Students’ perceptions towards the uptake of educational technologies in Christian Religious Education

Abstract

The techniques and methods of teaching in Kenya have changed rapidly from traditional methods to contemporary, 21^st century, technology-based approaches (Ashrafi et al., 2020). This development led to the introduction of several frameworks to support the application of numerous innovations in learning. However, the uptake of technology-supported teaching and learning, particularly in Christian Religious Education (CRE), remains low. Recent research indicates that the application and utilization of these technologies in CRE are minimal because of the perceptions of key stakeholders, including teachers, students, and school administrators. Educational technologies not only enhance the quality of learning but also contribute to deepening of religious knowledge and insights, fostering the development of moral values and beliefs. Therefore, this study investigated the relationship between students’ perceptions and the uptake of educational technologies in CRE in Embu County, Kenya. This study sampled 300 students from 30 public secondary schools using a descriptive cross-sectional survey design and multistage random cluster sampling methods. Data were collected using a semi-structured questionnaire and analyzed using descriptive and inferential statistics. The results showed a significant weak positive relationship between students’ perceptions and their uptake of educational technologies in CRE (r = 0.002, p = 0.01 and R² = 0.032). The findings of this study imply that initiatives aimed at increasing the uptake of educational technology should be specific, lucid, and tailored to the perceptions, ideas, opinions, experiences, and diverse needs of students.

Keywords:

• Technology uptake
• students’ perceptions
• educational technologies
• christian religious education
• Technology Acceptance Model (TAM)

REVIEWING EDITOR:

• Sirui Wang, UCLA: University of California Los Angeles, Los Angels, US

SUBJECTS:

• Primary/Elementary Education
• Secondary Education
• Teaching & Learning

1. Introduction

In the current world, all sectors of life are powered and driven by technology. Without adopting ever-changing technologies, modern human would encounter many difficulties in leading conventional lives. Today, most local, and international money transfers can be concluded through technologies that integrate financial and telecommunication advancements in a faster, safer, and more convenient way such as the MPESA(‘M’ for money and ‘Pesa’ for money in Swahili) platform in Kenya (Mutevu, 2015). In the aviation sector, technological advancements have enabled pilots to utilize automatic flight modes, allowing planes to safely navigate through skies to their intended destinations (Alam, 2016). Similarly, in the field of medicine, technologies such as Magnetic Resonance Imaging (MRI) have significantly improved diagnostics and treatment procedures (Williamson & Muckle, 2018). These examples represent only a fraction of the impact of technology in the contemporary world. The implication for the education sector is that in an increasingly technology-driven world, the use of educational technologies in the instructional process cannot be ignored (Friesen, 2020; Ngussa, 2015; Okoye et al., 2021). According to Murundu et al. (2017) and Situma (2016), the integration of technology in education, particularly CRE, yields numerous positive outcomes. For instance, it enhances learning and retention compared to conventional methods by making the content more interesting and relevant. Additionally, student-centered technology-based pedagogies are introduced, preparing learners for a world that is increasingly reliant on technology in almost all of spheres of life.

Educational technologies are an inevitable and indispensable part of the modern world, specifically the instructional process, and the significance and role of technology in education has been made vivid at the height of the COVID 19 pandemic (Bariham et al., 2020). With the global lockdown, restrictions on movement and social contact designed to contain the pandemic meant that traditional modes of teaching and learning were impossible to implement. The world needed to rely on technology to circumvent this challenge for education. Globally, recent times have seen great and rapid shifts in the techniques and methods of teaching that rely on traditional methods, such as teacher-centered methods of chalk and board, to those that emphasize and enhance student-centered technology-driven pedagogies (Hassan & Aziz, 2019; Kimosop, 2019; Marcus, 2019; Peace, 2020; Saoke et al., 2022). Research conducted by Boiliu et al. (2022) on effectiveness of CRE learning during Covid-19 pandemic in Indonesia underscored the profound influence of the pandemic on the educational landscape, particularly in the realm of CRE. The traditional face-to-face mode of instruction transitioned to an online format, leading to alterations in learning designs, materials, the utilization of learning media, and ultimately impacting learning outcomes.

Educational technologies are instructional innovations that enhance learning and greatly impact the way content is taught by leveraging students’ unique experiences and identities (Drossel et al., 2017; Situma, 2016). They include, but are not limited to, computers, internet, LCD projectors, laptops, virtual learning environments such as Google Meet and Zoom, and social learning platforms such as Facebook and WhatsApp among others (Jebungei, 2017). According to Onesmus (2020), Kenyan public secondary schools exhibit a concerning lack of widespread adoption of educational technology, largely attributed to their socio-economic status. He notes that the commonly available technologies in schools typically encompass computers, laptops, LCD projectors, tablets, digital cameras, smartphones, printers, scanners, social learning platforms such as WhatsApp groups, among others. Although some scholars such as Serdyukov (2017) argue that there is nothing like a ‘perfect method of teaching’, the rapidly expanding transition towards a more technology driven education is supported by an equally rapidly growing body of academic literature that links increased application of educational technologies to increased pedagogical effectiveness. For example, Kimosop (2019), Marcus (2019), Okoye et al. (2021), and Saoke et al. (2022) showed that technology usage is a salient factor in enhancing students’ content retention. They argued that there is an association between the methods and approaches applied in teaching and the nature of the learning outcomes achieved. However, despite the global upsurge in educational technologies and the corresponding accumulation of empirical evidence showing their utility, the uptake of these technologies in Kenya remains elusive, particularly in the context of Christian Religious Education (CRE) (Hassan & Aziz, 2019).

