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Abstract

The increasing number of students in higher education has led to the formation of large class teaching and learning environments, which is a threat to quality education. The Department of Fashion Design and Textiles Studies of Kumasi Technical University is one such department that is facing this challenge. Computer-based technology has infiltrated many aspects of academic activities, yet there is little understanding of how it can be used to promote student engagement and educational data analysis in the classroom. This study explores the analytics readiness of students at the Department of Fashion Design and Textiles of Kumasi Technical University. The study used a mixed method of research and customized the JICS analytics readiness questionnaire as an instrument. With inspiration from the UTAUT model, a conceptual framework was designed to explore the factors (facilitation condition, social influence, and performance expectancy). With a sample size of 221 obtained with the purposive sampling technique, a descriptive and regression analysis were conducted to analyze data gathered from the respondents. The study revealed that existing structures, culture and policies in the institution are ready at its foundational state but will need an upgrade to sustain future educational data complexities. Also, the analysis revealed that facilitation conditions and performance expectancy have a significant effect on the student’s readiness to adopt LA. However, there was no significant relationship between social influence and students’ readiness. It is recommended that the needed resources for online learning should be provided coupled with appropriate policies to guide students in LA adoption.

Keywords

analytic readiness teaching learning higher education technology

Introduction

For the past few decades, educators have witnessed a wide range of computer technology integration into the teaching and learning environment, leading to many novel advancements in educational settings (Grassini, 2023; Mhlongo et al., 2023). This has also triggered a wide variety of research into technology to enhance teaching and learning within the educational system (Tahat et al., 2023; Vahdat et al., 2015). The move is in the right direction because computer technology has infiltrated many stages and aspects of human development. This is evident in the ubiquity of mobile devices and the seamless integration of technology into daily activities such as shopping, exploring, reading, and many others (Schindler et al., 2017; Wang & Chen, 2023). In the 21st century, education is taking a dynamic direction, with teaching and learning increasingly occurring beyond classroom settings and formal learning activities. Technology plays a central role in this extension of learning to new places, times, and modes (Educause, 2013; Haleem et al., 2022). Ifenthaler (2017) is of the view that these numerous technologies provide a lot of opportunities for the effective use of educational data. The availability of educational data has created another interesting research direction known as Teaching and Learning Analytics (T&LA) which focuses on the use of educational data for decision-making in the classroom, department and faculty, institutional and national levels. Learning analytics is typically defined as the measurement, collection, analysis, and reporting of data about learners and their contexts, for understanding and optimizing learning and the environments in which it occurs (Masiello et al., 2024; Mazzullo et al., 2023; Siemens & Gasevic, 2012).

Ferguson (2012) noted that Teaching and Learning Analytics is a rapidly growing field of Technology-Enhanced Learning (TEL) research, due to its numerous benefits derived from previous fields of study such as business intelligence, web analytics, educational data mining, and others. Given the benefits of this novel technology, many researchers and educators are working to implement T&LA in their institutions. However, there is a need to explore the readiness of institutions’ faculties for the implementation of T&LA. In general, educators are more likely to accept technology that is consistent with their teaching style, familiar to them, and aligned with their teaching philosophy (Bice & Tang, 2022; Gülbahar, 2008). This study is also geared toward this direction, exploring and analyzing the feasibility of implementing T&LA by conducting quantitative and qualitative research methods to inform future research decisions in this area.

Teaching and Learning Analytics

As clearly defined by Hernández-de-Menéndez et al. (2022) and confirmed by Masiello et al. (2024) and Peña-Ayala (2017) among several scholars, Learning Analytics (LA) is the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. Teaching and Learning Analytics focuses on making meaning out of data and insights from LA to be used effectively by teachers for improved teaching methods using the LA tools available (Sergis & Sampson, 2017). Learning analytics can be summarized as the collection, analysis, and application of data accumulated to assess the behavior of educational communities. The application of learning analytics help obtain vital information about learning activities in educational setting and the learners involved (Nguyen et al., 2023). This is done through diverse scientific approaches to determine the learning experience of students. Whether it be through the use of statistical techniques and predictive modeling, interactive visualizations, or taxonomies and frameworks, the ultimate goal is to optimize both student and faculty performance, refine pedagogical strategies, streamline institutional costs, determine students’ engagement with the course material, highlight potentially struggling students (and to alter pedagogy accordingly) to fine-tune grading systems using real-time analysis, and to allow instructors to judge their educational efficacy (White & Larusson, 2014). This is unlike conventional teaching systems where educators usually obtain feedback on student learning experiences in only face-to-face interactions. In a seamless learning environment, an adaptation of learning analytics is the best strategy to assess and track students’ academic well-being (Moon et al., 2024). As an online activity, learning analytics is a digital-based learning activity that requires technology literacy for effective adoption. It is therefore posited that T&LA implementation in educational institutions requires adequate equipping of both teachers and learners for a full appreciation of its usefulness to both parties (Mazzullo et al., 2023).

Past studies have deliberated extensively on the benefits and challenges involved in T&LA application in higher education (Aguilar, 2018; Holstein & Doroudi, 2019; Mazzullo et al., 2023; Reinholz et al., 2019; Sharif & Atif, 2024; Tzimas & Demetriadis, 2024). It is established that LA offers massive benefits in the educational environment such as the increase in learner’s involvement in learning activities, easy tracking of students at risk academically, and acquisition of reliable feedback (Ara et al., 2023). This has led to several recommendations for its adoption in educational institutions, especially in the 21st century and beyond where technology adoption is prevalent in diverse sectors. Contrary to this, research has also identified several setbacks that make LA adoption questionable. Paolucci et al. (2024) observed that LA is said to have harmful impacts such as improper usage and misinterpretation of data, and privacy infringement coupled with no evidence of improved outcomes. These notwithstanding, steps are being taken by scholars for LA improvement to help in decision-making, especially in educational settings.

The Need for Data-Driven Decision Making

Over the years, some educational technologists have pushed the agenda of establishing a robust system of education technological innovative ways of enhancing the operations of the school to solve common problems (Amakyi, 2013; Sliwka et al., 2024). And just as it usually happens in most institutions in Ghana, these proposals, though not all together rejected, seem not to attract much attention in institutions where the conventional practice is most prevalent. During the covid-19, there was a huge wake-up call to institutions in the area of technological infrastructure and online delivery with a massive invasion of devices in the educational system (Dramani et al., 2022) and Kumasi Technical University (KsTU) was no exception. These technologies, together with the existing ones, created more complexity in educational settings which require suitable and timely solutions. In such situations, Higher Educational Institutions are pushed to adopt best managed technological resource strategies to provide the needed creative solutions (Eshun, 2012). In the same light, the researchers are of the view that there is a greater need to consider Learning Analytics to provide a better insight in the educational system to foster data driving decisions for today and the future. This will go a long way to improve upon the academic development of the institution.

Solving Problems With Teaching and Learning Analytics in High Education

According to Elias (2011), educators have always sought to answer some obvious questions as solutions to solving problems in the educational environment. Irrespective of the method of education, a series of questions often arises: How effective is the course being pursued? Is it meeting the needs of the students? How can the needs of learners be supported? What are the most effective interactions? How can they be further improved in future? Traditional approaches to answering these questions have not proven to be so effective because they rely on students’ evaluation, the analysis of grades and attrition rates, and instructor perceptions most often gathered at the end of a semester or completion of a course. As a result, the quality of evaluation and analysis of learning tends to be lacking. Consequently, lack of proper feedback and self-reflection tends to delay intervention and slow down the pace of development. Data offers huge opportunities in the educational community and could be used significantly to improve decision-making in higher education (Liu, 2023; Prinsloo & Slade, 2017). Both corporate and research data are collected and managed to be used for running of operations of higher institutions (McNicol, 2024). Information concerning students and staff of educational institutions is used for making predictions to help improve resource allocations to reduce costs. Also, data helps management to track the performance of students on frequent basis for academic advancement.

