; Petersen 2020; Fathema, Shannon, and Ross 2015). TAM consists of five major constructs: perceived usefulness, perceived ease of use, attitude toward use, intention to use and actual usage (Mwandosya 2021). It is a good base for quantitatively analyzing users’ attitudes regarding innovative technologies and their acceptance of such technologies (Venkatesh, Thong, and Xu 2016). In the case of this study, SUZA students used m-learning system as the new technological innovation. Apart from system usage and challenges of m-learning that were examined in this study, two TAM factors – perceived usefulness and perceived ease of use – were also examined. Davis (1989) posited that perceived ease of use and perceived usefulness are important factors that build confidence in using new information technology (e.g., m-learning technology). Perceived usefulness is defined as ‘the degree to which an individual believes that using a particular system would enhance his or her job performance. Perceived ease of use, on the other hand, is defined as ‘the degree to which an individual believes that using a particular system would be free of physical and mental effort.’ TAM underwent further development to become TAM2 (Venkatesh and Davis 2000). TAM2 includes external factors that better explain technology acceptance, which include social influence, as well as cognitive and instrumental processes. Both TAM and TAM2 are widely used in studying technology adoption and forecasting user behaviour regarding the adopted technology (Almaiah and Alismaiel 2019), although this research did not focus on evaluating TAM2 factors.

Literature review

Research on describing the development of m-learning and on analyzing the adoption and use of m-learning in HLIs has been conducted in numerous countries, including many in Africa. This section reviews and analyzes some studies which are closely related to the current study.

Mwandosya (2021) investigated the use of mobile educational tools for enhancing innovative teaching and learning in four higher education institutions in Tanzania. The findings showed that students in all four HLIs used electronic-based learning systems for learning, but the use of mobile systems for formal learning was rare, with those students who used mobile devices using them for self-learning purposes. The shortcomings of this study are the lack of participants’ perceptions on the use of mobile educational tools. In addition, none of Zanzibar’s universities participated in this study.

Petersen (2020) also conducted a study to examine students’ attitudes towards using a mobile LMS in a large classroom setting. This study was based on the TAM theory. Findings from the study indicated that perceived usefulness and perceived ease of use positively influenced students’ attitudes towards using mobile LMS. The study, however, was conducted in South Africa, a country that differs from Zanzibar both economically and culturally.

Mtebe and Kondoro (2016) reported on the adoption of a m-learning client at the University of Dar-es-Salaam (UDSM). They also reported on students’ opinions on mobile client usage and availability. The results revealed that 95% of the participants acknowledged that the mobile system was easy to use and provided support for their learning. Furthermore, the study revealed that the cost of internet is the main m-learning challenges the students face. However, the finding of their study only shows the UDSM students’ opinion on the adoption of m-learning. It should be noted however that UDSM is a leading university in Tanzania and much older than SUZA.

The research work conducted by Hu et al. (2015) showed that students were using mobile browsers to access Moodle LMS activities on their mobile devices instead of a native mobile application. In this study, the researchers collected data to investigate how often the students used mobile devices to access different learning activities and elicited their opinion on mobile LMS usage. Respondents suggested that among the aspects that required much attention were usability and reliability of mobile access. These aspects were found to be the main limitations in this study. These limitations were also reported in the study conducted by Papadakis et al. (2018).

Asiimwe and Hatakka (2017) conducted a small-scale evaluation by interviewing m-learning support staff in order to understand their perceptions of the effectiveness of the Mobiclass in teaching and learning. Mobiclass is a m-learning system adopted by the University of Makerere in 2011 for extending learning. The results revealed that the majority of the students owned smartphones and used them to access course content, as well as for collaboration and interaction. However, poor mobile infrastructure, poor m-learning instructional and content design, lack of awareness of m-learning opportunities, lack of m-learning policies, lack of awareness of existing policies, and insufficient teacher training were reported as challenges. The study, however, was conducted in Uganda, a country that differs from Zanzibar culturally. In addition, the University of Makerere is older than SUZA. The challenges of m-learning were also reported in a study conducted by Masika et al. (2015). In their study, challenges like the lack of smart devices, lack of technical know-how in accessing or using apps, sub-optimal internet access, cost of acquiring apps, and limited device memory were reported.