The absence or minimal use of innovative technology-based learning methods has resulted in the negative outcome that the teaching of CRE has been closely associated with classroom preaching (Situma, 2016). The net effect is that, as a subject, the teaching of CRE fails to achieve the desired learning outcomes as a crucial subject for inculcating societal moral values and beliefs in learners (Groenewegen, 1995; Kowino et al., 2011; Murundu et al., 2017). Similarly, Díaz (2021) on the effectiveness of digital technology for evangelization, highlighted that technology in CRE has the promise to enhance and broaden students’ comprehension of doctrinal teachings in a transformative manner. However, despite the promise, an unreflective utilization of these tools may impede evangelization efforts. Thus, intentional and reflective use of technology is imperative for CRE to effectively advance its evangelistic goals. Among the factors that have been suggested as explanatory variables for this deficiency is how teachers and students perceive the use of educational technologies. A study on CRE teachers’ attitudes towards instructional innovations in Meru County Kenya by Saoke et al. (2022) reveals that the utilization of educational technologies has not received adequate attention in Kenyan public secondary schools for the CRE subject. The study emphasizes that unfavorable policies pose significant obstacles to the transformation and modernization of CRE, compounded by challenges such as religious plurality, secularity, and intense competition from other subjects. Additionally, Murungi et al. (2017) highlights that, despite global recognition of the importance of educational technologies in enhancing education, the effective integration of these technologies into public secondary schools in Embu County, Kenya, is yet to be realized.

Mensah and Akorful (2018) suggest that the curriculum proposes various methods for teaching CRE in both rural and urban areas, including the lecture method, role play, question and answer sessions, brainstorming, discussions, utilization of resource persons, story-telling, and textbook reading. However, despite this array of methods, the study questions whether the students’ interests are taken into consideration when selecting these techniques. Ndwiga et al. (2020) contend that these methods are predominantly teacher-centered, exhibiting certain drawbacks such as insufficient stimulation of learners’ innovative capacities, limited encouragement of intellectual thinking, reliance on memorization, cramming of facts, poor knowledge retention, and high dependency among learners, thereby missing the advantages of technology-based intellectual discoveries. In light of these shortcomings, Situma (2016) advises that, given the contemporary development of educational technologies, CRE should no longer heavily rely on lectures to deliver content. He suggests that the approach should shift towards student-centered pedagogies rather than teacher-centered ones. Similarly, Widjaja et al. (2022) and Widjaja and Boiliu (2021) argue that, to fulfill its mission, CRE requires technologically literate instructors, and tasks should be aligned with students’ demands and competencies. This entails incorporating their ability to explore innate potentials necessary to meet their individual needs

Theoretically, the Technology Acceptance Model (TAM) has gained wide application among scholars as a framework for explaining the adoption and usage of educational technologies (Teo & Noyes, 2011; Venkatesh et al., 2000). Based on this framework, this study assessed the association between students’ perceptions and the uptake of educational technologies in the learning of CRE in Embu County, Kenya. Consequently, this study intended to answer the following research questions: (i) What are CRE students’ perceptions towards educational technologies? (ii)What is the uptake level of educational technologies among CRE students? and (iii)What is the relationship between CRE students’ perceptions and uptake of educational technologies in public secondary schools in Embu County, Kenya?

1.1. Review of extant literature

Numerous scholarly studies have focused on the relationship between perceptions and usage of educational technologies. Some scholars, such as Makgato (2012), Mtebe and Christina (2017), Salem et al. (2019), and Shpeizer (2019), have investigated the challenges associated with integrating educational technologies in the instruction process. These challenges include lack of access to educational technologies, inadequate technological skills, resistance to change, and school policies regarding technology usage, among others. Other researchers such as Bali and Liu (2018), Faizi et al. (2015), and Teo et al. (2018) have not only examined the perceptions of students on educational technologies but also on the reasons for their usage in learning. They explained that students use these technologies extensively because of their capacity to enhance learning across different subjects, fosters innovation, enrich their understanding and increase their motivation. Additionally, Lai and Bower (2019), Lemay et al. (2019), Momani and Jamous (2017), and Mwikali (2014) explored the conditions, contexts, and situations for evaluating technology usage. Their research highlighted factors such as user behavior, learning outcomes, institutional environment, and the interplay between technology and pedagogy, which have attracted research attention.

In a survey involving 302 participants from 13 countries, Tenakwah et al. (2022) found a notable connection between perceived efficacy, ease of employing educational technologies, and individuals’ motivation to adopt available educational technologies. Based on a textual analysis of comments in an online survey to explore perception and technology usage, Cui and Wu (2021) established that people portray different perceptions regarding technology usage. They concluded that people tend to be optimistic, excited, or fearful about new technologies.