The increasing number of students in higher education has led to the formation of large class teaching and learning environments, which can hurt the quality of instruction, skill acquisition, student performance, and motivation for progress (Hornsby & Osman, 2014). The Fashion Department of the Faculty of Creative Arts and Technology, KsTU is one such department that is facing this challenge. The Department admits students with a wide range of strengths and weaknesses in the fashion design process. This complexity for instance, combined with the large number of students enrolled in higher education today, has led most educators to research the best ways to utilize technological resources and provide the necessary creative solutions. Preliminary studies revealed that a lot of lecturers were facilitating over 500 students in a semester. In such situations, educators would have to devise strategies to handle these large classes. These have attracted a growing need and interest in learning analytics and Educational data mining, which provides data and visualizations to monitor and inform decision-making.

The current study focuses on analytics readiness among students as it is a major area to consider in the quest to go digital with teaching and learning activities for a significant improvement in a tertiary institution like KsTU. Tsai et al. (2020) see LA as a promising field that could greatly impact quality teaching and learning in higher education when the needed attention is received from researchers. A recent study (Márquez et al., 2024) posits that LA is used significantly to track the learning process of students in various disciplines and monitor their training activities. KsTU is a university devoted to science, technology, and entrepreneurship education, with various practical-based programs including fashion design. As such, the university needs to track the progress of its students as part of its core activities. In-depth research in this area (LA) would therefore serve as good grounds for the management of the University to make appropriate decisions regarding online facilitation. Alzahrani et al. (2023) and Tsai et al. (2020) found the need to address the challenges involved in adopting LA since most higher education institutions are unable to adopt it irrespective of the benefits it comes with. Addressing this topic from different geographical locations becomes relevant to understanding the requisite steps for implementing LA policies in different educational settings.

KsTU has witnessed some transformational changes in the light of computer technology since the Polytechnic was established. There have been several advocates for online teaching and learning adoption among both students and lecturers by the university management body. An example of available resources is the virtual classroom (Vclass) adopted by the university for online teaching and learning. Computer-based technology has infiltrated many aspects of academic activities, yet there is little understanding of how it can be used to promote student engagement and educational data analysis in the classroom; a concept receiving strong attention in higher education due to its association with several positive academic outcomes (Schindler et al., 2017). This study seeks to highlight on the need for “Teaching and learning Analytics,” especially in higher education. It also explores and analyzes Teaching and Learning Analytics readiness in Kumasi Technical University to suggest and recommend the best ethical use of data for future studies.

Theoretical Development and Hypothesis Formulation

Theoretically, the current study is underpinned by the Unified Theory of Acceptance and Use of Technology (UTAUT) model to help examine the readiness of Kumasi Technical University students to adopt T&LA in their studies. The UTAUT model proposed by Venkatesh et al. (2003) is validated to be statistically significant and more reliable for solving issues relating to Information Technology (Ahmed et al., 2024; Venkatesh et al., 2012), hence its adoption in the present study for pressing issues regarding ICT adoption at Kumasi Technical University. This adopted model comes with four key predictors indicating the readiness of people to adopt technology in their practices in various sectors and geographical settings (C. Y. Lai et al., 2024). These include Effort Expectancy, Performance Expectancy, Social Influence, and Facilitating Conditions. The present study however adopts three of the core predictors by exploring the effect of performance expectancy, social influence, and facilitation conditions on the students’ intention to use T&LA. It is established that performance expectancy defines the degree to which people believe using technology will help them gain much in their job performance (Alghazi et al., 2021; Brandsma et al., 2020; Haynes et al., 2024). Also, social influence is defined as the importance placed by others on the use of technology (Tanantong & Wongras, 2024). It predicts how an individual’s willingness to adopt technology is influenced by their peers, families, supervisors, etc. (Cao et al., 2024; Cioc et al., 2023). Facilitation condition as a predictor under the UTAUT model refers to the availability of the required technical support for effective system utilization (Bayaga & du Plessis, 2024). These predictors adopted in this study collectively help interpret the willingness of KsTU to use T&LA.

Ethical and Policy Considerations: Facilitation Conditions

The availability of technology resources greatly influences individuals’ willingness to adopt it in their practices. With the upsurge in technology, several ethical issues are considered before the adaptation of certain technologies, especially in established institutions (Barsky, 2019; Kisselburgh & Beever, 2022; Mala & Dar, 2024; Stahl et al., 2017). The advent of Generative AI for instance has seen several criticisms regarding ethical issues and integrity within scholarly environments including higher education (Hosseini et al., 2024; Qadhi et al., 2024). This has led to some institutions banning the use of such technology tools in their settings (Michel-Villarreal et al., 2023). Nonetheless, some scholars believe such tools are inevitable and need not be banned (Dis et al., 2023). The establishment and implementation of favorable policies to guide the use of these technology tools in organizations helps to encourage and boost the readiness of individuals to adopt the same for massive improvement. According to Dhirani et al. (2023), the major ethical issues confronting emerging technologies include risks associated with Artificial Intelligence, health implications due to technology use, data privacy, developing sustainable environments, and infodemic and data weaponization issues. These issues mostly deter people from adopting technologies recently. Research shows that the issues could be addressed by the setting and implementation of policies to regulate technology usage (Dhirani et al., 2023; Maccaro et al., 2024). Evidentially, considering appropriate and favorable policies and ethical issues for the use of technology and data in higher educational institutions would certainly lead to massive technology adoption. We then hypothesize that:

H1: Facilitation Conditions significantly and positively influence students’ readiness to use learning analytics

Stakeholder Engagement and Data Ownership: Social Influence

Data plays a significant role in T&LA for decision-making in establishments including higher institutions. It has become a source of inspiration, innovation, and knowledge leading to questions regarding privacy, ownership, and preservation (Asswad & Gómez, 2021). The unavailability of data as a result of issues regarding data ownership and stakeholder engagement, however, is a hindrance to adopting learning analytics in higher education. There have been several concerns about the difficulty of releasing data due to accountability issues on the side of companies regarding ownership in the case of data leakage (Fadler & Legner, 2022). Cybersecurity, trust issues, and privacy have been a deterrence to data sharing among organizations (Tan et al., 2023). Making data available to individuals by owners and stakeholders for analysis and subsequent use for decision-making would improve the willingness of people to adopt data analytics. For instance, higher educational authorities showing concern by emphasizing their readiness to assist and provide the needed data for students and staff for data analytics is a great form of social influence on individuals, especially students for effective adoption of T&LA in their studies. As leaders play a key role in influencing people to adopt technology (Cortellazzo et al., 2019; Lathabhavan & Kuppusamy, 2024), higher institutional bodies have the power to socially influence learners to adopt T&LA by placing more importance on it. It is established that support (incentives and encouragement as a form of social influence) from higher institutional bodies and stakeholders is a factor that influences technology adoption by teachers and students (Nagy & Dringó-Horváth, 2024). It is therefore hypothesized that:

H2: Social influence significantly affects students’ readiness to adopt learning analytics