Kaliisa and Picard (2017), in their study, identified the challenges of incompatibility of mobile devices with the university LMS, low mobile battery life and the inability to save and store large files on the mobile phones.

Other studies, for example by Al-Said (2015) and Asiimwe and Hatakka (2017), have also analyzed m-learning application challenges based on the FRAME evaluation model. FRAME is a m-learning evaluation model consisting of three dimensions: device, learner and social. Regarding the device aspect, the challenges were poor mobile infrastructure and high infrastructure costs. The learner aspect is related to poor m-learning instructional and content design practices and low awareness of m-learning opportunities. Non-collaborative, interactive, and unresponsive learning platforms are associated with the social aspect.

Although there are some challenges in m-learning implementation, m-learning is a potential innovative approach to deliver education at tertiary level (Ahmad 2020). Hence, more studies on m-learning are needed that will help to design and implement better m-learning strategies, especially in the African context. Unlike the previous studies discussed in this section that emphasized the adoption of m-learning and the students’ perception, this study investigated students’ perception on m-learning technology usage in HLI in the context of Zanzibar, where there is limited knowledge in the literature.

Research methodology

This section presents the methodology of the study.

Research design

A descriptive survey research design was used in this study. A descriptive survey is a systematic method of gathering data from a sample of individuals through questionnaires or interviews to describe a population’s characteristics, attitudes or behaviours (Siedlecki 2020). The study utilized a questionnaire to gather quantitative and qualitative data from the students who were part of a m-learning pilot project at SUZA.

Sample and sampling techniques

SUZA has about 5,000 students with more than 60 different programmes, ranging from certificates to Doctor of Philosophy programmes. This study was conducted with a target population of 585 students who participated in the m-learning project in the academic year 2019/2020. Of them, a total of 240 students were voluntary and randomly selected to participate in this study. The study participants participated in completing a questionnaire survey from July 2020 to August 2020. The detailed characteristics of the participants are shown in Tables 1 and 2.

Table 1: Participants’ information by gender, age, study campuses and years of study.

	N = 240
Parameter	Frequency	%
Gender
Male	94	39.2
Female	146	60.8
Age
16–20	40	16.7
21–25	186	77.5
26–30	10	4.2
31–35	4	1.7
36+	0	0
Years of Study
First Year	121	50.4
Second Year	75	31.3
Third Year	44	18.3
Campuses
Tunguu	144	60
Nkrumah	34	14.2
Mbweni	29	12.1
Mchanga Mdogo	33	13.8

Note: N = Total number of students

Table 2: Participants’ study programme and study courses.

SNo	Semester/	Study	Course title	N = 240
	Year	Program		Frequency	%
1.	Semester II	Bachelor	CS/INF 1209 – Database Systems	18	7.5%
	2019/2020	Degree
2.	Semester I	Bachelor	ED 2103 – Educational Resources, Media and Technology I	41	17.1%
	2019/2020	Degree
3.	Semester II	Bachelor	INF2202 – IS Strategy Management and Acquisition	36	15%
	2019/2020	Degree
4.	Semester I
	2019/2020	Diploma	MLT04106 – Prevention and Control of Diseases Transmission	29	12.1%
5.	Semester I
	2019/2020	Diploma	PDCH 0212 – Physical Chemistry II	33	13.75%
6.	Semester I
	2019/2020	Diploma	PDED 0022 – Introduction to Educational Resources, Media and Technology	83	34.6%

Research instrument

An online questionnaire with both closed and open-ended questions was formulated. Closed question types include single-choice, multiple choice, and Likert-scale questions. Open-ended question provide the opportunity for participants to share in-depth experiences of the phenomena (Welsh et al. 2015). Our questionnaire consisted of different sections based on the research questions. Some of the question items were prepared by the researchers and then reviewed by a prominent researcher from SUZA. Other items were adapted from various published sources (Peramunugamage, Usoof, and Hapuarachchi 2019; Davis 1989). The final questionnaire was administered via students’ WhatsApp groups for the collection of data from the participants.

