Mobile Use in the Classroom is a Mixed Bag,and Lecturers Need to Provide Students With Guidelines

Abstract

In the era of modern life, the use of mobile devices in the classroom helps to enhance the overall performance of students, however, it has some harmful effects on academic performance. Therefore, this study attempts to identify the association between mobile phone use and the academic performance of the students of Jahangirnagar University, Bangladesh. Structural Equation Modeling (SEM) is used for identifying the relationship among variables. Results revealed that about 80% (269) of the students use mobile phones during class, among them, 65.05% (175) use mobile phones for browsing social sites and texting purposes and 24.54% (66) use them to send and receive phone calls. Moreover, students’ academic results are significantly (p < .001) associated with whether students use their mobile phones in the classroom. The results of SEM depict that positive features including recording lectures, support to study, remainder to-do lists, etc., and other features of the mobile phone have played a role positively in improving academic performance, however, different types of distractions have an adverse effect on the academic performance of students. This study explored the relationship between students’ academic performance and mobile applications that help to provide guidelines for students about mobile in the classroom.

Keywords

mobile phone academic performance SEM university students Bangladesh

Introduction

In the era of modern life, mobile technology has become part and parcel of everyone’s daily life. In recent times, mobile technologies become more functional and can replace laptops for ease and more accessibility in our lives (Ally, 2013), therefore, it is possible to do any technology-based activities at any time from anywhere. As a result, the rate of using mobile phones is increasing among people with any background including university students. Among the higher education level students in Bangladesh, approximately 87% have smartphones (Ahmed, 2020). The ownership of mobile phones is rapidly increasing among university students (Dahlstrom & DiFilipo, 2013; Kobus et al., 2013). University students use mobile phones for various purposes such as social media, communication, academic activities, etc. (Rahman, 2014). Results from an earlier survey indicated that students’ primary uses of their phones were for contact and conversation, followed by information retrieval or search (Yasan-Ak & Yildirim, 2024). The usage of mobile phones among university students impacted their academic performance from both positive and negative dimensions based on their purpose and duration of usage (Amez & Baert, 2020). There is a conflict between attitudes toward smartphones as a useful tool for learning and socialization in the digitally transformed classroom and as an anomaly to the intended digital transformation, possibly endangering the classroom’s social and disciplinary order (Wikström et al., 2024). According to the study’s findings, scientific lab exercises facilitated by mobile devices and applications improved preservice teachers’ digital literacy and attitude toward integrating technology in the classroom (Ürek, 2024). Researchers found a positive correlation between mobile phone attachment and learning in the classroom; however, to minimize negative effects, it is important to control phone usage in the classroom (Urien & Courage, 2024).

In a positive sense, a lot of opportunities for academic learning are now open for students who own a personal mobile device (Ally & Tsinakos, 2014). A study reported that using mobile devices in the classroom had a notable positive impact on literacy (Dorris et al., 2024). Students can use mobile devices as a helping hand for their daily academic activities such as taking notes, answering questions, researching, communicating, etc. (Gikas & Grant, 2013; Jackson, 2013; Santos, 2013; Tian & Zhou, 2023). Smartphones enable students to access internet-based services anywhere, enhancing the continuous search for study-related information and offering a multimedia learning platform, potentially enhancing online learning through the provision of helpful information, digital materials, and the utilization of educational tools, mobile learning (m-learning), and apps (Amez & Baert, 2020; Fu et al., 2021; S. F. A. Hossain, Shan, & Nurunnabi, 2019; Miah et al., 2018). Additionally, the use of social networking and communication apps can foster quicker information sharing, promoting efficient collaboration and studying (Amez & Baert, 2020). Furthermore, students may access the online session from their smartphones (Shakoor et al., 2021). Therefore, mobile phone use may have a positive impact on academic performance. Some studies conducted in the out-context of Bangladesh showed that mobile phone usage is positively associated with the academic performance of a student (S. F. A. Hossain et al., 2020; S. F. A. Hossain, Nurunnabi, et al., 2019; Pulliam et al., 2019; Zhao et al., 2018).