Learning Analytics Acceptance and Implementation: Performance Expectancy

The willingness of teachers and students to accept and implement T&LA is dependent upon the belief that it is capable of increasing their technology literacy and knowledge advancement. Given that performance expectancy predicts the perceived increment in job performance (Alduais & Al-Smadi, 2022; Rahman et al., 2021; Venkatesh et al., 2003), learners would want to be sure of what to gain in adopting technology (T&LA) in their studies. For instance, during the COVID-19 period, most educators and stakeholders turned the crisis into opportunities by adopting e-learning systems to run their systems as far as teaching and learning activities are concerned (Hani et al., 2021; Shahriar et al., 2023). E-learning was adopted with the belief that it would help sustain teaching and learning effectively (Elberkawi et al., 2022; Islam et al., 2023). Students were compelled to submit online assignments and engage in online lectures as it happened to be the best way to help them get involved in learning. The post-COVID-19 era is still witnessing the use of e-learning in some educational institutions due to its perceived usefulness while others have gone back to face-to-face methods of teaching. It is established that the more students face difficulty in using technology tools such as online teaching and learning platforms, the lower their willingness to adopt such in their studies (Jafar et al., 2023), with the notion that it does not support their performance expectancy. According to research, gaining interest in adopting technology in higher education is directly affected by perceived usefulness (Xie et al., 2023). The perceived usefulness of T&LA among higher education institutional students would therefore inform their willingness to accept and implement it in their studies. Based on this, we hypothesize that:

H3: Performance Expectancy positively influences students’ readiness to use learning analytics

Conceptual Framework

The framework of the current study shows the prediction of students’ readiness to accept T&LA with facilitation conditions, social influence, and performance expectancy. From the framework, it is assumed that the dependent variable (students’ readiness) would be influenced by the predictors (facilitation conditions, social influence, and performance expectancy) positively. Figure 1 shows the conceptual framework developed for the study with inspiration from the UTAUT model.

Figure 1.

A conceptual framework developed for the study.

Methods

Design, Setting, and Participants

The study was designed to explore the analytics readiness to facilitate a pilot project for analytics initiative in the Faculty of Creative Arts and Technology (FCAT) of KsTU. For the exploration, a survey with the JICS analytics instrument was adopted to ascertain the readiness for a successful implementation. LA being an emerging educational technology, we sought to explore the culture and practices closely related to teaching and learning analytics to establish the preparedness for a successful implementation in the school. This is usually adopted when educators are confronted with an information need and insufficient data (Scheuren, 2004). As a mixed-method study, the purposive sampling techniques was used to gather relevant information from the respondents. The sample for the study comprised 57 males, 163 females, and an anonymous (n = 221) from HND regular students attending public technical university in Ghana. Again, the population involved all students offering HND and BTech fashion design in the Faculty of Creative Arts and Technology of Kumasi Technical University, Kumasi. The study sampled 2018/2019 HND students registered in the fashion design department. The ages ranged between “below 19 years” and “above 40 years” with heterogeneous educational backgrounds. The study adjudged purposive sampling as an appropriate technique in the current research to carefully select the respondents based on a specific target population and sample (2018/2019-year group students in FCAT and faculty staff). To obtain reliable responses for such a study based on the customized questions, it became necessary to stick to one faculty (FCAT). This was done to exclude respondents without affiliation with fashion design for a fair assessment. Purposive sampling under the non-probability approach becomes relevant in such situations, hence its usage in the present study.

Data Collection

To adequately deal with the subject, an online and hard copy questionnaire was designed backed with interview questions triangulation. The researchers also used a Likert scale to develop an online questionnaire which was administered through the various online platforms of the faculty. The instrument for the study was adopted from the JISC Learning Analytics Readiness (JISC-LAR) and customized to align with the core objectives of the study. The data gathering was based primarily on four major thematic areas inspired by the UTAUT model for the constructs. These include ethical and policy considerations (facilitation conditions), stakeholder engagement and data ownership (social influence), learning analytics acceptance and implementation (performance expectancy), and students’ readiness. Given that the questionnaire was customized using the JISC-LAR, it became necessary to follow an appropriate methodology to test the reliability of the data gathered before the analysis. Following this, a pilot study was conducted among a few HND students to test and validate the reliability of the questionnaire prior to the actual research. Again, the data gathered for the main study was tested to ensure good internal consistency. In this case, using the Cronbach alpha feature of the Statistical Package for the Social Sciences (SPSS) is deemed an appropriate method to ascertain data reliability based on value interpretations, hence its adoption in the current study. It is established that the results values ranging from 0.70 to 0.9 are deemed appropriate coefficients to rely on (Taber, 2018). Values below the given range come with reservations and are regarded as unreliable. The test conducted in the current study yielded good internal consistency with Cronbach’s alpha values more than the required value of α ≥ .70. Table 1 shows the reliability results of the constructs. From Table 1, Facilitation Condition, Social Influence, Performance Expectancy, and Students’ Readiness all indeed meet the requirement as far as the reliability test with the SPSS software is concerned.

Table 1.

Construct Reliability and Validity.

Construct	Cronbach’s alpha (α > .7)
Students’ readiness (SR)	.831
Performance expectancy (PE)	.715
Social influence (SI)	.739
Facilitation condition (FC)	.846

Data Analysis

Descriptive statistics plays a major role in the study to summarize the responses of participants. The SPSS software was used to describe the frequencies, percentages mean, and standard deviation of the various responses of participants. Again, a regression analysis was conducted with the same software to test the hypothesis set for the study.

Results

Gender Distribution

The researchers are of the view that everybody in the target group should be given a fair chance to give his/her response. Moreover, Table 2 gives a fair representation of the ratio of teachers (14.9%) and students (85.1%) in the faculty. The data in Table 2 shows that greater part of female responded to the questionnaire than male (74.1% and 25.9%). These findings are in agreement and reflects in other related studies/reports done in the classroom, which also indicate that the greater percentage of the population in the faculty comprised of females as compared to male.

Table 2.

Gender Distribution of Respondents.

Gender	Counts	% of Total	Cumulative %
Male	57	25.9	25.9
Female	163	74.1	100.0

Students View on Culture and Practices Relating to Data-Driven Decision-Making by Faculty/Institution Management

The results as indicated in Table 3 represent 29.0% of the respondents who think that “to some extent” the faculty uses data to make decisions in the department. The highest respondents were 37.6% who responded “Not sure” to the question. This shows that the use of data in decision-making is not altogether absent in the faculty, however, the significant figures show that there is a need for improvement in this area.

Table 3.

Institutional Senior Management Team Use Data to Make Decisions.

Levels	Counts	% of Total	Cumulative %
Not sure	83	37.6	37.6
Hardly	6	2.7	40.3
To some extent	64	29.0	69.2
To a great extent	15	6.8	76.0
Not at all	53	24.0	100.0

Willingness to Explore the Potentials of Learning Analytics

A question was posed on the faculty’s encouragement to explore the benefit of LA. The pie chart shows that 38.5% responded that they are “not sure” if senior members encourage the faculty/institution to investigate the potential of learning Analytics. On the other hand, 24.4% (“to some extent”) and 15.4% (to a great extent) responded positively to the question. Figure 2 shows the results of the willingness to explore the potential of learning analytics.

Figure 2.

Faculty encouragement to explore the benefit of LA.

Performance Indicators to Be Improved With the Use of Learning Analytics?

According to Table 4, the highest percentage figure were those who responded “To some extent” 79 (35.7%) of any performance indicators that need improvement with the use of learning analytics. The second significant frequency was 69 representing 31% and this was the response of those who responded “not sure.” Alfred (Personal communication, 2019) in an interview stated that;

I know the behavior patterns of students in my small class but in the large classes it is almost impossible and if there is a technology that can help in this area it will be better.

Table 4.

Response to Performance Indicators That Needs Improvement.

Levels	Counts	% of Total	Cumulative %
Not sure	69	31.2	31.2
To some extent	79	35.7	67.0
To a great extent	28	12.7	79.6
Hardly	17	7.7	87.3
Not at all	28	12.7	100.0

In a related research with 37 teaching/support staff, 58% of the respondents also said in their opinion, (“to some extent”) there are some key performance indicators that needs improvement with the use of learning analytics.

