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Article

An Empirical Study of ClassPoint Tool Application in Enhancing EFL Students’ Online Learning Satisfaction

by
Abbas Hussein Abdelrady
1 and
Huma Akram
2,*
1
Department of English Language & Translation, College of Science and Arts in Alasyah, Qassim University, Buraidah 52571, Saudi Arabia
2
School of Education, Northeast Normal University, Changchun 130024, China
*
Author to whom correspondence should be addressed.
Systems 2022, 10(5), 154; https://doi.org/10.3390/systems10050154
Submission received: 15 August 2022 / Revised: 10 September 2022 / Accepted: 13 September 2022 / Published: 15 September 2022
(This article belongs to the Special Issue Innovations in Educational Systems and Digital Transformations in Education)
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Versions Notes

Abstract

:
Keeping in view the basic necessity of the time, integrating Information and Communication Technology (ICT) in teaching-learning practices has become an integral part of higher education. In relation to English as a foreign language (EFL) learning, several forms of interactive platform have been applied to enhance students’ engagement, interest and learning satisfaction. Likewise, this quasi-experimental study was designed to investigate the role of ClassPoint tool integrated activities in strengthening e-learning satisfaction of undergraduate female EFL learners in Saudi Arabia. The intervention, i.e., the ClassPoint tool-integrated instruction for the experimental group, lasted for around a month, whereas the students from the controlled group received non-ClassPoint integrated instructions. Subsequently, the data from both groups was analyzed statistically via a t-test. Based on the analysis, the use of ClassPoint tool activities showed a significant increase in the e-learning satisfaction enrichment of EFL learners compared to the non-ClassPoint traditional instruction. Therefore, the study suggests using the ClassPoint tool at all educational levels in all modes of learning to keep learners stay engaged, motivated, and satisfied with their learning.

1. Introduction

The incorporation of information and communication technology (ICT) in teaching-learning practices has become an essential component of higher education as per the prerequisite of the time [1]; thereupon, the traditional, i.e., face to face instructional practices have also transformed into interactive and innovative approaches [2,3]. Technology-integrated modes of instruction not only bring innovativeness to pedagogical approaches [4], but they also help learners’ cognitive abilities flourish [5], strengthen satisfaction [6], foster engagement [7], improve the usefulness of assessments [8], provide instant feedback [9], and help to upgrade desired learning behaviors [10] and academic performance [11]. In the same vein, the Saudi Arabian Ministry of Education also recommends that higher education institutions make the best use of ICT in order to fulfil international requirements and accomplish their educational goals [12]. Simultaneously, online learning technologies have made it possible for all universities in Saudi Arabia to provide remote education successfully [13]. In connection to the EFL instructional methods, the utilization of technology to improve the level of students’ e-learning satisfaction has also been indicated by previous research in the context of Saudi Arabia; for instance, WhatsApp [14], Socrative [15], Gallery Run [16], and Kahoot [17], have emerged as effective applications for increasing student engagement and learning outcomes at all educational levels [18].
However, it is still challenging for EFL teachers to choose, customize, and deploy technology effectively in their instructional practices in such a way that it stimulates students not only for the short term but throughout their studies. In relation to barriers, the learning experiences of students are considered one of the key issues with online learning. Students show dissatisfaction with respect to their e-learning due to a number of academic, technological, and communication challenges, as these barriers do not allow them to attain their expected EFL progress [18]. Whilst student learning satisfaction is regarded as an important determining factor of educational programs’ effectiveness, which helps them achieve academic success [1]. The learner’s perceptions of the degree to which their learning requirements and objectives have been met are regarded as essential elements of the concept of satisfaction [19]. In addition, it is widely accepted as an important measure for determining the effectiveness of computer-based learning practices. According to Serenko (2011) [20], satisfaction can also be estimated by evaluating the discrepancy between the students’ experiences and expectations related to their learning achievements. In a similar vein, the satisfaction of students and the experiences they endure with e-learning practices are key contributors to the accomplishments of higher education e-learning objectives [21]. Keeping this in mind, it is deemed essential to identify students’ learning satisfaction to get familiar with their learning experiences, which depend upon a number of factors, including their level of e-skills [22], their ability to socially and competently participate, and suitable academic supervision provided by e-learning platforms [23]. The student’s levels of contentment with the e-learning experience can also be affected by some other factors, such as appropriate teaching and learning spaces, the student’s ability to apply what they have learned, a connection between online and offline activities, the guidance they receive from other students and instructors, as well as their academic accomplishments [24]. Zou et al. (2022) [6] added that the level of student satisfaction is correlated with the adaptability of e-learning, facilitating conditions, the speed with which teachers respond, the perceived utility of the program, perceived enjoyment, perceived convenience of using the program, and the practicability of the program. Regarding its significance, Kuo et al. (2014) [19] argued that students’ learning satisfaction should be studied since it is considered a crucial component of the outcome of educational endeavors and demonstrates a strong association with academic success.
Therefore, educational institutions must strengthen their students’ learning experiences, so that teaching and learning practices can be transformed according to their needs [23]. Furthermore, realizing students’ learning experiences allows teachers and governing bodies to improve the quality of online courses in order to satisfy them and attain desired educational expectations [25]. In relation to the EFL context, Zou (2020) [26] suggested EFL teachers encourage students to use the latest technological applications to help them stay motivated by engaging in various interactive activities. In contrast, many EFL teachers are not familiar with effective ways to utilize technology-assisted instructional approaches [27]. Therefore, it is necessary to emphasize the effective integration of technological applications in EFL teaching-learning practices. In addition, the active participation of students in an EFL classroom has been accorded paramount importance to raise instructional effectiveness [28,29]. At the same time, in every class, some students raise their hands to answer the teacher’s questions right away, while others hesitate to respond. In relation to the online learning environment, instructors encounter difficulties in making such students participate in the conversations, as they feel too shy to respond. Keeping this in mind, the Classroom Response System (CRS) application is recognized as an effective approach to keep students engaged and to sustain their attention through instant questioning or quizzes during synchronous online or blended educational settings [8]. Aside from helping teachers track students’ knowledge and learning levels instantaneously, the CRS technique also helps students reduce their shyness and test anxiety by allowing them to receive immediate feedback from their instructors in real-time, which also strengthens their e-learning satisfaction [30].
Likewise, the ClassPoint application is an appropriate substitute for the CRS technique, allowing instructors to embed various interactive activities in their Microsoft PowerPoint presentations without switching to another application during teaching. Students can follow along on their devices, and the teachers can get students’ responses synchronously during the class. A teacher can add any of these interactive activities to any existing or newly created PowerPoint file. Students can begin responding to the activity once it is started, and the instructor can review the responses at any time during or after the conducted sessions. In relation to prior studies, Bong and Chatterjee (2021) [31] identified the relationship of ClassPoint-integrated instructional methods in enhancing students’ engagement, but the CRS technique also helps students strengthen their learning experiences and satisfaction [32]. In this regard, there was a critical need to explore the effect of the ClassPoint tool in improving students’ e-learning satisfaction in the setting of an EFL classroom. Therefore, to accomplish these objectives, a CRS substitute named ClassPoint was integrated into online instructional approaches among second-year undergraduate EFL learners at Qassim University. This study gives empirical evidence in the context of Saudi Arabia, which would help instructors make EFL teaching interactive by incorporating several forms of synchronous-based questions, quizzes, or opinion-based polls in their instructional practices. Therefore, the study sought to investigate these research questions:
1.
Does the use of ClassPoint activities help in enhancing the EFL students’ e-learning satisfaction?
Question 1 is guided by the following hypothesis:
Hypothesis 1.
ClassPoint-integrated lessons significantly enhance the EFL students’ e-learning satisfaction in comparison to the non-ClassPoint integrated lessons.
2.
What are the most influential components of e-learning satisfaction for EFL students in both forms of learning environments?