In their study, Ani et al. (2007) and Vaportzis et al. (2017), from their survey and thematic analysis respectively found that there is a relationship between age and the uptake of educational technologies. They argued that most learners of all age brackets are eager and willing to adopt technology. However, their findings showed that, compared to younger and new millennial learners, older learners are slower to adopt and use educational technologies due to differences in perceived usefulness. Gardner (2017) and Sawyer (2017) investigated the consequences and factors affecting perceptions and usage of technology using the Technology Acceptance Model (TAM) and Diffusion of Innovations theory, respectively. They established that, given a range of available educational technologies, most learners were unwilling to embrace technology despite being competent and conversant, as perceptions form a significant factor in determining usage. They argue that different perceptions account for disparities in the adoption of technology between teachers and students.

Some studies have attempted to estimate the predictors of perceptions that influence the eventual use of technology. For example, Barnett et al. (2015) established that, personality differences between individuals account for intrinsic factors (e.g. personal beliefs and intentions regarding technology) and extrinsic factors (e.g. access, time and support) that determine the perceptions and acceptance of educational technologies. Cheng et al. (2013) and Park et al. (2022) contend that the perception dimension of technology has a significant impact on technology acceptance and usage.

Based on his theoretical construct, ‘Diffusion of Innovations’ Rogers (1995) appraised that technology-driven pedagogy can optimize its effectiveness only if it is anchored in an educational system that fully incorporates the perceptions and opinions of teachers, students and administrators in the design and implementation of technology-driven learning. Research on technology adoption has shown that people are naturally resistant to change unless they develop attitudes towards using technology that depend on their perceptions (Roberts et al., 2021). In the social sciences, Karkouti (2021), Muathe et al. (2019), and Park et al.(2022) highlighted that the main barrier towards the integration of educational technologies into the instructional process is the poor user’s perceptions and attitudes, which should not be overlooked. Nevertheless, Fatimah (2017), Hartman et al. (2019), and Peace (2020) showed that technology acceptance is an important subject in the field of educational research; however, perceptions towards usage remain under-explored. In addition, there is limited information on how students’ perceptions influence their uptake of educational technologies in CRE. Therefore, the research intended to examine the objectives regarding to what are the students’ perceptions towards educational technologies and the relationship between perceptions and the uptake of educational technologies in CRE, in Embu County, Kenya.

1.2. Theoretical framework

The Technology Acceptance Model (TAM) proposed by Davis (Davis, 1986) informed the study. This theory attempts to understand, explain, and predict human behavior related to the usage of any technology (Davis, 1989). According to this model, individual behavior towards technology is a function of perceptions. Perceptions (external variables) are regarded to be a composite independent variable comprising of perceived usefulness (PU) and perceived ease of use (PEOU) (Lai, 2017). Perceived usefulness (PU) is operationalized as the extent to which an individual perceives that the adoption of a specific technology would be advantageous and improve job performance (Karkouti, 2021). Conversely, Perceived ease of use (PEOU) is operationalized as the extent to which an individual believes that using a particular system would involve minimum effort (Venkatesh et al., 2003). The external variables, PU and PEOU have an effect on attitudes, and this attitude, in turn shapes final usage (Venkatesh et al., 2000). Technology Acceptance model proved to be the most suitable framework compared to other alternative theories, such as the diffusion of innovations. This is because TAM aims to understand human behavior and predict how they adopt new technologies, while the diffusion of innovations primarily observes technology and categorizes individuals based on their adoption patterns as early innovators, early adopters, early majority, or late adopters (laggards). Figure 1 conceptually illustrates the Technology Acceptance Model (TAM).

Figure 1. The Technology Acceptance Model by Fred Davis (1986).

In the literature, some scholars such as Teo and Noyes (2011) and Venkatesh et al. (2003) have suggested that the Technology Acceptance Model (TAM) is a robust framework for analyzing behavioral intentions towards the usage of educational technologies. Therefore, this framework forms a basis for the current application of the TAM.

2. Methods

2.1. Study area

Kenya is comprised of 47 counties that serve as administrative units. This research was conducted in Embu County, which is among the 47 counties in Kenya. Embu County had 195 public secondary schools across five sub-counties: Embu East, Embu West, Embu North, Mbeere South and Mbeere North, which had 44, 25, 26, 54 and 46 schools, respectively. However, two of the five sub-counties that is, Embu East and Mbeere North were randomly selected for the study.

2.2. Study variable description

The uptake of educational technologies in CRE was the dependent variable of this study. A five-point Likert scale was used to measure uptake items. Each item had alternatives provided as 5 = always (A), 4 = often (O), 3 = sometimes (S), 2 = rarely (R), and 1 = never (N). This scale assessed the uptake of educational technology in the CRE. The mean values of the scores on the Likert scale for uptake were computed and interpreted as indicative of the level of students’ uptake, that is, a mean of 1.0–1.4 indicated never used, 1.5–2.4 indicated rarely used, 2.5–3.4 indicated sometimes used, 3.5–4.4 indicated often used while 4.5–5.0 indicated always used. The independent variable (students’ perceptions) was selected based on their ability to evaluate the correlation between students perceptions and the uptake of educational technologies in CRE. Kubiatko and Haláková (2009) discovered that students’ uptake of educational technologies in learning was mainly influenced by attitudes and the perceived impact of those technologies. To a great extent successful integration and uptake of educational technologies relies on students’ perceptions towards usage (Dawes & Selwyn, 1999). Negative perceptions lead to low or no uptake and vice versa. Becta ICT Research (2004) noted that negative perceptions act as an obstacle to the uptake process.