On the other hand, many aspects of using mobile phones can interrupt the academic activities of university students. University students often view smartphones as sources of entertainment rather than tools for academic work, potentially leading to a time trade-off between smartphone use and study-related activities (Amez & Baert, 2020; Lepp et al., 2013). This may have a detrimental impact on academic performance, as smartphone use can interfere with focus and contribute to multitasking or task-switching (Amez & Baert, 2020; Q. Chen & Yan, 2016; Junco, 2012; Junco & Cotten, 2012). It was believed that mobile phones distracted students from their studies and jeopardized their safety due to improper use (such as sharing and taking pictures and videos while in class), cyberbullying, and problems with social media (Gath et al., 2024). Factors such as visual and auditory notifications, the fear of missing out (FOMO), addiction behavior, and a lack of academic motivation are identified as potential causes that might disrupt student’s attention during study-related activities (Q. Chen & Yan, 2016; Junco & Cotten, 2012; Samaha & Hawi, 2016). Moreover, several studies reported that phone ringing or vibrating, sending text messages, reading or sending emails, using social sites, etc., are the common reasons for distraction from learning in the classroom (Bajamal et al., 2023; Baker et al., 2012; Geist, 2011; Mayxay et al., 2013; Thomas et al., 2013). Therefore, the usage of mobile phones has an adverse effect on students’ academic performance (M. M. Hossain, 2019). In addition, mobile phone use has a severe negative effect on their physical and mental health including low sleep quality and quantity, anxiety, loneliness, depression, dissatisfaction with life, more social relationships, substance addictions, attention deficit, and hyperactivity disorder (Amez & Baert, 2020)—all of them have direct and indirect negative effect on the academic performance (Lepp et al., 2014). Many studies conducted in the out-context of Bangladesh explored that the usage of mobile phones is significantly negatively correlated with the student’s academic performance measured by grade point average (GPA)—the value of the correlation coefficient ranges from −.079 to −.380 (Alosaimi et al., 2016; Amez & Baert, 2020; Asante & Hiadzi, 2018; Bravo-Sánchez et al., 2021; R.-S. Chen & Ji, 2015; Felisoni & Godoi, 2018; Ibrahim et al., 2018; Janković et al., 2016; Kim et al., 2019; Lepp et al., 2015; Li et al., 2015; Lin & Chiang, 2017; Petrucco & Agostini, 2023; Rosen et al., 2018; Samaha & Hawi, 2016; Uzun & Kilis, 2019; Witecki & Nonnecke, 2015).

A growing body of literature highlighted both of positive and negative effects of mobile phone usage on the student’s academic performance outcomes in terms of cumulative grade point average (CGPA) appears to be circumstantial. Nevertheless, there is a scarcity of understanding regarding the impact of mobile phone usage on the academic performance of university students in the context of Bangladesh. To fill up this backdrop, this study aimed to explore the relationship between mobile phone use and academic performance (CGPA).

Methodology

Study Area

This study considered Jahangirnagar University, Bangladesh which is a public research university established in 1970. In Bangladesh, it is the only university that is entirely residential.

Research Design

The materials used in this study were adopted from the existing literature and understanding of the authors. A structured questionnaire was prepared considering dichotomous questions (yes/no), multiple-choice questions, and a 5-point Likert scale consisting of strongly agree (1), agree (2), neutral or don’t know (3), disagree (4), and strongly disagree (5). Some of the questions were adapted from Tindell and Bohlander (2012), McCoy (2013), and Santos and Bocheco (2014). The authors assigned a code to each binary response question (e.g., yes = 1; no = 2) as guided by Cohen et al. (2017).