1.1. Theoretical Framework

The social cognitive theory developed by Bandura (1986) [33] serves as the theoretical foundation for the examination of the levels of learning satisfaction experienced by EFL students participating in online educational settings. This theory offers a comprehensive framework for comprehending human behavior and innovative learning abilities. Accordingly, individuals learn by their own and others’ activities, which helps them to develop new analytical capabilities and behavioral patterns. When they yield satisfactory effects, their learning satisfaction and cognitive capabilities would likely be strengthened, whereas unsatisfactory results would tend to decrease their learning satisfaction and cognitive capabilities.
Following the SCT, the study focuses on examining the learner’s experience with ClassPoint-integrated EFL learning practices and their technological competencies, which are affected with the help of three determinants characterized as the components of e-learning satisfaction, which include cognitive, technological, and social environment aspects [see Figure 1].

1.1.1. Cognitive Features

The cognitive features that influence an individual’s behavior while using e-learning platforms are termed cognitive factors. Computer self-efficacy and performance expectations are comprehended as the most significant cognitive elements affecting an individual’s behavior in the utilization of e-learning systems [34]. Performance expectations are viewed as the individual’s beliefs regarding the effectiveness of e-learning systems in their learning outcomes. In relation to the influence of performance expectations, Venkatesh et al. (2020) [35] provided evidence that a student’s performance expectation positively influences their learning outcomes and perceived e-learning satisfaction.
In a similar vein, computer self-efficacy is viewed as the individual’s capability to accomplish their computer-related tasks successfully. The influence of computer self-efficacy on the individual’s learning behavior has been demonstrated by Wolverton et al. (2020) [36], who identified that it helps reduce students’ e-learning barriers and increase e-learning satisfaction.

1.1.2. Technological Environment

The learner’s interaction with appropriate technological infrastructure, e-learning platforms, and course content that influences their utilization behavior in an e-learning system, is termed the technological environment [37]. In relation to components, system functionality and content features are recognized as the most significant elements of the technological environment for an e-learning system.
According to Pituch and Lee (2006) [38], system functionality is comprehended as the individual’s beliefs regarding the usefulness of an e-learning system in providing access to instructional and assessment materials, while technology-based course materials, content, and assessments, are referred to as content.
Prior research has also identified that the usefulness of e-learning platforms play a vital role in enhancing the student’s online engagement and e-learning motivation [39]. In addition, Al-Fraihat et al. (2020) [40] identified in their research that the robustness of the technological system, service, and information, significantly influences the perceived e-learning satisfaction of students. Therefore, it is essential to have technical staff who ensure the functionality of the technological system and resolve technical issues to run the e-learning system smoothly and enable students to satisfy their learning needs.

1.1.3. Social Environment

The learner’s interaction with their teachers and peers, which influences their utilization behavior in an e-learning system, is termed the social environment [37]. Pertaining to the components, communication and educational environment are comprehended as the most significant elements of the social environment, influencing an individual’s behavior in an e-learning system utilization.
Interaction is viewed as the social interaction and collaboration of students with their teachers and other students in an e-learning system, while the learning atmosphere of the learners is referred to as the learning climate [34]. In relation to prior literature, Venkatesh et al. (2020) [35] argued that social interactions and learning climate influence the student’s learning outcome positively and enhance perceived e-learning satisfaction.