Students’ perceptions are grounded in their ability to envisage if they influence the uptake of educational technologies in CRE, as seen in previous studies (Bariham, 2022; Bariham et al., 2020). To gauge perceptions, a 5-point Likert scale was employed with options including 5 = strongly agree (SA), 4 = agree (A), 3 = undecided (UN), 2 = disagree (D), and 1 = strongly disagree (SD). This scale assessed students’ viewpoints regarding the application of educational technologies in CRE. The mean values of the scores on the Likert scale were computed and interpreted as indicative of the level of their combined perception on a range of questionnaire items. That is, a mean of 1.0–2.4 indicated Negative perceptions, a mean of 2.5–3.4 indicated neutral perceptions while a mean of 3.5–5.0 indicated positive perceptions. Perceptions play a fundamental role in an individual’s behavior and social judgement; hence they are critical predictors towards the adoption and uptake of educational technologies (Bariham, 2022; Venkatesh et al., 2003).

2.3. Sampling procedure and sample size

The research was conducted in Embu County, Kenya using a descriptive cross-sectional survey research design and a probability sampling strategy, specifically multistage random cluster sampling. The design allows data to be collected simultaneously with all understudied phenomena, without manipulating or influencing the study variables (Wang & Cheng, 2020). The sampling unit was schools. The sampling frame consisted of 195 public secondary schools drawn from five sub-counties (clusters): Embu East, Embu West, Embu North, Mbeere North, and Mbeere South. The final sample was randomly drawn from two clusters, Embu East and Mbeere North, with 44 and 46 public secondary schools, respectively. Mugenda and Mugenda (2013) suggest that a sample size of 10–50% of the sampling frame is adequate. Israel (1992) and Wanjala et al. (2017) further explained that a small sample size is sufficient for a homogeneous population to obtain the required level of precision. As a result, the study used a 15% sample size to arrive at a sample of 30 schools. From the 44 and 46 public secondary schools in the two sampling clusters, 30 were randomly chosen for the study. Consequently, from the 30 schools, 10 form two students from each school were randomly chosen for the survey. The final sample consisted of 300 students (Table 1).

Table 1. Sample size determination.

	Population	Sampling technique	Sample
Sub-counties	5	Random	2
Public schools	195	15%	30
Schools per sub-county Embu East Mbeere North	44 46	Random Random	15 15
Students	1200	Random	300 (10 per school)

2.4. Data collection instrument

A semi-structured questionnaire containing both open and closed-ended questions was employed to gather both qualitative and quantitative data from students. A questionnaire was considered suitable, as it enabled the researcher to collect views, recommendations, and suggestions from the respondents regarding their perceptions and uptake of educational technologies in CRE (Shahidzade et al., 2022). The school principals were involved in granting permission and consent to administer the questionnaires to ten randomly selected students in the form two class in the schools. To evaluate the suitability of the research questionnaire for gathering pertinent data in accordance with the research objective, a pilot study was conducted in Embu West Sub-County.

2.5. Statistical analysis

The study data were cleaned and coded prior to statistical analysis. The analysis was performed using Statistical Package for Social Sciences (SPSS) version 25. The researcher conducted various statistical procedures, including descriptive statistics such as frequencies, percentages, standard deviations, and means, as well as inferential statistics such as correlation and regression. The validation of the students’ questionnaires was established through a thorough review by research supervisors and experts in the field. Therefore, the questionnaire’s content validity was appropriate for evaluating the extent to which the research items offered a pertinent and representative sample of the items being examined (Saoke et al., 2023). A pilot test was conducted in Embu West Sub-County to further test its validity and reliability. Prior to any research, a pilot study should be conducted to identify any ambiguities and uncertainties within the research instrument (Ismail et al., 2017). Johanson and Brooks (2009) point out that in survey research, 10–30 participants should be considered for piloting research instruments. Therefore, this study used three schools and 30 students for piloting, after which the questionnaire items were adjusted appropriately for actual data collection. Ahmed and Ishtiaq (2021) argue that the evaluation of methodology for high-quality research heavily emphasizes the crucial significance of validity and reliability. The consistency of the questionnaire items was analyzed using Cronbach’s alpha formula (Singh, 2017). The formula is shown in Equation (1)(1) $$\propto =K.c/[v+(K-1\left)c\right]$$(1) . (1) $$\propto =K.c/[v+(K-1\left)c\right]$$(1) where:

K denotes the number of items in the assessment instrument.

c signifies the average inter-item covariance among the items.

v denotes the overall mean-variance.

The reliability coefficient (Cronbach’s alpha) of the students’ questionnaire yielded a score of 0.75. Taber (2017) highlighted that the acceptable Cronbach alpha reliability coefficient (α) should be 0.7 and above in Social Sciences. To increase the reliability factor, all questionnaire items that were negatively worded were positively re-worded to cater to respondents’ education levels and eliminate any ambiguities. Blasberg et al. (2016) explained that it is important to re-word statements to avoid ambiguities and uncertainties.