To begin, the data’s reliability was assessed using Cronbach’s Alpha (Cronbach, 1951), a scale that ranges from 0 to 1, with a number near to 1 indicating greater reliability (Nunnally & Bernstein, 1994). Cronbach’s Alpha of .70 is considered acceptable (Nunnally, 1978; Zikmund, 1999). The Cronbach’s Alpha can be defined as $α = \frac{K \bar{c}}{(\bar{v} + (k - 1) \bar{c})}$ , where $K$ is the number of components ( $K$ items), $\bar{v}$ is the average variance of every component (item) and $\bar{c}$ is the average of all covariances between the components throughout the current sample of persons that is, excluding the variances of each component.

The summary statistics and frequencies were calculated to observe the background characteristics of the students. A common thought of everyone is that the students who use mobile phones in the classroom have poor academic results. To check this, the association between the variables was measured through the most popular Chi-square test. Moreover, MacCallum and Austin (2000) referred to structural equation modeling as an essential tool that can be used to see the relationship between observed and latten variables along with the effects between them (MacCallum & Austin, 2000). However, the researchers used structural equation modeling to check the effects of mobile phones categorized as “positive, negative, and other features” on their academic performance and the four latent variables along with their casual structures. The effects are including different factors as below,

• Positive Features/Effects: Conducting lessons with the mobile phone, helps to understand class, helps to follow own progress, can improve attention in class, can be used as a clicker to give feedback, can be used as a productivity hub, more connected to the world, give feedback to the instructor, etc. There are 9 items in this group.

• Negative Features/Effects: Can cause distraction from learning, can distract the teacher, can cause memorizing disabilities, can cause to note down the lectures and instructions properly, can cause cheating and health, etc. In this group, there are 6 items.

• Other Features/Effects: This can cause easy access to more information, should make rules for using in the classroom, keep a close eye on students how they are using mobile phones, can be utilized as a learning tool in the classroom, etc. In order to determine other features, 6 items were used.

The complete list of the variables used in the structural equation model is presented in Supplemental Table 1. There are three exogenous or independent latent variables that are positive features/effects ( $X_{1}$ ), negative features/effects ( $X_{2}$ ) and other features/effects ( $X_{3}$ ). On the other hand, the structure had only one endogenous or dependent latent variable that is, academic result ( $Y$ ). However, the structure is assumed as a linear regression (Figure 1).

Figure 1.

Theoretical framework.

Students use online search engines when they don’t comprehend a topic in a course, and it’s expected that they can discover information quickly and efficiently. Moreover, a mobile phone can be used as a recorder for recording lectures in the classroom, this helps later to understand the lectures, improve their skill in digital materials, texting a question to a teacher because they are embarrassed to raise their hand which helps you to clear your confusion in any lecture, a mobile phone can be used as productivity hub for challenge-based learning-remainder to-do lists, etc. The authors assumed that the above applications of mobile phones may impact academic performance positively. On the other hand, the authors think that the items such as can cause distraction from learning, can distract the teacher, can cause memorizing disabilities, can cause cheating which is unfair to other students who work hard to do well and mobile phones can cause to note down the lectures and instructions properly may have a negative influence on results. Finally, a mobile phone can be utilized as a learning tool in the classroom, should make rules for use in the classroom, keeping a close eye on students to observe how they are using mobile phones, and so on, considered for measuring the other effects on academic performance. It is well known that there is a connection between the consecutive results of a student. The authors also assumed that there is an inter-linkage among the effects and it is presented in the theoretical framework (Figure 1).