Understanding of Student Success and Performance

Table 5 indicates that 76.9% as against 23.1% of the respondents responded that there is a shared understanding of what student success means in the Faculty/institution. Student success refers to a condition where students are equipped for success in their own personal, civic, and professional lives, and demonstrate the values and competencies that make their institution distinctive. Although, institutions’ or faculties may differ in their definition of success, for the sake of student’s success measurement, it is expected that these are made clear and shared among stakeholders and students in order to fulfill the missions and goals of the school. Student success does not only refer to strong retention and graduation rates in a school, but also high-quality skill acquisition (WASC Senior College and University Commission, 2013). In the case of this study, which is focused on creative fashion design processes, all the preparation is channeled to the core objectives of the course toward achieving the goals and aspirations of the faculty.

Table 5.

Understanding of What Student Success Means in the Faculty/Institution.

Levels	Counts	% of Total	Cumulative %
No	51	23.1	23.1
Yes	170	76.9	100.0

Staff Willingness to Accept Learning Analytics

The study also sought to determine respondents’ views on staff willingness to accept teaching and Learning Analytics, provided it benefits learners and impacts on their (teaching staff) role/workload. According to Table 6, the highest figure was 76 (34.5%) which represents respondents who responded that “to some extent” staff are willing to accept teaching and learning Analytics. Also, 72 representing 32.7% responded “Not sure” and 42 representing 19.1% responded “To a great extent.” The figures of the two variables in favor of the question (i.e., 34.5% + 19.1% = 53.6%) indicated that more than 50% of the respondents were of the view that the staff is willing to accept Teaching and Learning Analytics, provided it benefits learners and impact on their (teaching staff) role/workload.

Table 6.

Staff Willingness to Accept Learning Analytics.

Levels	Counts	% of Total	Cumulative %
Not sure	72	32.7	32.7
To some extent	76	34.5	67.3
Not at all	11	5.0	72.3
To a great extent	42	19.1	91.4
Hardly	19	8.6	100.0

An interview with the dean of the faculty of Creative Arts and Technology revealed that there are plans to organize more workshop to further resource Teaching staff on how to incorporate online assessment and content delivery (Nyarko, personal communication, 2020). This move was also greatly motivated during the start of the COVID-19 pandemic, when the school almost came to a standstill with students struggling to assess contents from remote areas.

Ethical Issues, Legal and Policies on Data Use

Ethical Issues

Table 7 shows that 114 (52.1%) out of the 219 responded “Not Sure” while 28 (12.8%) responded, “To some extent.” Also, 49 (22.4%) responded “Not at all” and 20 (9.1%) responded “Hardly.” The researchers consulted some key members (3) of the ICT directorate and they pointed out that, there have been some discussions on ethical issues in the school which include violation of human rights and data use, violation in data use, and tone of language. These, however, were not regarded as issues that could seriously prevent LA in the school.

Table 7.

Ethical Issues on Usage of Data.

Levels	Counts	% of Total	Cumulative %
Not at all	49	22.4	22.4
Not sure	114	52.1	74.4
Hardly	20	9.1	83.6
To some extent	28	12.8	96.3
To a great extent	8	3.7	100.0

Legal Issues Around Learning Analytics Data

The results of a follow-up question to ethical issues are shown in the bar chart (Figure 3) where 47.5% of the respondents responded “Not Sure,” 22.4% responded “To some extent” and 8% responded “To a great extent” and 16.9% responded “Not at all.” For the purposes of in-depth information on legal issues the researchers also sought to ask the opinion of lecturers and teaching support staff and the results showed a similar trend (40% “To some extent” and “Not Sure,” 48.8%). Upon further consultation with some key ICT directorate officers, it was revealed that there are data protection laws and legal policies on the use of data. They also pointed out that the Ghana Data Protection Act 843 seeks to establish a Data Protection Commission, to protect the privacy of the entity and personal data by regulating the handling of individual information, to provide the processes to divulge personal information and other matters. They also assured that the institution is in the process of reviewing policies to regulate data usage.

Figure 3.

Legal issues on data usage.

Existing Policies and Processes on Data Used for Learning Analytics

A question was posed on existing policies regarding data usage. The results in Table 8 shows that 108 (49.3%) responded “Not sure” and 55 (25.1%) responded “To some extent.” These were the two highest significant value of responses and the rest were “Hardly” 15(6.8%), “Not at all” 23 (10.5%), and “To a great extent” 18 (8.2%). An interview with three key ICT directorate officers of the University revealed that with the level of data collection and usage in the institution for now, it is adequate enough. However, they admitted that when the data and its complexity increase, there will be a need for improvement.

Table 8.

Existing Policies Regarding Data Usage.

Levels	Counts	% of Total	Cumulative %
Not sure	108	49.3	49.3
To some extent	55	25.1	74.4
Not at all	23	10.5	84.9
To a great extent	18	8.2	93.2
Hardly	15	6.8	100.0

Future Plans for Learning Analytics Project

The study sought the opinion of respondents on whether there has been any engagement by stakeholders on future plans for learning analytics projects. The results, as shown in Table 9 were “Yes” 115 (52.3%) and “No” 105 (47.7%). As mentioned earlier, learning analytics in the field of education is a novel system and is now attracting attention. According to Niall (2017) in a UK report, the future work plan in the area of Learning Analytics Exploration will cover more in-depth case studies, learning analytics approach adopted by some named Universities and a comparative study of best practice implementation strategies around the globe. The key members of the ICT directorate who were consulted affirmed the benefits and prospect of learning analytics and stated that it is a critical factor for the growth of the institution as the complexity of the educational environment keep increasing.

Table 9.

Respondents’ Opinion on Future Plans for Learning Analytics Project.

Levels	Counts	% of Total	Cumulative %
No	105	47.7	47.7
Yes	115	52.3	100.0

Teaching Staff Availability and Capacity

The study also sought the opinion of respondents on whether teaching/support Staff will have available time when LA is integrated into the system. They were to consider previous and current events to make their assessment. The result shows that 138 (62.4%) responded “Yes” and 83 (37.6%) responded “No.” The adverse effect of the COVID-19 pandemic greatly increased the number of trainings on the use of online technologies for teaching and learning and for that matter, staff capacity and willingness to adhere to such training greatly increased. Again, most of the staff since then have availed themselves and always eager to learn new technologies that enhance teaching and learning. This is a guarantee that staff can take interventions with students when LA is rolled out on a large scale in the institution.

According to the NEA (2008) report, there is a growing encouragement among teachers with the use of educational technology enhancement tools. However, with the ever-emerging and continuous technological changes in the educational system, teachers need to stay up-to-date with their technological proficiency. The fact is, even if stakeholders choose to replace all teachers with computer-literate facilitators in faculties and classrooms, new and emerging technologies in the future will require further training to keep their skills up-to-date with current trends.

Data Sources and Ownership

The researchers sought to know the lecturer’s/teaching staff’s views on the issue of data sources and ownership in the institution. Table 10 shows that 121 (55%) responded “Not sure,” 36 (16.4%) responded “To some extent,” and 15 (6.8%) responded “To a great extent.” The rest were 20.4, distributed among the other variables. The researchers then consulted some key members of the ICT directorate and interviewed them about this issue. They responded that ownership of the various data sources in the institution is made clear to all staff and thus, they belong to the school. The ICT directorate in turn also plays as the custodians of the data.

Table 10.

Respondent’s Views on Data Sources and Ownership.

Levels	Counts	% of Total	Cumulative %
Not sure	121	55.0	55.0
To some extent	36	16.4	71.4
Not at all	30	13.6	85.0
Hardly	18	8.2	93.2
To a great extent	15	6.8	100.0

Hypothesis Testing

A multiple linear regression analysis was conducted to test the three hypotheses set for the study. With the SPSS software, the analysis was conducted at a 95% confidence intervals with the predictor variables (Facilitation conditions, social influence, and performance expectancy) and the dependent variable (students’ readiness). The results show a good model fit with F(3, 217) = 1,938.160, p < .001, Adj. R ² = .96, and R ² = .96. From the results, it is observed that facilitation conditions significantly and positively influence the students’ readiness to accept LA (β = .96, t = 66.41, and p < .001), hence hypothesis 1 (H1) was accepted. Also, the results found no statistically significant relationship between social influence and the student’s readiness to accept TA (β = .006, t = 0.395, and p = .693). The hypothesis 2 (H2) was therefore rejected following the results obtained. Lastly, hypothesis 3 (H3) was accepted with the results (β = .045, t = 2.95, and p = .004), which show a statistically significant and positive influence of performance expectancy on the student’s readiness to adopt LA, although found to be moderate. The results of the regression analysis are shown in Table 11.