To examine the fitness of the research data, researchers employed diagnostic tests, such as the Cronbach reliability test. Additionally, both correlation and simple linear regression were executed to ascertain whether a linear association existed between the dependent and independent variables. Models such as System Generalized Methods of Moments (GMM) and Panel Vector autoregression (VAR) are typically employed to examine the relationships among variables. However, in this study, there was a dependent variable (uptake of educational technologies) and a solitary independent variable (students’ perceptions). Consequently, this study necessitated a statistical approach to examine and characterize the relationship between these two variables. Therefore, the primary analytical model employed was simple linear regression analysis. Prior to simple linear regression, researchers assessed the suitability of the data for regression using Pearson correlation analysis to examine the linear relationship between the independent and dependent variables. Simple linear regression analysis was used to examine and quantify the nature of the relationship between dependent and independent variables. The regression model in this study is shown in Equation (2)(2) $$Y=B_0+B_1{X}_1+\mu$$(2) . (2) $$Y=B_0+B_1{X}_1+\mu$$(2) where Y represents the dependent variable (uptake of educational technologies), B₀ is the intercept/constant, B₁ is the regression co-efficient of X₁ (independent variable), X₁ is the independent variable (students’ perceptions), and µ represents the error term, which includes random variabilities that are not considered in the model.

2.6. Ethical consideration

The researcher adhered to the ethical regulations put in place by the Board of Postgraduate Studies at the University of Embu, where an approval to conduct the research was issued. In addition, the process included a thorough review of research ethics and approval from the National Commission for Science, Technology, and Innovation (NACOSTI) of the Ministry of Higher Education prior to data collection. As part of the process, a research approval license NO: NACOSTI/P/23/23007, was successfully applied for and obtained. To gain access to Embu County public schools and students, the researcher sought consent and permission from the Office of the County Director of Education in the Ministry of Education, State Department of Early Learning and Basic Education, Embu County. As a result, the study was further reviewed, and permission was granted with the authorization letter Ref: EBC/GA/32/1/Vol. V/91. For the selected schools, consent was granted by school principals during the research period. This study adhered to the principles of confidentiality and voluntary participation. Furthermore, respondents were assured that the data they provided were for academic purposes only and the principle of anonymity would be upheld.

3. Results and discussions

3.1. Descriptive characteristics

Table 2 presents the descriptive statistics of the students where N = 300.

Table 2. Students’ demographic information.

	Description	Frequency	Percentage
Gender	Male	125	42%
	Female	175	58%
	Total	300	100%
Age	14–15 years	63	21%
	16–17 years	120	40%
	Above 17 years	117	39%
	Total	300	100%

The study results regarding gender indicated that the majority of the students in the CRE group were female, comprising (52%) of the total. This aligns with the previous findings of Hejji Alanazi (2019) and Saoke et al.(2022) that CRE is perceived to be a female-dominated subject. The data on students’ ages indicated that the majority of the students (237 [79%] were16 years and above) while 63 (21%) were between 14 and 15 years. According to the ministerial policy in Kenya, on average, form two students should be between 15 and 16 years. However, the majority of the students were above 16 years of age. This was attributed to challenges such as persistent poverty, increased dropout rates, and repetition, as outlined in the UNESCO and International Ministry of Education report in Kenya. These challenges affect the progression of formal education in rural and marginalized areas where one of the study sub-counties belongs (Ministry of Education, 2001; Sabates et al., 2011). Additionally, this was also attributed to the government effect of a 100% transition rate, whereby it was mandatory for all Kenyan children to enroll in basic primary and secondary school education. This policy was implemented to combat illiteracy, increase access to education, adhere to constitutional rights to education, and achieve the Vision 2030 Agenda (Otieno & Ochieng, 2020)

3.2. Students’ perceptions towards educational technologies

Table 3 presents students’ perceptions items towards educational technologies.

Table 3. Students’ perceptions.

Statement	SD	D	UN	A	SA	M	Std Dev
I would like to learn and use technology in CRE.	0.3%	2.0%	6.0%	28.7%	63%	4.52	.73
Using technology makes my learning interesting in CRE.	1.7%	0.7%	5.3%	32.3%	60%	4.48	.77
Technology enables me to understand concepts better in CRE through accessing materials.	2.0	2.3%	5.7%	35.7%	54.3%	4.38	.86
Technology helps me to be more creative and innovative in CRE.	0.7%	2.3%	7.3%	44.3%	45.3%	4.31	.76
Educational technologies enhance my own learning in CRE thus motivating me.	1.3%	1.7%	7.7%	48%	41.3%	4.26	.78
I find technology useful as it makes my work easier in CRE.	4.0%	3.7%	8.7%	39.7%	44%	4.16	1.01
Technology can improve the way my CRE teacher presents learning materials	2.7%	5.3%	7.7%	43.3%	41%	4.15	.96
Using educational technologies to learn CRE saves my time and effort	3.7%	7%	8%	34.3%	47%	4.14	1.07
Educational technologies make me more productive in CRE.	2.7%	3.7%	11%	45.3%	37.3%	4.11	.93
I find technology useful for leisure activities e.g. interacting through CRE WhatsApp group.	13.3%	6.7%	7.7%	28.3%	44%	3.83	1.40
I prefer my CRE teacher’s method of teaching (conventional method)	13.7%	26.7%	13.3%	20.7%	25.7%	3.18	1.42
Overall item means						4.14	.97

SD: Strongly Disagree; D: disagree; UN: undecided; A: agree; SA: Strongly Agree; M: Mean; Std Dev: Standard Deviation.