Sources of Data

The participants of this study were selected from two faculties of Jahangirnagar University from different academic years. The sample size is determined by $n = \frac{pq z^{2}}{d^{2}}$ , where, $n$ = sample size, $z$ = standard normal variate (which is 1.96 at 95% confidence interval), $p$ = expected proportion of students using the mobile phone in a classroom (we have no previous information that’s why we use 0.5 since it gives the maximum sample size), $q = 1 - p$ and $d$ = margin of error (which is 5%). Hence, we get the sample size as 385. We send 400 questionnaires but among them, there are five missing questionnaires. So, the final sample size for this study is 395. The primary data were collected from 395 students through the structured questionnaire over the period December 01, 2019, to January 31, 2020, with the help of a stratified random sampling technique. This survey also focused on the demographics of the students, such as their academic performances, ages, genders, academic years, types of mobile phones they use, and a lot more. Before the final survey, the pilot survey was carried out among twenty students, and based on their feedback, a few modifications were made, and some questions were revised respectively. The researchers tried to make the questions more convenient for the students; that’s why the researchers provided printed questions along with an online format of the question in Google form to the respondents. The authors and a team were involved in data collection. Before starting the data collection, participants were asked to partake in the study voluntarily. They were ensured about the confidentiality of their information and individual identity as well. The data collection process began after providing consent to partake in the study. Among the respondents, 205 students (50.4%) from the Mathematical and Physical Science faculty and 190 students (46.7%) from the Arts faculty participated in this study and provided the completed questionnaire.

Measuring Academic Performance

Academic performance is an indicator of how well students did on different evaluation items assigned to them associated with educational criteria defined by the university or country. Assignments, tutorials, quizzes, presentations, viva, and examinations are all used to evaluate a student’s performance in Bangladesh. The unified grading structure proposed by Bangladesh’s University Grants Commission (UGC) has been implemented in all Bangladeshi universities since the 2006 to 2007 academic year. The following table depicts the distribution of marks and accompanying grade points used to assess students’ performance in a course (Table 1).

Table 1.

Academic Performance Measurement Indicators.

Marks	Grade point	Marks	Grade point
80% and above	4.0	55% to less than 60%	2.75
75% to less than 80%	3.75	50% to less than 55%	2.5
70% to less than 75%	3.5	45% to less than 50%	2.25
65% to less than 70%	3.25	40% to less than 45%	2.0
60% to less than 65%	3.0	Less than 40%	0.0

Statistical Package for Social Sciences program (IBM SPSS, version 24.0 and IBM^® SPSS^® Amos, version 24.0) was used for the statistical analysis of the data.

Results

The investigators have gone through the complete questionnaires to check the completeness and accuracy of the questions. As a result, the completed and accurate responses were added to the analysis. Also, Cronbach’s alpha value is .847 for all items included in the structural model suggesting that the items have relatively high internal consistency. For positive, negative, and other dimensions, as well as academic results, the values of Cronbach’s alpha are .806, .751, .895, and .725 respectively. In this study, the sample consisted of 45% (179) males and 55% (216) females. The sample consists of 47% (187) students from the third academic year, 30% (120) students from the second year, and about 22% (88) students from the fourth year and masters. Another wondering possession is almost 60% (232) of the respondents come from an urban area but 85.28% (336) of them are living in the university hall/mess. As the effect of carrying a mobile phone in the classroom either gives a better result or not is one of the main objectives of this study, thus several factors are involved in the structured questionnaire to investigate which reveals that among the respondents more than 85% (338) of the student carry a mobile phone in the classroom and most of them mentioned that they carry a mobile phone in communication, study, and habitual purpose. The most outrageous thing is that most of the students use mobile phones during class time to use social sites and chat through text messages. Findings revealed that about 80% (269) of the students use mobile phones during class, among them, 65.05% (175) use mobile phones for using social sites and texting purposes and 24.54% (66) use them to send and receive phone calls (Table 2).

Table 2.

Frequency Distribution of Different Characteristics of the Respondents.