2. Methodology

This study used a quasi-experimental approach with the help of the non-equivalent groups’ technique [41], as randomization occurring at the intervention level helps eliminate the issue of directionality by manipulating the independent variable. Thereupon, quasi-experimental research exhibits stronger internal validity than non-experimental studies.

2.1. Participants

The study was conducted in 2022, where the third-year class from the College of Science and Arts, Qassim University, was selective purposively, consisting of 32 undergraduate students. Although it is not necessary to specify the validity and reliability of sample size in quasi-experimental studies with prearranged groups (i.e., classrooms) [42], it is crucial to identify the criteria for selection. The participants were chosen on account of easy accessibility using the strategy of purposive sampling, which allows researchers to obtain data in real-time circumstances, while conserving time and ressources [43]. Subsequently, all of the participants were randomly distributed into two groups prior to the beginning of the intervention. Each group comprised 16 students: one group was specified as experimental, whereas the other was controlled [shown in Figure 2].

2.2. Settings

ClassPoint application was integrated into the EFL instructional program for the experimental group, while traditional PowerPoint-integrated instructions were used for teaching the control group students. The intervention lasted about a month, and the instructor covered the introduction section and lexical theories in the course of Semantics and Pragmatics to both groups. The same teacher taught both groups twice a week, with each session lasting for one and a half hours. During the intervention, students’ participation was analyzed with regard to the dynamics of the classroom during the lectures. Figure 2 provides an overview of the instructional intervention framework employed by the instructor for both groups. During the ClassPoint-integrated instructional practices, the classes began with a short lecture of 30 min, followed by discussions, presentations, and inquiry-based learning activities. Subsequently, the instruction was followed by different interactive synchronous e-quizzes (such as interactive short, multiple-choice, and interactive short-answer questions) and feedback, as seen in Figure 3. In terms of traditional PowerPoint-integrated instructional practices, the classes began with a long lecture, followed by a question and answer session, and feedback.

2.3. Research Instrument

A self-reported questionnaire was used to investigate the e-learning satisfaction of EFL learners of both experimental and control groups as pre-tests at the start of the study. Subsequently, after four weeks, the e-learning satisfaction of students was investigated again as post-tests at the end of week four. The survey instrument utilized in this study was constructed by Wu et al. (2010) [37] on the basis of social cognitive theory to examine the student’s e-learning satisfaction within three basic domains, i.e., cognitive, technological, and social factors. Wu et al. (2010) [37] used a two-step process to validate the model fit. Initially, they evaluated all the measures using construct validity and reliability. Subsequently, all the proposed hypotheses of the model were tested by the partial least square PLS method. At the same time, the said questionnaire had also been validated and extensively used by other researchers and scholars e.g., [23,44,45]. In addition, before collecting data in the present study, the questionnaire was modified to meet the study’s context, and the word blended e-learning system (BELS) was rephrased to the e-learning system. The modified form of the questionnaire was composed of 21 items grouped into four dimensions, which were classified as: (1) learning satisfaction, 4 items; (2) cognitive factors, 6 items; (3) technological environment, 5 items; and (4) social environment, 6 items. The questionnaire was divided into two sections, one for the student’s demographic information and the other for their perceptions regarding each component of the research model. All of the questions in the second section of the questionnaire were constructed using a seven-point Likert scale ranging from “strongly disagree” to “strongly agree”.

2.4. Model Fitness Analysis

In order to test the items’ suitability for all the indicators of the research model, their consistency was evaluated by means of reliability, validity, and goodness-of-fit (GOF) determination [46]. Table 1 indicates that the chi-square (χ2/df) value for all the indicators of test anxiety was less than the threshold value 5. Furthermore, the values of RMSEA, TLI, CFI, and SRMR, also demonstrated significant results in accordance with the recommended range [47], which provides evidence of an acceptable model fit.

2.5. Reliability and Validity Analysis

To confirm the alignment of the data with the study model, the validity and reliability of all questionnaire indicators must be evaluated. In this regard, the reliability and validity tests were conducted before analyzing the data using PLS. The reliability test of each indicator was assessed by means of Cronbach’s alpha scale (see Table 2). All of the constructs yielded more than 80% of Cronbach’s alpha coefficient value, which signified strong conformity to proceed with the study [48]. The validity of all the indicators was investigated through convergent and discriminant validity tests. The convergent validity was further classified into two measures: composite reliability (CR) and average variance extracted (AVE), which also yielded significant values, as the attained values were greater than the threshold values, i.e., CR > 0.70 and AVE > 0.50, respectively [49]. In addition, the factor loadings of all the items yielded strong loadings, as all the acquired values exceeded the threshold value of 0.6 [50].
Similarly, a discriminant validity test was carried out to check the extent to which all the constructs of the study differ from one another. The discriminant validity, i.e., square root of AVE for all the constructs, found reliably greater than the threshold values, i.e., >0.7 [46], and inter-construct correlations, as illustrated in Table 3.
As the population size was less than 20, a normality test was performed to check data distribution for parametric testing. In this regard, the Shapiro-Wilk test (see Table 4) was used to calculate the normal distribution of data, which is thought to be the reliable test for determining normality [51]. The results showed that p values were larger than 0.05, indicating that all of the study’s variables were normally distributed in the population. As a result, the findings provide a solid framework for conducting parametric tests to investigate group comparisons.