The study items in Table 3 reflects students’ overall perceptions towards educational technologies in CRE. The highest scoring item was ‘I would like to learn and use educational technologies in CRE’ which had a mean of 4.52 whereas the least scoring item was ‘I prefer my CRE teacher’s method of teaching (conventional method)’, which had a mean (3.18). Students acknowledged the benefits of using educational technologies, for example, enhancing their learning, making it interesting, motivating them, and enhancing creativity and efficiency. However, there were other students who preferred technologies for leisure and traditional methods of learning, which were the least scoring items with means of 3.83 and 3.18, respectively. This implies that students consider the benefits of using technology in learning. Therefore, the overall mean was 4.14, which revealed that most students had positive perceptions towards educational technologies and agreed with the items under study. In addition, Ramnarain and Ramaila (2018) explain that an overall mean score above (3.0) on a Likert scale is classified as very high and indicates a very positive attribute of the variable under study. The findings corroborated those of Malekani (2018), whose research in Tanzania also revealed that students had positive perceptions towards educational technologies. Research by Lumpkin et al. (2015) and Sari and Wahyudin (2019) emphasized the need for students to maintain positive perceptions of educational technologies. This requires creating a learner-centered environment that is both engaging and enjoyable for technology-assisted learning. Additionally, teaching of technological skills should be enhanced by incorporating suitable digital literacy into the curricula. It is also essential to guarantee accessibility to educational technologies that are user-friendly and customized to accommodate the diverse needs and preferences of students.

3.3. Uptake of educational technologies among the students

Table 4 displays students’ uptake of educational technologies.

Table 4. Students’ uptake levels.

Educational technologies	N	R	S	O	A	M	Std Dev
Computers	51.3%	17.7%	51%	25%	17%	1.99	1.24
Internet	24%	13%	27%	18.3%	17.7%	2.93	1.41
Smartphones	12.3%	9%	28.7%	19.3%	30.7%	3.47	1.34
Laptops	46%	13.7%	23.3%	8.3%	8.7%	2.20	1.33
Google	30.7%	12.3%	22.3%	14%	20.7%	2.82	1.51
Virtual Learning Environment e.g. zoom & google meet	72.3%	10.7%	9%	3.3%	4.7%	1.57	1.09
Social learning platforms e.g. WhatsApp	34.7%	19%	21.3%	8%	17%	2.54	1.46
Total means						2.50	1.34

N: never; R: rarely; S: sometimes; O: often; A: always; M: mean; Std Dev: Standard Deviation.

From the results on students’ uptake of educational technologies (Table 4), the highest score item was smartphone, with a mean of 3.47, indicating that it was often used. Conversely, the least scored item was virtual learning environments with a mean of 1.57, indicating that they were rarely used. The overall mean of the students’ uptake level was 2.50, revealing that educational technologies among the students were sometimes used. Additionally, the uptake Likert scale showed that a mean of between (2.5–3.4) suggested that the technologies were sometimes used in the learning of CRE by the students which is in agreement with the study by Bariham et al. (2020), which highlighted the infrequent use of computers. Their research showed that 89.5% of the participants never used computer tutorials, 63.1% never engaged with simulations, and 75.8% did not use applications. In contrast, the current study indicated that social learning platforms were sometimes used, and mobile phones were often utilized. This is in contrast with the findings of Bariham et al. (2020) who suggested that these tools were not used at all.

3.4. Correlation analysis between students’ perceptions and uptake of educational technologies

The aim of this study was to determine the link between students’ perceptions and their uptake of educational technologies, as shown in Table 5. Pearson correlation coefficient was computed, followed by simple linear regression analysis. This is because the Pearson correlation moment is used to measure the strength of the linear relationship between two variables in the study (Ahlgren et al., 2003).

Table 5. Students’ correlation (perception vs uptake).

		Students’ uptake levels
	Pearson correlation	.180**
	Sig. (2- tailed)	.002
Students’ perceptions	N	300

** Correlation is significant at 0.01 level (2-tailed).

To establish the relationship between students’ perceptions and the uptake of educational technology, a correlation analysis of their Likert means was performed. The findings indicated a weak positive correlation between students’ perceptions and their uptake of educational technologies in CRE (r = .180, p = 0.02). The p-value of (0.002) was less than (0.05), signifying that the correlation is statistically significant at the 99% confidence level. The results showed that students’ perceptions played a pivotal role in influencing the uptake of educational technologies among students in CRE. These findings were in agreement with those of Hussein (2017) who found that students’ perceptions and attitudes significantly impact their willingness and intention to utilize educational technologies. Conversely, the results differed from those of Yildiz Durak (2023) who found no correlation between the use of chatbot and factors such as visual design self-efficacy, engagement, satisfaction, and students’ autonomy. The findings suggest that frequency of usage and user satisfaction significantly impact learners’ self-efficacy.

3.5. Regression analysis

Sanderson and Windmeijer (2016) explained that statistical assumptions of linearity, independence, normality, and homoscedasticity should be checked before regression analysis is carried out. These assumptions were met as shown in Table 8 (Durbin Watson test). The graphs in Figures 2 and 3 show the normality assumption, Figure 4 shows the linearity assumption, and Figure 5 shows the homoscedasticity principle.