Variable	Categories	Frequency	Percentage
Gender	Male	179	45.43
Gender	Female	216	54.57
Academic year	Second year	120	30.38
	Third year	187	47.34
	Fourth year	60	15.19
	Masters	28	7.09
Residential area	Urban	232	58.88
Residential area	Rural	163	40.61
Type of accommodation	Hall/Mess	336	85.28
Type of accommodation	Home	59	13.71
Do you carry a mobile phone in class?	Yes	338	85.56
Do you carry a mobile phone in class?	No	57	14.44
Reason to carry a mobile phone in class	To communicate with family	118	29.87
	For study purpose	136	34.43
	It’s a habit to use mobile phone	109	27.60
	For leisure time in class	26	6.58
	Others	6	1.52
Do you use a mobile phone during class?	Yes	269	79.59
Do you use a mobile phone during class?	No	69	20.41
Reason for using a mobile phone during a class	To send and receive phone calls	66	24.54
	To send and receive text messages	59	21.93
	To send and receive emails	14	5.20
	To use social sites	116	43.13
	Others	14	5.20

To gain more insights into the association of academic performance with the residential area, type of accommodation, smartphone user or not, and use of a mobile phone during the time of class were tested through the popular chi-square test. The results of the chi-square test imply that the student’s academic results are strongly associated with their accommodation, residential area, and whether students use their mobile phones in the classroom as the Pearson Chi-square statistic rejects the null hypothesis of no association since the $p$ -value is less than .001. The type of mobile phone used by the respondents has no statistical association with academic performance (Table 3).

Table 3.

Results of Chi-Square Test of Different Factors Related to Academic Performance.

		Academic results (CGPA)		p-Value of chi-square
Factors		2.00–3.00	3.00–4.00	p-Value of chi-square
Type of accommodation	Hall/Mess	125 (37.20)	211 (62.80)	<.001
Type of accommodation	Home	15 (25.42)	44 (74.58)	<.001
Type of mobile phone	Analog	6 (30.0)	14 (70.0)	.601
Type of mobile phone	Smart Phone	134 (35.73)	241 (64.27)	.601
Use mobile phone during class	Yes	68 (29.06)	166 (70.94)	<.001
Use mobile phone during class	No	29 (27.88)	75 (72.12)	<.001
Permanent residential area	Urban	105 (45.26)	127 (54.74)	<.001
Permanent residential area	Rural	35 (21.47)	128 (78.53)	<.001

This study uses structural equation modeling (SEM) designed to check the student’s perception of using a mobile phone in the classroom along with its effects categorized as positive, negative, and other features. Path analysis is used to find an appropriate structural equation model. The results of the goodness of fit of the SEM are presented in Table 4 and it is observed that the structural equation model fit well. As the key objective of this study is to find the effects of using a mobile phone in the classroom on their academic results, thus a regression was performed through the structural equation modeling where the positive, negative, and other features were considered as the independent variable and their academic results were considered as the depended variables.

Table 4.

Results of Goodness of Fit of the Structural Equation Model.

Model Fit Index	Good fit	Acceptable fit	Model value
Chi-square (df)	Non-significant	Non-significant	982.344
Normed Chi-square $(χ^{2} / d)$	$χ^{2} / d < 3$	$3 < χ^{2} / d < 5$	4.142
RMSEA	0 < RMSEA < 0.05	0.05 < RMSEA < 0.08	0.073
RMR	0 ≤ RMR ≤ 0.05	0.05 < RMR < 0.1	0.058
NFI	0.97 ≤ CFI ≤ 1	0.95 ≤ CFI ≤ 0.97	0.958
CFI	0.97 ≤ CFI ≤ 1	0.95 ≤ CFI ≤ 0.97	0.964
GFI	0.95 ≤ CFI ≤ 1	0.90 ≤ CFI ≤ 0.95	0.948
AGFI	0.90 ≤ CFI ≤ 1	0.85 ≤ CFI ≤ 0.90	0.879

Note. RMSEA = Root Mean Square Error of Approximation; RMR = Root Mean Square Residual; NFI = Normed Fit Index; CFI = Comparative Fit Index; GFI = Goodness of Fit Index; AGFI = Adjusted Goodness of Fit Index.