Table 11.

Results of the Regression Analysis.

Model	Unstandardized coefficients		Standardized coefficients	T	Sig.	95% confidence interval for B		Hypothesis decision
Model	B	Std. error	Beta	T	Sig.	Lower bound	Upper bound	Hypothesis decision
(Constant)	0.038	0.046		0.822	.412	−0.053	0.128
Facilitation condition	0.944	0.014	.96	66.41	<.001	0.916	0.972	H1 supported
Social influence	0.005	0.012	.006	0.395	.693	−0.019	0.029	H2 not supported
Performance expectancy	0.040	0.014	.045	2.948	.004	0.013	0.067	H3 supported

Note. Dependent Variable-Students’ readiness.

Discussion

The results of the willingness to explore the potential of learning analytics by the faculty indicate a moderate agreement. In relation to this, Poortman and Schildkamp (2016) and Fanelli et al. (2023) confirm that institutions around the world are gradually paying attention to data-based decision making and it is because there are high expectations to make high-quality decisions, based not only on experience and instinct, but also on data. A lot of researchers have demonstrated that the use of data can help to improve student’s achievement as well as sustainable development goals (Alkaabi et al., 2023; Bachmann et al., 2022; Campbell & Levin, 2009; Carlson et al., 2011; M. K. Lai et al., 2009; McNaughton et al., 2012). Teachers can use available data, in the form of assessment data and classroom activity and observation to understand the learning needs of their students, and adapt pedagogical strategies accordingly to improve student’s acquisition of skills (Poortman & Schildkamp, 2016). Also, on the issue of the staff’s willingness to accept LA, the results indicate that more than 50% of the respondents agreed with the staff’s readiness. Research has proven that instructors benefit from Learning Analytics a lot because it has the capability of analyzing various data including students’ test scores, demographics, and students’ psychographics (Bousbia & Belamri 2014; Masiello et al., 2024). This is assumed to be the reason staff are willing to adopt LA.

Regarding ethical issues, key officers of the ICT directorate of the university were contacted to ascertain how ethical issues could hinder LA in the university with past experience. The responses of the officers revealed some a past issues, although handled with integrity and experience to ensure order in their operations. Key among them were: violation of human rights concerning data use, violation in system use, and proper channel of communication via online or tone of language. As affirmed by research, the increase in data and analytics comes with its own new challenges and questions of ethics and best practices of data collection and usage (Bormida, 2021; Mathies, 2018; Omoyiola, 2023). In connection with this, Ekowo and Palmer (2017) proposed three factors for Higher Educational Institutions to consider and follow. These are (i) The Purpose of Data and Analytics (this includes the questions analysis or analytics intend to answer and the goals they aim to achieve). (ii) The Unintended Consequences of Data Use and Analytics (This includes discussions and identification of potential unintended consequences and steps to take to mitigate them). (iii) The Outcomes to Measure (An effective plan includes measurable outcomes hoped to achieve due to using data and analytics). These measures are recommended to ensure that the best practices in relation to ethics are followed, most especially, in the area of student identification as far as LA adoption is concerned.

The regression analysis conducted in the current study indicates the need to focus on ethical and policy issues and provide appropriate technology facilities (facilitation condition) to ensure the easy adoption of LA. This assertion to the fact that a strong positive relationship was observed between facilitation conditions and the readiness of students to accept LA. The results of the current study are consistent with previous studies (Camilleri & Camilleri, 2023; Miah et al., 2023) which found a significant influence of facilitation conditions on online learning adoption in educational institutions. Yuan et al. (2023) found that facilitation conditions are in fact, the biggest factor that affects online teaching and learning adoption just as observed in the current study. With the results obtained in the current study (β = .96, t = 66.41, and p < .001), it is indeed confirmed that facilitation conditions are the biggest factors that affect online learning among the other factors explored. This means that making LA technology tools available, organizing training programs, and implementing favorable policies for technology adoption just as Camilleri and Camilleri (2023) also advocate, will encourage KsTU students to engage in teaching and learning analytics in their studies.

The study again found no significant relationship between social influence and the student’s readiness to accept LA. Contrary to this finding, a recent study (Budhathoki et al., 2024) to understand ChatGPT and anxiety found a significant impact of social influence on technology adoption in higher education. The insignificant relationship observed in the current study suggests that the leadership body of KsTU does not necessarily need to focus on the issue of data ownership and stakeholder engagement that were used in the current study to measure social influence, in the quest to win the students’ interest in adopting LA. Thus, irrespective of how the students see their peers and leaders engaging in online learning, the unavailability of resources (facilitation conditions) observed to be a key factor in the present study, would discourage the students from online learning. Also, the moderately significant influence of performance expectancy observed on the student’s readiness means that the students’ belief in the academic improvement they will gain by using LA does not strongly convince them to adopt it. A previous study (Miah et al., 2023), however, found no significant influence of performance expectancy on online learning adoption. This notwithstanding, other studies (Biloš & Budimir, 2024; Dahri et al., 2023; Kurniasari et al., 2023; Milicevic et al., 2024) observed a very significant effect of performance expectancy on technology adoption. It could be said that a little emphasis need to be placed on performance expectancy by the leadership of KsTU in the quest to instill LA in the students, as far as this study is concerned.

Conclusion

Research into best-managed technological resources like Teaching and learning analytics is growing very fast and will continue to revolutionize the educational environment due to the benefits that it brings to the development of academic progress. The current study was conducted to understand the readiness of the Faculty of Creative Arts and Technology at KsTU to adopt LA. Responses from students and staff indicate the willingness to adopt with conditions that the necessary measures are put in place. The descriptive statistics revealed that the preparedness of the institution for analytics implementation is satisfactory at its foundation and will need to be upgraded to forestore future eventualities. Nevertheless, the study has been able to establish that the fundamental parameters needed for the implantation of Teaching and learning analytics like; human recourse, culture, interest, ethics and policies can be guaranteed. With the UTAUT model as a theoretical background, the study further probed to make predictions on the readiness of the students to adopt LA. With predictor variables (Facilitation condition, Social Influence, and Performance expectancy), a regression analysis was conducted to predict the readiness of the students. The analysis revealed a significant and positive influence of facilitation conditions and performance expectancy on the student’s readiness. However, there was no significant effect of social influence on the student’s readiness. It is recommended that the needed resources for online learning should be provided in addition to the setting and implementation of appropriate policies to guide students in LA adoption.

Implications and Suggestions

The results obtained on the readiness of the staff and the students to adopt LA calls on the leadership of KsTU to sensitive its members on the benefits of technology adoption, especially LA. This must go parallel with the provision of appropriate resources to help both staff and students build their literacy level in online teaching and learning activities. The findings that facilitation condition is a very significant factor necessitates this action suggested. Also, as Ghana hopes to fully explore Sustainable Development Goal 4, which seeks to ensure quality education globally by 2030, the government needs to provide all tertiary educational institutions in Ghana with the requisite conditions or resources to adopt LA for quality online teaching and learning. This will positively impact the knowledge and application of LA in all Ghanaian tertiary institutions as revealed in the current study with KsTU as a case. The study also advocates for the frequent organization of professional development programs by leadership in all Ghanaian tertiary educational institutions for facilitators to constantly upgrade their knowledge in ICT for better exploration of online facilitation tools.