3. Results

3.1. Research Question 1 (Hypothesis)

Before integrating ClassPoint activities into the experimental group, the students’ e-learning satisfaction in both groups, i.e., experimental and controlled, was investigated, which was named the pre-test. Subsequently, an individual sample t-test was used to compare the scores of the two groups. The results did not find any major difference between the e-learning satisfaction scores of the two groups, as the attained p-value (i.e., 0.13) was greater than the threshold value (see Table 5). In relation to the mean scores, both groups’ e-learning satisfaction scores were unsatisfactory [52] as mean scores were <3.5 and close to each other, i.e., the experimental group (M = 3.23) and the controlled group (M = 3.20).
For examining the effect of ClassPoint activities on the EFL students’ e-learning satisfaction, the post-test mean scores of the experimental (ClassPoint learning mode) and controlled (non-ClassPoint learning mode) groups were compared with each other (see Table 6). The results revealed a substantial difference between test scores of experimental and controlled group test scores (i.e., p = 0.00) at alpha level 0.01. In relation to the mean scores, the mean value of the experimental group was considerably higher than the controlled group (shown in Figure 4), which supports the hypothesis of the study and affirms that the ClassPoint integrated instructional practices significantly enhance the EFL students’ e-learning satisfaction in comparison to the non-ClassPoint integrated lessons.

3.2. Research Question 2

To investigate the most influential components of e-learning satisfaction, the comparison of the mean values for all the sub-components was conducted with respect to their projected modes of learning environments (displayed in Figure 5). The results show that students engaged in the ClassPoint integrated learning mode yielded considerably higher mean scores in all the sub-components of e-learning satisfaction than the non-ClassPoint engaged EFL learners. In relation to the ClassPoint-integrated setting, the students showed higher satisfaction on account of the technological environment. The cognitive features, on the other hand, made them the least satisfied. Regarding the non-ClassPoint-integrated setting, the students showed higher satisfaction on account of the cognitive features; in contrast, the social environment predictor made them the least satisfied.

4. Discussion

Echoing the central role of technology in education, the extensive utilization of ICT in teaching-learning practices has attracted research interest for many years. Likewise, the use of innovative ICT applications has been considered an essential requirement of computer-assisted language learning (CALL) settings, and several forms of interactive platforms have been applied to the EFL learning environments to enhance the student’s engagement, academic success, and learning satisfaction. Taking this into consideration, the present study provides a significant amount of information that is helpful in comprehending the role of the ClassPoint tool in strengthening the EFL student’s e-learning satisfaction through the lens of the social cognitive theory of learning satisfaction.
Firstly, unsatisfactory e-learning satisfaction was found among all the respondents of the study, before the application of the ClassPoint tool in educational practices. Quispe and Alecchi (2021) [53] also addressed a similar constraint among higher education students and identified that the majority of students showed dissatisfaction with respect to their teacher’s instructional practices on account of inadequate management of synchronous classes, lack of feedback, and their limited interaction with their teachers. In contrast, the study by Supriyatno and Kurniawan (2020) [54] identified a significant learning satisfaction among higher education students during the COVID-19 emergency online teaching and learning phase in Indonesia.
In relation to interventions, the use of interactive technology in teaching-learning practices has been suggested by several scholars to strengthen the EFL student’s learning satisfaction. The current study also investigated the role of an interactive digital application, i.e., the ClassPoint tool, in strengthening the EFL student’s learning satisfaction. The findings indicated that the learning satisfaction of ClassPoint-engaged EFL learners was considerably higher than the non-ClassPoint engaged EFL learners, which affirms the usefulness of the ClassPoint tool in strengthening the EFL student’s learning satisfaction. There could be several reasons for a high level of e-learning satisfaction among ClassPoint-engaged EFL learners. In this direction, the interactive and motivating aspects of the ClassPoint (an alternative form of the CRS technique) application environment [9] can be considered as one reason for strengthening the student’s learning satisfaction, which helps students stay engaged during e-learning practices. Lee et al. (2019) [55] also supported the use of instant response technology and specified the effectiveness of Kahoot as a game-based type of student response system in increasing active participation, enhancing academic success, and the e-learning satisfaction of students. Likewise, the interesting and entertaining aspects of the CRS technique could be the other reason for enhancing students’ learning satisfaction among ClassPoint engaged EFL learners. On the one hand, interactive game-based assessments help reduce students’ boredom, while on the other hand they enhance students’ learning satisfaction [32]. Wang and Tahir (2020) [17] also advocated the use of interactive game-based technological applications, i.e., Kahoot, in strengthening students’ learning satisfaction and indicated that a game-based student response system also helps teachers identify the student’s learning difficulties and thus plan strategies to overcome them. Therefore, teachers should create interesting and attractive quizzes/assessments/lessons to enhance students’ involvement and attention, which would help pave the path to attaining educational objectives.
Regarding the determinants, all the sub-components of e-learning satisfaction were significantly increased among the ClassPoint engaged learners, in comparison with the non-ClassPoint engaged learners. In relation to the most influential predictors among ClassPoint engaged learners, the highest learning satisfaction was found on account of the technological environment. This finding demonstrates coherence with the findings of previous research, which suggest that the availability of advanced technical applications, gadgets, and internet accessibility, plays a key role in attaining the academic achievement and learning satisfaction of students [56]. Synchronically, the study by Surahman and Sulthoni (2020) [57] identified that undergraduate students showed less enthusiasm and satisfaction with online learning as compared to traditional learning, on account of limited internet access during the COVID-19 period’s emergency remote instruction.
Regarding the non-ClassPoint learning mode, cognitive characteristics were revealed to be the greatest predictive component of e-learning satisfaction among students. This finding highlights the importance of self-efficacy in attaining success in students’ online learning satisfaction and, ultimately, outcomes. A similar finding has been reported by Aldhahi et al. (2022) [58], who identified a substantial relationship between students’ e-learning satisfaction and online learning self-efficacy following an abrupt change to remote learning brought on by the COVID-19 phase. Likewise, the study by Rabin et al. (2020) [59] identified that English as a second language (ESL) learners with higher self-efficacy showed higher satisfaction with online learning, as compared to the students with a low level of self-efficacy. Therefore, it is suggested that EFL teachers integrate the ClassPoint application in the CALL setting to boost students’ self-efficacy and strengthen their e-learning satisfaction.
At the same time, it was discovered that the social environment was the least predictive factor of e-learning satisfaction among learners who were not engaged in the ClassPoint-integrated educational setting. This finding signifies the importance of social interaction in teaching-learning practices, which not only enhances the students’ involvement in the learning practice but also helps acquire a productive learning climate and strengthens the students’ online learning satisfaction. This result demonstrates congruence with those of Akram et al. (2021) [23], who identified that students’ e-learning experiences were most strongly influenced by their social settings, and they showed significant satisfaction on account of better social connection with their teachers and peers in a synchronous learning mode. Similarly, another representative study by Surahman and Sulthoni (2020) [57] also identified that undergraduate students showed online learning dissatisfaction on account of inadequate lecturers’ guidance and interaction with their teachers and peers. It is, therefore, suggested that EFL teachers employ the ClassPoint application in teaching-learning practices to increase their students’ involvement and enrich their learning experiences by integrating interactive activities. Besides, governing bodies should gain awareness regarding newly emerged interactive digital applications and make teachers familiar with them so that they may implement them successfully in their instructional practices. Furthermore, university management should monitor their students’ learning satisfaction and lecturers’ teaching practices constantly, to ensure the effectiveness of e-learning programs and organize remedial plans accordingly.