The fitted model shows that all the factors related to positive features and other features got a positive coefficient toward the academic result which implies that, an increase of positive and other features will improve the responded academic performance. Thus, mobile with internet access to use for study purposes, recording class lectures, and study-related apps will help to increase academic performance. Moreover, with the help of mobile phones conduct lessons, access skills in digital material, help an individual to follow their progress, understand a class lecture, introduce digital platforms by mobile into lessons, use as clickers to give feedback to teachers, and productivity hub for challenged based learning-remainder. Moreover, to-do lists can also be a useful thing to ensure the better performance of a student. However, making rules for using mobile phones and giving them access to use them for a particular study purpose can help a student to improve their academic performance. On the other hand, the negative path coefficient of the negative features indicates that they have a significant negative effect on academic performance which implies that frequent occurrence of these factors will affect the responded academic result. Thus, the distractive mobile user can distract the learning, and the negative effects such as memorizing disability, cheating, disconnection from face-to-face activities, health problems, and using social sites during class time can decrease the academic performance of the students (Figure 2).

Figure 2.

Path coefficients of the structural equation model.

Discussion

Our daily lives, academic life, and culture are inextricably linked to cell phones. Many of the education-enhancing abilities of an internet-connected computer are available to students through modern “smartphones,” including online information searching, file sharing, and engaging with professors as well as fellow students. The study’s findings show that almost 80% (269) of students use mobile devices in class for various purposes, with 65.05% (175) using them for social media and texting and 24.54% (66) utilizing them for phone conversations. A previous study highlighted that 70% of respondents utilized their smartphones for educational activities like checking their email, using dictionaries, taking notes, etc. (Mammadova, 2018). It is observed that 34.43% (136) of students carried their mobile phones in class for study purposes. However, in addition to the beneficial aspects of the mobile phone, researchers, policymakers, and others are concerned that excessive use and bad practice have a negative impact on learning outcomes. The findings reveal that the academic performance of a student is strongly associated with the type of accommodation and their permanent residential area in the context of Bangladesh. The reasons for this may be a family’s economic strength, accessibility, and availability of educational institutes are better in urban areas than in rural areas (Huq et al., 2021) and students from urban areas owned advanced mobile phones with Wi-Fi connectivity that impacted the duration of mobile phones. Previous studies also highlighted that the differences in cognitive as well as noncognitive skills have been noted across social classes, and there is a strong correlation between these skills and subsequent academic success (Heckman, 2006; Rothstein, 2004; Sackett et al., 2009; Zwick, 2013).

Our study findings revealed that mobile phone use during class is also significantly associated with academic results. Researchers have noted that using a mobile phone as a teaching tool offers various advantages for the educational process, including improving student engagement in the teaching-learning process and its value as an additional teaching tool (Mohammadi et al., 2020). It is observed that recording the class lecture, to-do lists, taking notes, and using to search for unknown words, and the study-related topic search will help to increase academic performance. The earlier studies pointed out that using a mobile phone has some positive effects on students’ performance incorporating the ease of contact with their classmates, teachers, and parents (Grewal et al., 2020; S. F. A. Hossain et al., 2021; S. F. A. Hossain, Nurunnabi, et al., 2019). Researchers found that educational smartphone apps were a useful learning tool for expanding and acquiring knowledge (Mergany et al., 2021) and supporting our study findings. Over the past 2 years, the use of cell phones by students and academics of all ages has increased dramatically around the world. To foster a more autonomous learning environment, higher education institutions need to better holistic ways of influencing students’ smartphone use (Shakoor et al., 2021). Recently, a study on multitasking revealed that when students use smartphones during classroom lectures, they are multitasking (Shakoor et al., 2021). Researchers measured the effect of multitasking on academic performance, investigated these concerns, and found that many students regularly report using various electronic media, including smartphones, while in-class lectures, reading at home, and completing assignments or homework, and concluded that there is no correlation between multitasking and academic performance (M. E. Hossain & Ahmed, 2016). It is observed that limited use of mobile phones for educational purposes can improve academic performance. The reason behind that they utilize their mobile devices to look for course material, obtain help with homework, access electronic textbooks, and complete computations (Kho et al., 2006). Students utilize smartphones to find, access, and download learning materials from the internet that aid in the understanding of course content, coursework, and assignments (Chaputula & Mutula, 2018; Pullen et al., 2015). The study findings indicate that more than 85% of the students carry a mobile phone in the classroom and mentioned that they use a mobile phone for communication, study, and habitual purposes. This finding is consistent with the previous study (Mwambakulu & Chikumba, 2021). The primary reason students use smartphones for learning purposes is that some universities do not have enough computers for their students and do not have the most up-to-date and relevant texts in their libraries. Due to its wireless connectivity and versatility, mobile phones are used for sharing knowledge, accessing information and services, and finding answers to a range of questions (Derveni & Dagdilelis, 2020; Zogheib & Daniela, 2022). Students who were comfortable with communication using smartphones were more likely to perform better academically (Han & Yi, 2019). Many options are available to enhance teaching and learning due to the widespread use of mobile technology (Qi, 2019). A study suggested using mobile phone usage in teaching to enhance student performance (Mabuza & Osodo, 2020).