Reliability and validity of the study

Reliability and validity are terms used to evaluate the quality of research. Reliability refers to how research results are consistent and stable, while validity indicates how well the research measures what it intends to achieve and how accurately it reflects the reality of the fact of study (Smit et al. 2021; Drost 2011). To ensure the reliability and validity of the study, the data collection instrument underwent review by researchers who provided feedback on the items' alignment with the intended responses. Subsequently, a pre-test of the questionnaire was carried out in the classrooms of each programme participating in this study. Besides, the researchers, in collaboration with instructors, guided the students on how to accurately fill out the survey questionnaire, following the suggestions outlined by Johanson and Brooks (2010) regarding pilot study procedures. The observation revealed that students understood the questions easily and accurately filled out the questionnaire based on their opinions. Consequently, it is expected that the research instruments used in this study would consistently produce similar results if employed under similar situations on multiple occasions within any HLI globally that shares the same environment as SUZA.

Data analysis

Data were analyzed quantitatively and qualitatively. The quantitative responses were analyzed by using SPSS version 20 to describe and present data in an easy and accessible quantitative format. This process helped the researchers to illustrate and sum up observations. The findings were presented through the use of descriptive statistics (frequency, percentages, mean and standard deviations), graphs and charts. The qualitative responses were thematically analyzed, which involved data familiarization, initial code generation, organizing codes into themes, reviewing themes and naming them (Welsh et al. 2015, cited in Braun and Clarke 2006).

Findings

Demographic information

In this section, the participants were asked about their gender, age, study campus, years of study, study courses and programmes. The findings revealed that 240 students who participated in this study were from four SUZA campuses: Tunguu, Nkrumah, Mbweni and Mchanga mdogo. The findings also revealed that 94 (39.2%) of the participants were male, and 60.8% were female. In terms of age, years of study, study courses and study programmes, the distribution in terms of frequency and percentages is shown in Tables 1 and 2, respectively.

Mobile devices used by the participants for m-learning

In addition, the participants were asked about the mobile devices they used to access the m-learning system. The results of their responses are shown in Table 3.

Table 3: Mobile devices used by the participants for m-learning.

Parameter	N = 240
Mobile device used by the participants	Frequency	%
Smartphone	228	95
Laptop	8	3.3
Tablet	4	1.7

Students’ use of a mobile learning system

This section of the questionnaire examines both the frequency of using the mobile system for learning as shown in Figure 2 and the learning activities performed by students through m-learning system. The findings are shown in Table 4.

Figure 2: Frequency of using the mobile learning system.

Table 4: Learning activities accessed through mobile application.

Learning activity	Classification	Frequency	%
Accessing Learning resources	Yes	232	96.7
	No	8	3.3
Viewing Assignment	Yes	223	92.9
	No	17	7.1
Submitting Assignment	Yes	216	90
	No	24	10
Online Interaction	Yes	163	67.9
	No	77	32.1
Online Collaboration	Yes	128	53.3
	No	112	46.7
Taking Quiz	Yes	227	94.6
	No	13	5.4

Learning activities accessed through a mobile system

In this question, the respondents were required to answer either ‘Yes’ if they were using a mobile m-learning system to perform the learning activities listed in Table 4 or ‘No’ if otherwise. The majority of the respondents responded ‘Yes’ in all identified learning activities as follow: 96.7% access learning resources; 92.9% view assignments; 90% submit assignments; 94.6% take quizzes; 67.9% engage in online interaction; and 53% engage in online collaboration. The findings indicate that the majority of the respondents seem to be very familiar with the uses of the mobile system in their studies. This implies that the existing technology is becoming increasingly important in the learning process in HLIs. This tendency not only emerged in SUZA but Asiimwe and Hatakka (2017) also revealed that majority of the students owned smartphones and used them to access course content as well as for collaboration and interaction in HLIs.

Participants’ perception on usefulness and ease of use of a mobile learning system

This part of the research was designed to assess the participants’ perceptions and attitudes on the usefulness and ease of use of the mobile system for m-learning. The questions in this section consisted of a 5-point Likert scale, ranging from (1) ‘Strongly Disagree’ to (5) ‘Strongly Agree’. The respondents were asked to indicate their levels of agreement by choosing one of the scales in the question items based on their views. Table 5 presents the calculated weighted average (mean) for the question items. Mean represents the general average of the participants’ responses, while standard deviation specifies the extent to which the participants’ responses to a question deviate from the mean.