Figure 2. Histogram for students’ perceptions towards educational technologies.

Figure 3. Histogram for students’ uptake levels of educational technologies.

Figure 4. A plot of uptake verses students’ perceptions.

Figure 5. Residuals vs fitted values; uptake vs students’ perceptions.

Figures 2 and 3 show students’ perceptions towards educational technology and their uptake levels, respectively. These findings indicated that the data were normally distributed. Figure 4 shows the linearity of uptake versus students’ perceptions. Based on Figure 5, the homoscedasticity principle was not violated because the residuals in the scatter plot converged around zero. Homoscedasticity examines the pattern of residuals (discrepancies between the observed and predicted values) at different levels of the independent variable. This assumption is verified by visually inspecting a plot of standardized residuals against the standardized predicted regression value (Hayes & Montoya, 2017). Ideally, the residuals should be randomly scattered around the zero value to ensure an even distribution and different shapes (Osborne & Waters, 2003). The Durbin Watson test was 1.576 (Table 6), which means that there was no autocorrelation; therefore, the independence assumption was also met. In comparison, statisticians generally consider values between 1.5 and 2.5 to be typical, where as values outside this range can be a cause of concern (Glen, 2016).

Table 6. Model fitness of Students’ perceptions Vs Uptake.

Model	R	R Square	Adjusted R square	Standard error of the estimate	Durbin- Watson
1	.180^a	.032	.029	.88005	1.576

^a Predictors: (Constant) Perceptions.

Regression analysis also verified fitness of the model, as shown in Table 6.

The R-squared value represents the coefficient of determination which indicates the extent to which the independent variable could explain the dependent variable (Saoke et al., 2023). However, in a regression analysis with two or more independent variables, the adjusted R-square is used as the coefficient of determination (Yudiawan et al., 2021). In this study, there was one independent variable (students’ perceptions); therefore, the R- squared was used as the coefficient of determination. Simba et al. (2016) stated that the coefficient of determination should be between 0 and 1 to be considered significant, whereas in this study R² = 0.032 (Table 6). Therefore, R² = 0.032 (3.2%) suggests that perceptions explain 3.2% of the variation in students’ uptake of educational technologies in the CRE. In other words, the input of the independent variable to the dependent variable was 3.2%. This means that, the remaining 96.8% are described by variables other than the study’s model. For instance, studies on technology acceptance conducted by Xiao et al. (2019) in India and Torppa et al. (2018) in Finland underscored the significance of socio-demographic factors such as gender and family background in influencing computer-based assessments. Similarly, Hanham et al. (2021) and Padilla-meléndez et al. (2013) identified the significant impact of gender on the acceptance of educational technologies in learning. Conversely, a study on acceptance and usage of technology among Science and Mathematics students by Skryabin et al. (2015) revealed that students’ educational levels (grades) played a role in technology adoption. The study found a positive association between educational technology usage and the reading performance of 4th-grade students, while indicating a negative correlation with that of 8th-grade students. Therefore, the research findings underscore the importance of students’ perceptions towards the uptake of educational technologies in CRE. The Analysis of Variance (ANOVA) should be computed to assess whether the model fit the study data (Mungeria, 2021; Nzomo et al., 2023). The results are summarized in Table 7.

Table 7. ANOVA: Students perceptions Vs uptake.

Model	Sum of squares	df	Mean square	F	Sig
Regression	7.750	1	7.750	10.006	.002
Residual	230.799	298	.774
Total	238.549	299

Following the analysis, the model was statistically significant, F (1, 298) = 10.006 and p = 0.002, which was less than 0.05, indicating that the model was suitable and justifiable for regression analysis. This implies that perceptions significantly influence the uptake of educational technologies in CRE learning. Regression coefficients were also computed to examine the impact of students’ perceptions on the uptake of educational technologies, and the results are presented in Table 8.

Table 8. Distribution of coefficients.

	Unstandardized coefficients		Standardized coefficients
Model	B	Std Error	Beta	t	Sig.
1 Constant	1.152	.430		2.681	.008
Perceptions	.326	.103	.180	3.163	.002

The results (β = 0.180, p = 0.002) showed a positive weak relationship between students’ perceptions and their uptake of educational technologies. In addition, a unit increase of 0.326 in perceptions towards educational technologies could possibly lead to an increase in the uptake of educational technologies in CRE. These results corroborate Gray et al. (2020) and Joo et al. (2014) who found a substantial relationship between perceptions and the uptake of educational technologies. However the results differ from those of Agasisti et al. (2020) and Kunina-Habenicht and Goldhammer (2020), who found a negative correlation between students’ perceptions and the usage of educational technology.