On the other hand, study findings revealed that different types of destruction adversely impacted the academic results/performance of a student. These findings are also consistent with the following study findings. The cause of students using social media sites while a lecture is in session is not new, nor is the lecture itself boring (Mwambakulu & Chikumba, 2021). Researchers highlighted that using smartphones distracts students from their studies in particular aspects and impacts students’ academic capabilities and progression (Ifeanyi & Chukwuere, 2018). Moreover, the high levels of smartphone addiction demonstrate that it has an impact on the student’s academic achievement (Kibona & Mgaya, 2015). The worry about losing concentration while learning was more prevalent than the concern of addiction or having access to pornographic materials. Students at universities spend a lot of time on their smartphones, either networking and conversing with their classmates or looking for entertainment. The use of a mobile phone has several negative and unhealthy effects on university students’ performance, including the waste of time and money. Its vibrations and use could be hazardous to one’s health (Grewal et al., 2020; Javid et al., 2011). According to research, one-third of students check their phones frequently due to ringxiety or phantom ring, which is a symptom of mobile phone addiction, and this has an impact on their studies (Subba et al., 2013). The existence of a smartphone, as well as a high phone number of conscious thoughts, has an impact on memory learning and recall, showing that smartphone proximity has a negative impact on learning and memory (Tanil & Yong, 2020). In order to reduce the amount of possibilities that students have to pull out their phones, teachers at school need to be more proactive in the classroom (Armakolas et al., 2024). Therefore, smartphone use in the classroom should be controlled, otherwise, the lecturer’s role will likely be underutilized. Smartphones should be used as a supplement to classroom learning rather than as a replacement.

Implications

The implications of mobile phone use in the classroom are complex and multifaceted. While they offer significant educational benefits, they also pose challenges that require careful consideration and management. Mobile phones provide instant access to the internet, allowing students to look up information quickly, engage in research, and supplement classroom learning with real-time data. A wide range of educational applications and resources, such as interactive simulations, quiz platforms, and language learning apps, can improve learning. These can make learning more engaging and effective. Participants of this study highlighted that mobile phones enable easy communication and collaboration among students and help them work together on projects, share resources, and coordinate group activities more efficiently. Moreover, it can be said that the application of mobile phones in the classroom helps students develop digital literacy skills, which are essential in today’s technology-driven world. This includes understanding how to responsibly use technology and navigate online resources. However, one of the biggest concerns was that mobile phones were a major source of distraction for example, students sometimes used their mobile phone for non-educational purposes, such as social media, texting, or gaming, which can detract from learning. Moreover, concentration on mobile phones may impact the development of social skills and the ability to engage in meaningful classroom discussions. It may be concluded that the presence of mobile phones in classrooms can increase the risk of cyberbullying because students may use their devices to send harmful messages or share inappropriate content, creating a negative classroom environment. Finally, it is challenging to manage classrooms where mobile phones are allowed. Setting and enforcing rules around phone usage requires additional effort and can detract from teaching time. Therefore, balancing these pros and cons is crucial for maximizing the positive impact of mobile phones in educational settings while minimizing potential drawbacks.