A moderate influence of performance expectancy on students’ readiness also implies that management does not need to focus more on such a factor in the quest to build its members for online teaching and learning. Nonetheless, organizing training programs as previously stated to equip members with skills and competencies in online activities would go a long way to prepare them for LA adoption. The insignificant effect of social influence on student’s readiness as observed in the current study also implies that data ownership and stakeholder engagement does not have any effect on the student’s readiness. A focus should then be placed on provision of resources for the learners rather than data ownership.

Moreover, the researchers support the suggestion made by Hansen and Wasson (2016) that the regular use of computer competence (or computer literacy) for some time now, should be extended to cover data literacy and use. This is meant to prepare educators for new kinds of data and information available for teaching and learning. The choice of technology tools or applications should be contextualized to generate the needed data for goals to be achieved. There should be a comprehensive education on how the data and its interpretations can be used in pedagogical strategies to improve teaching and learning (Hansen & Wasson, 2016). Ethical use of data and policies regarding data usage must be improved to guide future occurrences of misuse.

Limitations

The current study was conducted using the staff and students of the Faculty of Creative Arts and Technology only, hence a sample size of 221. Future studies could be extended to various faculties within the university and explored with large sample sizes to understand the wider view of the situation and how it could be addressed to benefit the university community. Also, other methodologies to predict the moderation of other factors such as effort expectancy on students’ readiness could be explored as the current study focuses on the influence of only three factors (facilitation conditions, social influence, and performance expectancy) on the students’ readiness as far as the UTAUT framework is concerned. Again, it could be said that as the current study was conducted to understand the readiness of Ghanaian tertiary students to embrace learning analytics, relying only on data gathered in KsTU somewhat limits the application of the results in decision-making regarding all tertiary schools in Ghana. Future research could include samples from all tertiary institutions in Ghana to obtain all-inclusive results for concrete decision-making. This will go a long way to effectively impact the education system of Ghana and not only KsTU for quality education in the country.

Footnotes

The authors would like to thank all participants for their support to see this research conducted successfully.

ORCID iDs

Emmanuel Amos

Harry Barton Essel

George Kwame Fobiri

Akwasi Adomako Boakye

Yaw Boateng Ampadu

Ethical Considerations

Prior to the research,participants were informed about the purpose,procedures and benefits of the study. They were assured that participation in the research is purely voluntary and data gathered from them will be kept strictly confidential.

Consent to Participate

Participants confirmed their understanding,gave informed consent,and were made aware that they could withdraw at any time without consequence. Participants’ personal information was therefore handled confidentially.

Funding

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

Declaration of Conflicting Interests

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

Data Availability Statement

Data used for the current study are available at

References

Aguilar

S. J.

(2018). Learning analytics: At the nexus of big data, digital innovation, and social justice in education. TechTrends, 62, 37–45.

Ahmed

R. R.

Streimikiene

Streimikis

Khouri

(2024). Mobile learning using extended UTAUT model during COVID-19: Evidence from developed countries. Economic Research-Ekonomska Istraživanja, 37(1), Article 2300389.

Alduais

Al-Smadi

M. O.

(2022). Intention to use E-payments from the perspective of the unified theory of acceptance and use of technology (UTAUT): Evidence from Yemen. Economies, 10(10), Article 259.

Alghazi

S. S.

Kamsin

Almaiah

M. A.

Wong

S. Y.

Shuib

(2021). For sustainable application of mobile learning: An extended UTAUT model to examine the effect of technical factors on the usage of mobile devices as a learning tool. Sustainability, 13(4), Article 1856.

Alkaabi

Mehmood

Bhatacharyya

Aldhaheri

(2023). Sustainable development goals from theory to practice using spatial data infrastructure: A case study of UAEU undergraduate students. Sustainability, 15(16), Article 12394.

Alzahrani

Tsai

Iqbal

Marcos

Scheffel

Drachsler

Kloos

C. D.

Aljohani

Gasevic

(2023). Untangling connections between challenges in the adoption of learning analytics in higher education. Education and Information Technologies, 28(4), 4563–4595.

Amakyi

(2013). Readiness for data-driven decision making in education in Ghana. Journal of Education and Practice, 4(8), 134–139.

Ara

S. S.

Tanuja

Manjula

Venugopal

(2023). A comprehensive survey on usage of learning analytics for enhancing learner’s performance in learning portals. Journal of Educational Technology Systems, 52(2), 245–273.

Asswad

Gómez

J. M.

(2021). Data ownership: A survey. Information, 12(11), Article 465.

10.

Bachmann

Tripathi

Brunner

Jodlbauer

(2022). The contribution of data-driven technologies in achieving the sustainable development goals. Sustainability, 14(5), Article 2497.

11.

Barsky

(2019). Technology in field education: Managing ethical issues. Journal of Technology in Human Services, 37(2–3), 241–254.

12.

Bayaga

du Plessis

(2024). Ramifications of the Unified Theory of Acceptance and Use of Technology (UTAUT) among developing countries’ higher education staffs. Education and Information Technologies, 29, 9689–9714.

13.

Bice

Tang

(2022). Teachers’ beliefs and practices of technology integration at a school for students with dyslexia: A mixed methods study. Education and Information Technologies, 27, 10179–10205.

14.

Biloš

Budimir

(2024). Understanding the adoption dynamics of ChatGPT among Generation Z: Insights from a modified UTAUT2 model. Journal of Theoretical and Applied Electronic Commerce Research, 19(2), 863–879.

15.

Bormida

M. D.

(2021). The big data world: Benefits, threats and ethical challenges. In Iphofen

R. A.

(Ed.), Ethical issues in covert, security and surveillance research (pp. 71–91). Emerald Publishing Limited.

16.

Bousbia

Belamri

(2014). Which contribution does EDM provide to computer-based learning environments? In Pena-Ayala

(Ed.), Educational data mining: Applications and trends (pp. 3–28). Springer.

17.

Brandsma

Stoffers

Schrijver

(2020). Advanced technology use by care professionals. International Journal of Environmental Research and Public Health, 17(3), Article 742.

18.

Budhathoki

Zirar

Njoya

E. T.

Timsina

(2024). ChatGPT adoption and anxiety: A cross-country analysis utilising the unified theory of acceptance and use of technology (UTAUT). Studies in Higher Education, 49(5), 831–846.

19.

Camilleri

M. A.

Camilleri

A. C.

(2023). Learning from anywhere, anytime: Utilitarian motivations and facilitating conditions for mobile learning. Technology, Knowledge and Learning, 28, 1687–1705.

20.

Campbell

Levin

(2009). Using data to support educational improvement. Educational Assessment, Evaluation and Accountability, 21, 47–65.

21.

Cao

Feng

Lim

Kodama

Zhang

(2024). How social influence promotes the adoption of mobile health among young adults in China: A systematic analysis of trust, health consciousness, and user experience. Behavioral Science, 14(6), Article 498.

22.

Carlson

Borman

G. D.

Robinson

(2011). A multistate district-level cluster randomized trial of the impact of data-driven reform on reading and mathematics achievement. Educational Evaluation and Policy Analysis, 33, 378–398.

23.

Cioc

M. M.

Popa

Ş. C.

Olariu

A. A.

Popa

C. F.

Nica

C.-B.

(2023). Behavioral intentions to use energy efficiency smart solutions under the impact of social influence: An extended TAM approach. Applied Sciences, 13(18), Article 10241.

24.

Cortellazzo

Bruni

Zampieri

(2019). The role of leadership in a digitalized world: A review. Frontiers in Psychology, 10, Article 1938.

25.

Dahri

Al-Rahmi

Almogren

Yahaya

Vighio

Al-maatuok

Al-Rahmi

A. M.

Al-Adwan

(2023). Acceptance of mobile learning technology by teachers: Influencing mobile self-efficacy and 21st-century skills-based training. Sustainability, 15(11), Article 8514.