4. Conclusions and policy recommendations

This study investigated the relationship between students’ perceptions and the uptake of educational technologies in CRE in Embu County, Kenya. Educational technologies are instructional innovations that enhance learning and greatly impact the way content is taught by leveraging students’ diverse culturally unique experiences and identities. The study revealed a positive weak relationship between students’ perceptions and their uptake of educational technologies (r = .180, p = 0.02). According to the results, this study suggests that improving perceptions of educational technologies has the potential to increase use of educational technologies. The results highlight several strategies and policies that can be implemented in Kenya and similar countries to improve students’ perceptions. These strategies include ensuring accessibility and user-friendliness of educational technologies, designed inclusively to cater to the diverse needs and preferences of students. Additionally, enhancing the teaching of technology skills by incorporating appropriate digital literacy into the curricula. The implications of these results also are noteworthy for a wide range of stakeholders, including educators, policymakers, and scholars actively involved in technology education. Based on the findings, the research proposes the following recommendations: First and foremost, it is imperative for policymakers, including entities like the Ministry of Education (MoE) and the Kenya Institute of Curriculum Development (KICD), to develop comprehensive educational technology policies when designing the national curriculum. These policies need to be explicit, clear, and customized to accommodate the perceptions, ideas, opinions, experiences, and diverse needs of students. This approach aims to establish an inclusive and supportive environment for integrating technology into the instructional process, acknowledging, and leveraging the unique needs of individual students. Secondly, the Ministry of Education (MoE) should advocate for the integration of educational technologies into public secondary schools. This initiative is essential to facilitate the shift from conventional teacher-centered learning pedagogies to 21st-century, technology-based, student-centered learning approaches. Such a transition aligns with the objectives of the Vision 2030 agenda and supports the ongoing adoption of a Competency-Based Curriculum (CBC). The CBC emphasizes the development of key competencies, particularly digital literacy, among students. Additionally, the study recommends further research to explore additional critical factors influencing the uptake of educational technologies by CRE students, beyond perceptions alone. This recommendation is driven on the findings, which indicate that perceptions contribute only 3.2% to the variation, implying that the remaining 96.8% is influenced by other factors beyond those considered in the current study model. A more extended duration study on students’ perceptions and uptake of educational technologies in other parts of Kenya could be undertaken in the future. This would offer deeper insights into the integration of technology in the education sector, particularly in the teaching and learning of CRE. A similar investigation could be carried out in diverse educational settings, including private secondary schools (given that the present study focused on public secondary schools) and across different educational levels such as primary schools, colleges, and universities, considering the distinct characteristics of these settings. Finally, the outcomes of this study are anticipated to make a valuable contribution to academic research. The investigation delved into students’ perceptions and the uptake of educational technologies in CRE in Embu County, Kenya. These findings can serve as a reference point for academic research in the realm of technology education. Moreover, the research outcomes can be applied to practical contexts, providing valuable assistance to educators.

Ethics declarations

This study was reviewed by the Ministry of Education, State Department of Early Learning and Basic Education, Embu County, where consent was granted via the authorization letter Ref: EBC/GA/32/1/Vol. V/91. To access public secondary schools in Embu County, teachers, and students. Additionally, the research underwent thorough research ethics review and informed consent was obtained from the National Commission for Science, Technology, and Innovation (NACOSTI), in the Ministry of Higher Education, where an approval research license was issued with approval No. NACOSTI/P/23/23007. For the selected schools, permission and consent were granted by school principals during the research period. This study adhered to the principles of confidentiality and voluntary participation. Furthermore, respondents were assured that the data they provided were for academic purposes only and that the principle of anonymity would be upheld.

Acknowledgement

We would like to acknowledge the University of Embu, County Director of Education, Embu County, for their approval and authorization in conducting the research. We also thank the form two students in Embu East and Mbeere North Sub Counties in Embu County for participating in the survey.

Disclosure statement

No potential conflict of interest was reported by the author (s).

Data availability statement

Data will be made available on request.

Additional information

Notes on contributors

Rachael Wanjiku Gitiha

Rachael Wanjiku Gitiha, is a postgraduate student in the Department of Education, University of Embu, Kenya. Her research focuses on Educational Technology and the teaching and learning of Christian Religious Education (CRE). She can be contacted at email: [email protected]

Peter Rugano

Dr. Peter Rugano, is a science teacher educator at the University of Embu, Kenya. He holds a doctorate degree in Science Education from Syracuse University in United States of America (USA). He has wide experience in Basic Education Leadership, Science Teaching and Education Technology. His research interest is in the Development of Quality Teachers, School Leadership, and Instructional Technology. He can be contacted at email: [email protected]

Steve Wakhu

Dr. Steve Wakhu is a specialist in Criminology, Conflict, Peace, and Security Studies at the University of Embu, Kenya. He holds a Doctor of Social Science (Dr. rer. Pol, 2020). and a Master of Public Policy in International Conflict Studies and Conflict Management (MPP, 2014); both from the University of Erfurt in Germany. He has wide experience in Criminology and Security Management Studies. His research interests are in Crime, Criminal Justice, Armed Conflict, Peace and Security, Post Conflict Reconstruction and Terrorism. He can be contacted at email: [email protected]

Ciriaka Gitonga Muriithi

Dr. Ciriaka Gitonga Muriithi is a teacher educator, an expert in educational psychology, a senior lecturer at the University of Embu, Career Development Instructor (CDI), Career Development Facilitator (CDFI) and a founder member Career Development Association of Kenya (CDAK). She also holds the position of the Dean of the school of Education and Social Sciences, at the University of Embu. Her research focuses on Education Issues, Education Policy, Teacher Education, Inclusive Education, and Research Grants Writing. She can be contacted at email: [email protected]