Table 5: Participants’ perception on usefulness and ease of use of mobile learning system (N = 240).

Sno	TAM factor	Item	Mean	Std. Deviation
1	Perceived usefulness of mobile learning system	The structure of the learning content in mobile Moodle is organised to support learning	3.18	1.369
2		Moodle app enable me to interact with other students and the teacher	3.10	1.453
3		Learning activities were accomplished more easily	3.08	1.385
4	Perceived ease of use of mobile learning system	I am OK with Interface Moodle mobile app	3.13	1.366
5		It was very easy to use Moodle mobile app	3.10	1.378
6		The online materials can be accessed from anywhere	3.10	1.450
7		I feel confident to learn with Moodle Mobile app	3.13	1.358

Note: Ratings are based on a 5-point Likert-type scale: 1 – ‘Strongly Disagree’, 2 – Disagree ‘, 3 – ‘Neutral’, 4 – ‘Agree’, and 5 – ‘Strongly Agree’.

The findings indicate that the mobile device is crucial and indispensable in the learning process in the new learning approach and the development in its technologies. It facilitates independent learning and a student-centred approach while also making learning materials accessible.

Challenges facing participants while using m-learning

This objective of the study required the respondents to indicate whether they faced any challenges while using the m-learning system. Of the total, 57% said ‘Yes’ while 43% said ‘No’ as shown in Figure 3.

Figure 3: Participants’ challenges while using m-learning.

The challenges identified related to the functionality of the mobile application, such as the slowness of downloading materials and the small size of the font in some activities. Other major challenges were related to ICT infrastructure that support the implementation of the mobile LMS. These included the availability of reliable internet connectivity on the university campuses. Some participants also said that students do not have sufficient knowledge of the m-learning application. Another issue was related to the language barrier (English language) as the medium of instruction that was used to facilitate instruction through the m-learning system. This was mostly reported by diploma students as they said the application contains some technical terms that are difficult for them to understand. Similarly, missing of notification alerts for new updated learning information in the application, cost of internet bundles, the disappearance of courses from students’ dashboards, and registration on the application were minor challenges reported by some students. The findings showed that although m-learning has a significant and pivotal role at SUZA, several limitation and challenges still persist which are hindering the usefulness of the existing technologies.

Discussion

Mobile LMS is contemporary learning technology in many HLIs in developing countries. Its main aim is to improve classroom learning and extend learning remotely, as there is high penetration of mobile devices (Mtebe and Raisamo 2014). Based on the findings obtained from answering question one of this study, the mobile LMS seemed to be a potential platform for accessing and performing various learning activities regardless of spatial and time constraints. The findings for addressing research question one showed that the majority of students used the mobile LMS to access different learning activities and learning resources, as well as to view and submit course assignments, take quizzes and perform online collaborations and interactions. This finding also aligned with the findings of various other studies. For example, the findings of studies by Papadakis et al. (2018) and Hu et al. (2015) found that most of their participants used mobile devices to access learning resources, view assignments and take quizzes. Mtebe and Kondoro (2016) reported similar findings as they also found that students in HLI used mobile LMS to access and view course materials rather than interacting with entertainment activities.

Based on the TAM theory, the adoption process of a new system by the user is much dependent on the perceived usefulness and perceived ease of use of the adopted system. In the context of this study, the adopted system was a m-learning system which was intended to effectively improve and extend learning at SUZA. The findings of this study rest on two major constructs of the TAM theory, perceived usefulness and perceived ease of use, as articulated by Davis. The centrality of these constructs is shown by the fact that the mean score of each question item in Table 5 is above 3, indicating that majority of students showed a positive attitude with regard to usefulness and ease of use of the mobile LMS for supporting their learning. This finding addresses research question two that looked at whether most of the participants perceived the usefulness of the mobile system to support their learning as well as its ease of use.

Despite the fact that the m-learning application seemed to be useful in supporting students’ learning, some challenges were reported by the participants. The most significant challenge reported by the majority was internet connectivity. Although the learning materials and activities can be accessed all the time in offline mode via a mobile application once have been downloaded, internet connectivity is critical when students want to download the newly uploaded learning content. The issue of internet connectivity is a reality in developing nations because most the universities experience a lack of reliable internet connectivity (Kaliisa and Picard 2017). The same challenge has also been reported by other scholars, such Mtebe and Kondoro (2016) and Masika et al. 2015. The issue of lack of knowledge about m-learning had also been reported by scholars (Hasan 2018).