26.

Dhirani

L. L.

Mukhtiar

Chowdhry

B. S.

Newe

(2023). Ethical dilemmas and privacy issues in emerging technologies: A review. Sensors, 23(3), Article 1151.

27.

Dis

E. A.

Bollen

Zuidema

Rooij

R. V.

Bockting

C. L.

(2023). ChatGPT: Five priorities for research. Nature, 614(7947), 224–226.

28.

Dramani

I. E.

Tang

Coffie

C. P.

(2022). Usage intention of e-Learning systems in Ghanaian tertiary institutions: A case study of the university for development studies. Sustainability, 14(12), Article 7360.

29.

Educause. (2013). 7 things you should know about calibrated peer review. Retrieved January 1, 2021, from https://library.educause.edu/resources/2013/10/7-things-you-should-know-about-calibrated-peer-review

30.

Ekowo

Palmer

(2017). Predictive analytics in higher education: Five guiding practices for ethical use.

31.

Elberkawi

Aljawarneh

Rashaideh

Alharbi

(2022). The COVID-19 pandemic and E-learning: Challenges and opportunities from the perspective of students and instructors. Journal of Computing in Higher Education, 34, 21–38.

32.

Elias

(2011). Learning analytics: Definitions, processes and potential, 2011. Citado, 4, 27–28.

33.

Eshun

(2012). Developing innovative assessment strategy to foster creativity development in graphic design studio [Unpublished thesis, KNUST-Dspace].

34.

Fadler

Legner

(2022). Data ownership revisited: Clarifying data accountabilities in times of big data and analytics. Journal of Business Analytics, 5(1), 123–139.

35.

Fanelli

Pratici

Salvatore

F. P.

Donelli

C. C.

Zangrandi

(2023). Big data analysis for decision-making processes: Challenges and opportunities for the management of health-care organizations. Management Research Review, 46(3), 369–389.

36.

Ferguson

(2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5–6), 304–317.

37.

Grassini

(2023). Shaping the future of education: Exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences, 13(7), Article 692.

38.

Gülbahar

(2008). Improving the Technology Integration Skills of Prospective Teachers through Practice: A Case Study. Turkish Online Journal of Educational Technology-TOJET, 7(4), 71–81.

39.

Haleem

Javaid

Qadri

M. A.

Suman

(2022). Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers, 3, 275–285.

40.

Hani

A. B.

Hijazein

Hadadin

Jarkas

A. K.

Al-Tamimi

Amarin

Shatarat

Abeeleh

M. A.

Al-Taher

(2021). E-Learning during COVID-19 pandemic; Turning a crisis into opportunity: A cross-sectional study at The University of Jordan. Annals of Medicine and Surgery, 70, Article 102882.

41.

Hansen

C. J.

Wasson

(2016). Teacher inquiry into student learning: The TISL heart model and method for use in teachers’ professional development. Nordic Journal of Digital Literacy, 11(1), 24–49.

42.

Haynes

Harris

Schall

M. C.

Liu

Davis

(2024). Gauging the technology acceptance of manufacturing employees: A new measure for pre-implementation. Sustainability, 16(12), Article 4969.

43.

Hernández-de-Menéndez

Morales-Menendez

Escobar

C. A.

Mendoza

R. A.

(2022). Learning analytics: State of the art. International Journal on Interactive Design and Manufacturing, 16, 1209–1230.

44.

Holstein

Doroudi

(2019). Fairness and equity in learning analytics systems (FairLAK) [Conference session]. Companion Proceedings of the Ninth International Learning Analytics & Knowledge Conference, Tempe, AZ, United States, March 4 to March 8, 2019.

45.

Hornsby

D. J.

Osman

(2014). Massification in higher education: Large classes and student learning. Higher Education, 67, 711–719. https://doi.org/10.1007/s10734-014-9733-1

46.

Hosseini

Rasmussen

L. M.

Resnik

D. B.

(2024). Using AI to write scholarly publications. Accountability in Research, 31(7), 715–723.

47.

Ifenthaler

(2017). Are higher education institutions prepared for learning analytics? TechTrends, 61(4), 366–371.

48.

Islam

M. R.

Sarker

Sultana

Faisal-E-Alam

Castanho

R. A.

Meyer

(2023). Understanding the COVID-19 pandemic’s impact on E-Learner satisfaction at the tertiary level. Sustainability, 15(8), Article 6694.

49.

Jafar

Dollah

Mittal

Idris

Kim

Abdullah

Joko

E. P.

Tejuddin

D. N. A.

Sakke

Zakaria

N. S.

Mapa

M. T.

Vun Hung

(2023). Readiness and challenges of e-learning during the COVID-19 pandemic era: A space analysis in Peninsular Malaysia. International Journal of Environmental Research and Public Health, 20(2), Article 905.

50.

Kisselburgh

Beever

(2022). The ethics of privacy in research and design: Principles, practices, and potential. In Knijnenburg

B. P.

(Ed.), Modern socio-technical perspectives on privacy (pp. 395–426). Springer.

51.

Kurniasari

Lestari

Tannady

(2023). Pursuing long-term business performance: Investigating the effects of financial and technological factors on digital adoption to leverage SME performance and business sustainability—Evidence from Indonesian SMEs in the traditional market. Sustainability, 15(16), Article 12668.

52.

Lai

C. Y.

Cheung

K. Y.

Chan

C. S.

Law

K. K.

(2024). Integrating the adapted UTAUT model with moral obligation, trust and perceived risk to predict ChatGPT adoption for assessment support: A survey with students. Computers and Education: Artificial Intelligence, 6, Article 100246.

53.

Lai

M. K.

McNaughton

Timperley

Hsiao

(2009). Sustaining continued acceleration in reading comprehension achievement following an intervention. Educational Assessment, Evaluation and Accountability, 21, 81–100.

54.

Lathabhavan

Kuppusamy

(2024). Examining the role of digital leadership and organisational resilience on the performance of SMEs during the COVID-19 pandemic. International Journal of Productivity and Performance Management, 73(8), 2365–2384.

55.

Liu

(2023). Data-informed decision-making in higher education. In Yang

H. X.

(Ed.), The rise of Chinese American leaders in U.S. higher education: Stories and roadmaps (pp. 159–169). Springer.

56.

Maccaro

Stokes

Statham

Williams

Pecchia

Piaggio

(2024). Clearing the fog: A scoping literature review on the ethical issues surrounding artificial intelligence-based medical devices. Journal of Personalized Medicine, 14(5), Article 443.

57.

Mala

A. F.

Dar

S. A.

(2024). Ethics in information technology: A practical guide. Technometrics, 66(1), 132–133.

58.

Márquez

Henríquez

Chevreux

Scheihing

Guerra

(2024). Adoption of learning analytics in higher education institutions: A systematic literature review. British Journal of Educational Technology, 55(2), 439–459.

59.

Masiello

Mohseni

Palma

Nordmark

Augustsson

Rundquist

(2024). A current overview of the use of learning analytics dashboards. Education Sciences, 14(1), Article 82.

60.

Mathies

(2018). The ethical use of data. New Directions for Institutional Research, 2018(178), 85–97.

61.

Mazzullo

Bulut

Wongvorachan

Tan

(2023). Learning analytics in the era of large language models. Analytics, 2(4), 877–898.

62.

McNaughton

Lai

M. K.

Hsiao

(2012) Testing the effectiveness of an intervention model based on data use: A replication series across clusters of schools. School Effectiveness and School Improvement, 23(2), 203–228. https://doi.org/10.1080/09243453.2011.652126

63.

McNicol

T. C.

(2024). Improving ethical usage of corporate data in higher education: Enhanced Enterprise Data Ethics Framework. Information Technology & People, 37(6), 2247–2278.