5. Conclusions

The results of the current research offer empirical evidence and confirms that integrating ClassPoint tool activities into the EFL learning environment significantly enhances students’ e-learning satisfaction when compared to the non-ClassPoint-integrated learning mode. This relationship contributes to the improvement of the students’ e-learning satisfaction on the one hand, while on the other hand it helps enhance the evaluation of the students’ EFL performance in online educational systems. In this regard, it is essential to induce the ClassPoint tool integration at all educational levels in all modes of language learning to enrich the learners’ learning experiences by developing an interest in the learning process. Furthermore, the preliminary findings of this study provide information to the teachers and students to identify and manage certain predictors of e-learning satisfaction in an EFL classroom. In relation to the implementation, governing bodies should organize workshops or seminars to cultivate awareness among instructors and students regarding the effective use of the ClassPoint tool so that they may make the best use of this application.

Author Contributions

A.H.A. conceptualized and implemented the intervention of ClassPoint application. H.A. designed the study, analyzed the data and wrote the whole manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the Deanship of Scientific Research, Qassim University.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data that support the findings of this study would be available upon a reasonable request.

Acknowledgments

The authors would like to thank the Scientific Research, Qassim University, for their great support and efforts throughout the project accomplishment. We are grateful to the third-year students from the Department of English Language and Translation, College of Science and Arts at Al Asyah, for their cooperation.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Quispe-Prieto, S.; Cavalcanti-Bandos, M.F.; Caipa-Ramos, M.; Paucar-Caceres, A.; Rojas-Jiménez, H.H. A systemic framework to evaluate student satisfaction in Latin American universities under the COVID-19 pandemic. Systems 2021, 9, 15. [Google Scholar] [CrossRef]
  2. Akram, H.; Aslam, S.; Saleem, A.; Parveen, K. The challenges of online teaching in COVID-19 pandemic: A case study of public universities in Karachi, Pakistan. J. Inf. Technol. Educ. Res. 2021, 20, 263–282. [Google Scholar] [CrossRef]
  3. Al-Adwan, A.S.; Nofal, M.; Akram, H.; Albelbisi, N.A.; Al-Okaily, M. Towards a Sustainable Adoption of E-Learning Systems: The Role of Self-Directed Learning. J. Inf. Technol. Educ. Res. 2022, 21, 245–267. [Google Scholar]
  4. Akram, H.; Yingxiu, Y.; Al-Adwan, A.S.; Alkhalifah, A. Technology Integration in Higher Education During COVID-19: An Assessment of Online Teaching Competencies Through Technological Pedagogical Content Knowledge Model. Front. Psychol. 2021, 12, 736522. [Google Scholar] [CrossRef] [PubMed]
  5. Akram, H.; Abdelrady, A.H.; Al-Adwan, A.S.; Ramzan, M. Teachers’ Perceptions of Technology Integration in Teaching-Learning Practices: A Systematic Review. Front. Psychol. 2022, 13, 920317. [Google Scholar] [CrossRef]
  6. Zou, C.; Li, P.; Jin, L. Integrating smartphones in EFL classrooms: Students’ satisfaction and perceived learning performance. Educ. Inf. Technol. 2022, 27, 1–22. [Google Scholar] [CrossRef]
  7. Tarng, W.; Pan, I.-C.; Ou, K.-L. Effectiveness of Virtual Reality on Attention Training for Elementary School Students. Systems 2022, 10, 104. [Google Scholar] [CrossRef]
  8. Balta, N.; Tzafilkou, K. Using Socrative software for instant formative feedback in physics courses. Educ. Inf. Technol. 2019, 24, 307–323. [Google Scholar] [CrossRef]
  9. Chang, C. Effects of an Instant Response System integrated learning activity on EFL students’ learning achievement and perceptions. System 2021, 103, 102637. [Google Scholar] [CrossRef]
  10. Luo, Y.; Du, H. Learning with desktop virtual reality: Changes and interrelationship of self-efficacy, goal orientation, technology acceptance and learning behavior. Smart Learn. Environ. 2022, 9, 22. [Google Scholar] [CrossRef]
  11. Tao, J.; Gao, X.A. Teaching and learning languages online: Challenges and responses. System 2022, 107, 102819. [Google Scholar] [CrossRef]
  12. Saudi Arabia Ministry of Education (MOE). Ministry of Education. 2021. Available online: www.moe.gov.sa (accessed on 12 May 2022).