Limitations and Further Study

In this study, the authors considered the students only from the one university in Bangladesh; hence, it may not be generalizable to the population of the whole country. Also, a single data collection method, a self-report questionnaire, limits the opportunity for participants to engage in an in-depth conversation about the research questions. The authors propose a qualitative study alongside a quantitative method for future research on smartphone usage and academic performance to understand how students attach meaning to the use of smartphones and the role of smartphone use in improving their academic performance results. Moreover, a future study may include more students from different universities in every part of Bangladesh which will be helpful to make a policy.

Conclusion

Mobile phones and their availability have increased dramatically in recent years. As a result, this study tried to find out the effects of mobile phones on students’ academic results because of using mobile phones in the classroom along with their perception of using mobile phones in the classroom as a learning tool. Several factors are tried to find for using mobile phones in the classroom as a learning tool because students have been using this technology in many ways, however, some of them harm their academic performances. Thus, the results reveal that the most accessible tool in the classroom was mobile phones rather than other tools, though few students also carry other devices in the classroom as per their needs. Both the students from urban and rural have mobile phones and the maximum student has smart mobile phones with an internet connection. Another fact is that the student’s academic results are strongly associated with the type of accommodation, smartphone use during class, and permanent residential area.

This study also suggests that mobile phones should be used as a learning tool in the classroom as they can be used for conducting lessons, understanding a class lecture, recording the class lecture, productivity hub, giving feedback to teachers, and individuals to follow their progress, study-related apps, and there should be some ground rules for using this technology in the classroom. On the other hand, distractions, disconnection from face-to-face activities, cyberbullying, health problems, cheating, and using social sites during class time can also have an adverse impact on academic performance. The authors suggest that the use of mobile phones should be used in the classroom as a learning tool to improve student’s academic performances, however, it is necessary to restrict some destructive applications in the classroom. The authors believe that this study will be helpful in planning a further in-depth study that will investigate some intriguing findings.

Supplemental Material

sj-docx-1-sgo-10.1177_21582440241299481 – Supplemental material for Mobile Use in the Classroom is a Mixed Bag, and Lecturers Need to Provide Students With Guidelines

Supplemental material, sj-docx-1-sgo-10.1177_21582440241299481 for Mobile Use in the Classroom is a Mixed Bag, and Lecturers Need to Provide Students With Guidelines by Mehedi Hasan, Md. Karimuzzaman, Faruq Abdulla and Md. Moyazzem Hossain in SAGE Open

Footnotes

Acknowledgements

The authors are thankful to the participants who voluntarily provided the data for this study. The authors would like to express their gratitude to the four reviewers and Academic Editor for their valuable comments and feedback which helped to improve the quality of the manuscript.

Authors’ Contributions

MH, MK, and MMH initiated and conceptualized the manuscript. MH and MK collected and analyzed the data. Visualization was done by MH, FA, MK, and MMH. The draft manuscript was written by MH, MK, FA, and MMH. MMH critically revised the manuscript for important intellectual content and supervised the whole study. FA and MMH revised the manuscript. The final version is approved by all authors for publication.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical Approval and Consent to Participate

This study was conducted while maintaining ethical standards to the highest possible extent. All participants read, understood, and provided their consent.

ORCID iDs

Faruq Abdulla

Md. Moyazzem Hossain

Data Availability

Data will be made available for the reasonable request to the first and corresponding authors.

Supplemental Material

Supplemental material for this article is available online.

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