64.

Mhlongo

Mbatha

Ramatsetse

Dlamini

(2023). Challenges, opportunities, and prospects of adopting and using smart digital technologies in learning environments: An iterative review. Heliyon, 9(6), Article e16348.

65.

Miah

M. S.

Singh

J. S.

Rahman

M. A.

(2023). Factors influencing technology adoption in online learning among private university students in Bangladesh post COVID-19 pandemic. Sustainability, 15(4), Article 3543.

66.

Michel-Villarreal

Vilalta-Perdomo

Salinas-Navarro

D. E.

Thierry-Aguilera

Gerardou

F. S.

(2023). Challenges and opportunities of generative AI for higher education as explained by ChatGPT. Education Sciences, 13(9), Article 856.

67.

Milicevic

Kalas

Djokic

Malcic

Djokic

(2024). Students’ intention toward artificial intelligence in the context of digital transformation. Sustainability, 16(9), Article 3554.

68.

Moon

Lee

Choi

G. W.

Seo

Lim

(2024). Learning analytics in seamless learning environments: A systematic review. Interactive Learning Environments, 32(7), 3208–3225.

69.

Nagy

Dringó-Horváth

(2024). Factors influencing university teachers’ technological integration. Educational Sciences, 14(1), Article 55.

70.

National Education Association. (2008). Technology in schools: The ongoing challenge of access, adequacy, and equity (Policy Brief No. 19). NEA Policy and Practice Department.

71.

Nguyen

Tuunanen

Gardner

Sheridan

(2023). Design principles for learning analytics information systems in higher education. European Journal of Information Systems, 30(5), 541–568.

72.

Niall. (2017). JICS strategic approaches to learning analytics in UK higher education. Accessed December, 2021, from http://creativecommons.org/licenses/by/3.0/

73.

Omoyiola

B. O.

(2023). The social implications, risks, challenges and opportunities of big data. Emerald Open Research, 1(4), 1–8.

74.

Paolucci

Vancini

Bex

R. T.

Cavanaugh

Salama

Araujo

Z. D.

(2024). A review of learning analytics opportunities and challenges for K-12 education. Heliyon, 10(4), Article e25767.

75.

Peña-Ayala

(2017). Learning analytics: Fundaments, applications, and trends. A view of the current state of the art to enhance e-learning (pp. 25–63). Springer.

76.

Poortman

C. L.

Schildkamp

(2016). Solving student achievement problems with data use intervention for teachers. Teaching and Teacher Education, 60, 425–433. https://doi.org/10.1016/j.tate.2016.06.010

77.

Prinsloo

Slade

(2017). Big data, higher education and learning analytics: Beyond justice, towards an ethics of care. In Kei Daniel

(Ed.), Big data and learning analytics in higher education (pp. 109–124). Springer.

78.

Qadhi

S. M.

Alduais

Chaaban

Khraisheh

(2024). Generative AI, research ethics, and higher education research: Insights from a scientometric analysis. Information, 15(6), Article 325.

79.

Rahman

S. F.

Yunus

M. M.

Hashim

(2021). Applying UTAUT in predicting ESL lecturers intention to use flipped learning. Sustainability, 13(15), Article 8571.

80.

Reinholz

D. L.

Stone-Johnstone

Shah

(2019). Walking the walk: Using classroom analytics to support instructors to address implicit bias in teaching. International Journal for Academic Development, 25(3), 259–272.

81.

Scheuren

(2004). What is a survey. https://books.google.com.gh/books/about/What_is_a_Survey.html?id=CPqsjgEACAAJ&redir_esc=y

82.

Schindler

L. A.

Burkholder

G. J.

Morad

O. A.

Marsh

(2017). Computer-based technology and student engagement: A critical review of the literature. International Journal of Educational Technology in Higher Education, 14, Article 25.

83.

Sergis

Sampson

D. G.

(2017). Teaching and learning analytics to support teacher inquiry: A systematic literature review. In Peña-Ayala

(Ed.), Learning analytics: Fundaments, applications, and trends (pp. 25–63). Springer.

84.

Shahriar

S. H.

Arafat

Islam

Nur

J. E.

Rahman

Khan

S. I.

Alam

M. S.

(2023). The emergence of e-learning and online-based training during the COVID-19 crisis: An exploratory investigation from Bangladesh. Management Matters, 20(1), 1–15.

85.

Sharif

Atif

(2024). The evolving classroom: How learning analytics is shaping the future of education and feedback mechanisms. Education Sciences, 14(2), Article 176.

86.

Siemens

Gasevic

(2012). Guest editorial-learning and knowledge analytics. Journal of Educational Technology & Society, 15(3), 1–2.

87.

Sliwka

Klopsch

Beigel

Tung

(2024). Transformational leadership for deeper learning: Shaping innovative school practices for enhanced learning. Journal of Educational Administration, 62(1), 103–121.

88.

Stahl

B. C.

Timmermans

Flick

(2017). Ethics of emerging information and communication technologies: On the implementation of responsible research and innovation. Science and Public Policy, 44(3), 369–381.

89.

Taber

(2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48, 1273–1296.

90.

Tahat

D. N.

Habes

Tahat

Pasha

S. A.

Attar

R. W.

Al-Rahmi

W. M.

Alblehai

(2023). Technology enhanced learning in undergraduate level education: A case study of students of mass communication. Sustainability, 15(21), Article 15280.

91.

Tan

S. Y.

Taeihagh

Pande

(2023). Data sharing in disruptive technologies: Lessons from adoption of autonomous systems in Singapore. Policy Design and Practice, 6(1), 57–78.

92.

Tanantong

Wongras

(2024). A UTAUT-based framework for analyzing users’ intention to adopt artificial intelligence in human resource recruitment: A case study of Thailand. Systems, 12(1), Article 28.

93.

Tsai

Rates

Moreno-Marcos

Munoz-Merino

Jivet

Scheffel

Drachsler

Kloos

C. D.

Gašević

(2020). Learning analytics in European higher education—Trends and barriers. Computers & Education, 155, Article 103933.

94.

Tzimas

D. E.

Demetriadis

S. N.

(2024). Impact of learning analytics guidance on student self-regulated learning skills, performance, and satisfaction: A mixed methods study. Educational Sciences, 14(1), Article 92.

95.

Vahdat

Ghio

Oneto

Anguita

Funk

Rauterberg

(2015, April 22–24). Advances in learning analytics and educational data mining [Conference session]. 23rd European symposium on artificial neural networks, computational intelligence and machine learning, ESANN 2015 – Proceedings, Bruges, Belgium.

96.

Venkatesh

Morris

M. G.

Davis

G. B.

Davis

F. D.

(2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

97.

Venkatesh

Thong

J. Y.

(2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.

98.

Wang

Y.-K.

Chen

C.-Y.

(2023). Integrating mobile devices and wearable technology for optimal sleep conditions. Applied Sciences, 13(17), Article 9921.

99.

WASC Senior College and University Commission. (2013). Students achievement. Retrieved August, 2021, from https://www.msmu.edu/academics/provost/assessment/wasc-senior-college-and-university-commission/

100.

White

Larusson

J. A.

(2014). Identifying points for pedagogical intervention based on student writing: Two case studies for the “Point of Originality”. In Larusson

White

(Eds.), Learning analytics: From research to practice (pp. 157–190). Springer.

101.

Xie

Boudouaia

AL-Qadri

A. H.

Khattala

Aung

Y. M.

(2023). A study on teachers’ continuance intention to use technology in English instruction in western China junior secondary schools. Sustainability, 15(5), Article 4307.

102.

Yuan

Liu

Deng

Ding

Wijaya

T. T.

(2023). Facilitating conditions as the biggest factor influencing elementary school teachers’ usage behavior of dynamic mathematics software in China. Mathematics, 11(6), Article 1536.