  13. Al-Asmari, A.M.; Khan, M.S.R. E-learning in saudi arabia: Past, present and future. Near Middle East. J. Res. Educ. 2014, 2014, 2. [Google Scholar] [CrossRef]
  14. Ali, J.K.M.; Bin-Hady, W.R.A. A study of EFL students’ attitudes, motivation and anxiety towards WhatsApp as a language learning tool. Arab. World Engl. J. 2019, 5, 289–298. [Google Scholar]
  15. Alharbi, A.S.; Meccawy, Z. Introducing Socrative as a tool for formative assessment in Saudi EFL classrooms. Arab World Engl. J. 2020, 11, 372–384. [Google Scholar] [CrossRef]
  16. Abdulaal, M.A.A. The Effectiveness of Gallery Run Technique on Saudi EFL Upper-Intermediate Students’ Speaking Skill: Learners’ Attitudes in Focus. Arab World Engl. J. 2021, 12, 536–550. [Google Scholar] [CrossRef]
  17. Wang, A.I.; Tahir, R. The effect of using Kahoot! for learning—A literature review. Comput. Educ. 2020, 149, 103818. [Google Scholar] [CrossRef]
  18. Mahyoob, M. Challenges of e-Learning during the COVID-19 Pandemic Experienced by EFL Learners. Arab World Engl. J. 2020, 11, 351–362. [Google Scholar] [CrossRef]
  19. Kuo, Y.-C.; Walker, A.E.; Schroder, K.E.E.; Belland, B.R. Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. Internet High. Educ. 2014, 20, 35–50. [Google Scholar] [CrossRef]
  20. Serenko, A. Student satisfaction with Canadian music programmes: The application of the American Customer Satisfaction Model in higher education. Assess. Eval. High. Educ. 2011, 36, 281–299. [Google Scholar] [CrossRef]
  21. Murillo-Zamorano, L.R.; Sánchez, J.Á.L.; Godoy-Caballero, A.L. How the flipped classroom affects knowledge, skills, and engagement in higher education: Effects on students’ satisfaction. Comput. Educ. 2019, 141, 103608. [Google Scholar] [CrossRef]
  22. Prifti, R. Self-efficacy and student satisfaction in the context of blended learning courses. OpenLearn. J. Open Distance E-Learn. 2022, 37, 111–125. [Google Scholar] [CrossRef]
  23. Akram, H.; Yingxiu, Y.; Aslam, S.; Umar, M. Analysis of Synchronous and Asynchronous Approaches in Students’ Online Learning Satisfaction During COVID-19 Pandemic. In Proceedings of the 2021 IEEE International Conference on Educational Technology (ICET), Beijing, China, 18–20 June 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 203–207. [Google Scholar]
  24. Nortvig, A.-M.; Petersen, A.K.; Balle, S.H. A Literature Review of the Factors Influencing E-Learning and Blended Learning in Relation to Learning Outcome, Student Satisfaction and Engagement. Electron. J. e-Learn. 2018, 16, 46–55. [Google Scholar]
  25. Guillén-Gámez, F.D.; Mayorga-Fernández, M.J.; Álvarez-García, F.J. A study on the actual use of digital competence in the practicum of education degree. Technol. Knowl. Learn. 2020, 25, 667–684. [Google Scholar] [CrossRef]
  26. Zou, D. Gamified flipped EFL classroom for primary education: Student and teacher perceptions. J. Comput. Educ. 2020, 7, 213–228. [Google Scholar] [CrossRef]
  27. Alwehebi, K.A. The effect of online EFL classes during the COVID 19 pandemic: Evaluating the performance of university students and teachers in Saudi Arabia. Rev. Int. Geogr. Educ. Online 2021, 11, 990–1006. [Google Scholar]
  28. Yu, L.-T. The Effect of Videoconferencing on Second-Language Learning: A Meta-Analysis. Behav. Sci. 2022, 12, 169. [Google Scholar] [CrossRef]
  29. Almusharraf, A.; Almusharraf, N. Socio-interactive practices and personality within an EFL online learning environments. Educ. Inf. Technol. 2021, 26, 3947–3966. [Google Scholar] [CrossRef]
  30. Hunsu, N.J.; Adesope, O.; Bayly, D.J. A meta-analysis of the effects of audience response systems (clicker-based technologies) on cognition and affect. Comput. Educ. 2016, 94, 102–119. [Google Scholar] [CrossRef]
  31. Bong, E.Y.; Chatterjee, C. The Use of a ClassPoint Tool for Student Engagement During Online Lesson. In Proceedings of the Asian Conference on Education 2021, Tokyo, Japan, 25–27 November 2021. [Google Scholar]
  32. Nasu, V.H.; Afonso, L.E. The effect of student response system (SRS) on academic performance and satisfaction: A quasi-experiment with accounting science undergraduate students. In Proceedings of the XII ANPCONT, Paraíba, Brasil, 9–12 June 2018. [Google Scholar]
  33. Bandura, A. Social Foundations of Thought and Action; Prentice Hall: Englewood Cliffs, NJ, USA, 1986. [Google Scholar]
  34. Venkatesh, V.; Bala, H. Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. 2008, 39, 273–315. [Google Scholar] [CrossRef]