One of the important findings in this study is the issue of incompatibility of some mobile devices with the mobile system. Although this challenge was not mentioned by the majority of the study participants, it is important to ensure that m-learning is accessible to all learners’ devices. This challenge was also highlighted by Kaliisa and Picard (2017) in their study that investigated the challenges of m-learning in the African context, among others. This finding addressed research question three which focused on identifying challenges that hinder smooth implementation of m-learning at HLIs. These include lack or slowness of the internet, lack of knowledge of m-learning applications, and the language barrier – a foreign language used as a medium to access learning materials and as a medium of instruction.

In spite of the issues and challenges identified in this study, as well as in other previous studies related to adoption and implementation of m-learning technologies (Asiimwe and Hatakka 2017; Kaliisa and Picard 2017; Hasan 2018), it is evident that m-learning is in progress in HLIs in developing countries.

Ultimately, m-learning, since it provides students with opportunities to exploit learning resources and activities in both online and offline modes, should be extended by HLIs, in m-learning adoption and implementation, in collaboration with private and government institutions.

Recommendations

Based on the findings of this study, it seems that the majority of students used the m-learning system to perform various learning activities like navigating learning resources and viewing assignments. Similarly, the findings show that there are some challenges that may hinder m-learning. The main challenge described by the majority of the participants was internet connectivity. Thus, to better implement and extend m-learning at SUZA, as an early adopter of the platform, the university must strengthen its ICT infrastructure, especially its internet connectivity. Another critical challenge recognized from this study is the incompatibility of mobile devices and the m-learning system, resulting in some students failing to have access to resources via their mobile devices. Future research should consider the incompatibility issue to find ways to make sure that the m-learning system is accessible to all students, regardless of the type of mobile devices they use and its operating system (Android, MAC, or Windows). Additionally, m-learning awareness and training are very important, especially for computer-illiterate students, although in this study, this concern was pointed out by a minority of the participants. This observation is supported by previous research, including a study conducted by Asiimwe and Hatakka (2017).

Further, for better implementation of m-learning in HLIs, particularly in developing countries where resources are limited, there is a need to improve both the interface and content of the developed m-learning platform before extending it to be used across the entire university.

Conclusion

M-learning has been widely used in higher education to extend electronic learning via mobile devices. The main focus of this study was to find out the perception of students who were part of a m-learning pilot project at SUZA as one of the universities in a sub-Saharan Africa country. This study found a number of opportunities and challenges associated with m-learning at SUZA. In general, the findings show that most of the participants have positive attitudes toward adopting and using a mobile system for learning. This is because m-learning is features flexible learning materials and activities. In terms of challenges, however, the participants revealed that internet connectivity is a major challenge in m-learning. In addition, they indicated that the structure of the content in the mobile system must be improved.

Finally, the findings of this study can help early adopters of m-learning in higher education to improve m-learning administration and find proper strategies that will take full advantage of m-learning for enhancing the delivery of education. The study findings also provide some research opportunities. For instance, since the present study was limited to student participants’ perceptions on the adoption of m-learning, qualitative research could be done to discover detailed perceptions and opinions of both students and lecturers. Additionally, researchers could increase the sample size by involving more HLIs in Zanzibar and Tanzania in general. It is crucial to realise that the impact of m-learning in higher education cannot be measured unless there is a research agenda.

Author details

Table

S/No	First name	Family name	Email address	Contribution of authors
1.	Mwanajuma	Mgeni	[e-mail]	Constructed the idea and wrote the paper
2.	Haji	Haji	[e-mail]	Reviewed the paper, wrote some parts of the paper.
3.	Said	Yunus	[e-mail]	Data collection and data analysis
4.	Ali	Abdulla	[e-mail]	Data collection and data analysis

Availability of data and materials

Data are available from the authors upon reasonable request.

Acknowledgements

We acknowledge the support from Build Stronger University (BSU) Project from DANIDA Denmark, and the State University of Zanzibar.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors gratefully acknowledge the financial support for this study from the Build Stronger University (BSU) Project.