  35. Venkatesh, S.; Rao, Y.K.; Nagaraja, H.; Woolley, T.; Alele, F.O.; Malau-Aduli, B.S. Factors influencing medical students’ experiences and satisfaction with blended integrated E-learning. Med. Princ. Pract. 2020, 29, 396–402. [Google Scholar] [CrossRef]
  36. Wolverton, C.C.; Hollier, B.N.G.; Lanier, P.A. The impact of computer self efficacy on student engagement and group satisfaction in online business courses. Electron. J. e-Learn. 2020, 18, 175–188. [Google Scholar] [CrossRef]
  37. Wu, J.-H.; Tennyson, R.D.; Hsia, T.-L. A study of student satisfaction in a blended e-learning system environment. Comput. Educ. 2010, 55, 155–164. [Google Scholar] [CrossRef]
  38. Pituch, K.A.; Lee, Y.-K. The influence of system characteristics on e-learning use. Comput. Educ. 2006, 47, 222–244. [Google Scholar] [CrossRef]
  39. Deng, L.; Tavares, N.J. From Moodle to Facebook: Exploring students’ motivation and experiences in online communities. Comput. Educ. 2013, 68, 167–176. [Google Scholar] [CrossRef]
  40. Al-Fraihat, D.; Joy, M.; Masa’Deh, R.; Sinclair, J. Evaluating E-learning systems success: An empirical study. Comput. Hum. Behav. 2020, 102, 67–86. [Google Scholar] [CrossRef]
  41. Reichardt, C.S. Quasi-Experimental Design. In The SAGE Handbook of Quantitative Methods in Psychology; Millsap, R.E., MaydeuOlivares, A., Eds.; SAGE: Thousand Oaks, CA, USA, 2009; pp. 46–71. [Google Scholar]
  42. Fernández-Garcia, P.; Vallejo-Seco, G.; Livacic-Rojas, P.E.; Tuero-Herrero, E. Validez Estructurada para una investigación cuasi-experimental de calidad. Se cumplen 50 años de la presentación en sociedad de los diseños cuasi-experimentales. Anal. Psicol. 2014, 30, 756–772. [Google Scholar]
  43. Etikan, I.; Musa, S.A.; Alkassim, R.S. Comparison of convenience sampling and purposive sampling. Am. J. Theor. Appl. Stat. 2016, 5, 1–4. [Google Scholar]
  44. Wu, J.; Liu, W. An empirical investigation of the critical factors affecting students’ satisfaction in EFL blended learning. J. Lang. Teach. Res. 2013, 4, 176–185. [Google Scholar] [CrossRef]
  45. Mohammadi, H. Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Comput. Hum. Behav. 2015, 45, 359–374. [Google Scholar] [CrossRef]
  46. Hair, J.F., Jr.; Sarstedt, M.; Matthews, L.M.; Ringle, C.M. Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part I—Method. Eur. Bus. Rev. 2016, 28, 63–76. [Google Scholar] [CrossRef]
  47. Hair, J.F., Jr.; Howard, M.C.; Nitzl, C. Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. J. Bus. Res. 2020, 109, 101–110. [Google Scholar] [CrossRef]
  48. Byrne, B.M. Structural Equation Modelling with AMOS: Basic Concepts, Applications, and Programming, 2nd ed.; Taylor & Francis Group: New York, NY, USA, 2010; Volume 396, p. 7384. [Google Scholar]
  49. Arpaci, I.; Yardimci Cetin, Y.; Turetken, O. A cross-cultural analysis of smartphone adoption by Canadian and Turkish organizations. J. Glob. Inf. Technol. Manag. 2015, 18, 214–238. [Google Scholar] [CrossRef]
  50. Yong, A.G.; Pearce, S. A beginner’s guide to factor analysis: Focusing on exploratory factor analysis. Tutor. Quant. Methods Psychol. 2013, 9, 79–94. [Google Scholar] [CrossRef]
  51. González-Estrada, E.; Cosmes, W. Shapiro–Wilk test for skew normal distributions based on data transformations. J. Stat. Comput. Simul. 2019, 89, 3258–3272. [Google Scholar] [CrossRef]
  52. Pimentel, J.L. Some biases in Likert scaling usage and its correction. Int. J. Sci. Basic Appl. Res. 2019, 45, 183–191. [Google Scholar]
  53. Quispe, M.D.C.A.; Alecchi, B.A. Business School Student Satisfaction with Emergency Remote Teaching. J. Educ. e-Learn. Res. 2021, 8, 375–384. [Google Scholar] [CrossRef]
  54. Supriyatno, T.; Kurniawan, F. A New Pedagogy and Online Learning System on Pandemic COVID 19 Era at Islamic Higher Education. In Proceedings of the 2020 6th International Conference on Education and Technology (ICET), Malang, Indonesia, 17 October 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 7–10. [Google Scholar]
  55. Lee, C.-C.; Hao, Y.; Lee, K.S.; Sim, S.C.; Huang, C.-C. Investigation of the effects of an online instant response system on students in a middle school of a rural area. Comput. Hum. Behav. 2019, 95, 217–223. [Google Scholar] [CrossRef]
  56. Firat, M.; Bozkurt, A. Variables affecting online learning readiness in an open and distance learning university. Educ. Media Int. 2020, 57, 112–127. [Google Scholar] [CrossRef]
  57. Surahman, E.; Sulthoni. Student Satisfaction toward Quality of Online Learning in Indonesian Higher Education During the COVID-19 Pandemic. In Proceedings of the 2020 6th International Conference on Education and Technology (ICET), Malang, Indonesia, 17 October 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 120–125. [Google Scholar]
  58. Aldhahi, M.I.; Alqahtani, A.S.; Baattaiah, B.A.; Al-Mohammed, H.I. Exploring the relationship between students’ learning satisfaction and self-efficacy during the emergency transition to remote learning amid the coronavirus pandemic: A cross-sectional study. Educ. Inf. Technol. 2022, 27, 1323–1340. [Google Scholar] [CrossRef]
  59. Rabin, E.; Henderikx, M.; Yoram, M.K.; Kalz, M. What are the barriers to learners’ satisfaction in MOOCs and what predicts them? The role of age, intention, self-regulation, self-efficacy and motivation. Australas. J. Educ. Technol. 2020, 36, 119–131. [Google Scholar] [CrossRef]
Systems 10 00154 g001 550
Figure 1. Conceptual framework based on social cognitive theory.
Figure 1. Conceptual framework based on social cognitive theory.
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Figure 2. Research design.
Figure 2. Research design.
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Figure 3. Screenshots of ClassPoint integrated activities.
Figure 3. Screenshots of ClassPoint integrated activities.
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Figure 4. Comparison of mean scores of experimental and controlled groups.
Figure 4. Comparison of mean scores of experimental and controlled groups.
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Figure 5. E-learning satisfaction predictors in both modes of learning.
Figure 5. E-learning satisfaction predictors in both modes of learning.
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Table
Table 1. Validation of the model.
Table 1. Validation of the model.
χ2dfχ2/dfRMSEACFITLISRMRp-Value
9.22.83.20.0720.900.910.030.000
Table
Table 2. Reliability and validity evaluation.
Table 2. Reliability and validity evaluation.
VariablesItemsLoadingsAlpha ValueCRAVE
Learning Satisfaction (LS)LS10.790.800.920.72
LS20.80
LS30.81
LS40.81
Cognitive Factors (CF)CF10.790.830.870.70
CF20.78
CF30.82
CF40.81
CF50.80
CF60.81
Technological Environment TETE10.800.820.880.73
TE20.81
TE30.82
TE40.81
TE50.81
Social Environment (SE)SE10.790.880.900.74
SE20.78
SE30.79
SE40.80
SE50.81
SE60.82
Table
Table 3. Inter-construct correlations and discriminant validity.
Table 3. Inter-construct correlations and discriminant validity.
LSCFTESE
Learning Satisfaction (LS)0.84
Cognitive Factors (CF)0.47 **0.83
Technological Environment (TE)0.42 **0.51 **0.85
Social Environment (SE)0.40 **0.52 **0.49 **0.86
The discriminant validity values are represented by the bold inclined values. Significant at: ** p < 0.01.
Table
Table 4. Normality statistics.
Table 4. Normality statistics.
S-WdfSignificance
Experimental group0.872290.06
Controlled group0.882250.07
Table
Table 5. Pre-test comparison of EFL students’ e-learning satisfaction.
Table 5. Pre-test comparison of EFL students’ e-learning satisfaction.
NMSDdftp
Experimental group163.232.1341−0.300.13
Controlled group163.202.56
Table
Table 6. Post-test comparison of EFL students’ e-learning satisfaction.
Table 6. Post-test comparison of EFL students’ e-learning satisfaction.
GroupsNMSDdftp
Experimental group165.102.34322.570.00 **
Controlled group163.521.67
Significant at: ** p < 0.01.
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Abdelrady, A.H.; Akram, H. An Empirical Study of ClassPoint Tool Application in Enhancing EFL Students’ Online Learning Satisfaction. Systems 2022, 10, 154. https://doi.org/10.3390/systems10050154

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Abdelrady AH, Akram H. An Empirical Study of ClassPoint Tool Application in Enhancing EFL Students’ Online Learning Satisfaction. Systems. 2022; 10(5):154. https://doi.org/10.3390/systems10050154

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Abdelrady, Abbas Hussein, and Huma Akram. 2022. "An Empirical Study of ClassPoint Tool Application in Enhancing EFL Students’ Online Learning Satisfaction" Systems 10, no. 5: 154. https://doi.org/10.3390/systems10050154

APA Style

Abdelrady, A. H., & Akram, H. (2022). An Empirical Study of ClassPoint Tool Application in Enhancing EFL Students’ Online Learning Satisfaction. Systems, 10(5), 154. https://doi.org/10.3390/systems10050154

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