Adoption of Technology to Support Teaching and Learning in a Distance Learning Programme at Africa Nazarene University

Adoption of Technology to Support Teaching and Learning in a Distance Learning Programme at Africa Nazarene University

¹Dr Mary Atieno Ooko., ²Dr Wanyera Samuel

¹University of Pretoria of South Africa

²Otieno Jaramogi Oginga Odinga University of Science and Technology

DOI: https://dx.doi.org/10.47772/IJRISS.2025.908000270

Received: 06 August 2025; Accepted: 16 August 2025; Published: 06 September 2025

ABSTRACT

The current increasingly changing world shows the influence and effects of technology in all aspects of learning. In developed Western countries, the Higher Education institutions believe that these developments offer valuable opportunities for improved learning as a result of technological advancements and innovations in the learning environment. This has in turn placed the responsibility on developing countries, in order to strive better competitively in international markets, even under tremendous pressure, to similarly embed suitable blends of technologies within their own learning and curriculum approaches, and consequently enhance and improve new learning opportunities. The positive increasing growth in access to and use of technology has caused more approaches to be developed in e-learning and is manifested in different forms. This has supplemented or replaced the traditional methods in learning, enabling engagement of learners with their learning through various web technologies alongside face-to-face delivery, and sometimes completely replacing direct face-to-face contact. However, the success of use of technology in learning depends, to a significant extent, on how the students actually use them for learning purposes.

The purpose of this study isto examine the extent that technology is accepted, adopted and used to enhance learning and teaching in a distance education context .The study employed an extended version of the Technology Acceptance Model (TAM) in its investigation of the underlying factors that affect students’ use of technological systems in learning. It explored students’ perception and experiences of using technology for learning and teaching to guide theInstitute for Open Distance Learning (IODL) in Africa Nazarene University (ANU) to develop strategies for implementation of technology-enhanced learning. This study revealed that students’ attitudes and perceptions on the use of technology in learning and teaching were diverse and were both positive and negative. While positive attitudes and perceptions of users to adopt Technology in learning and teaching can simplify their understanding and use of the technology in learning and teaching, negative attitudes would instead complicate this making adoption difficult. A deeper focus on the factors that affect adoption and technology use in e-learning as well as their associations is a pre-cursor to a better knowledge and understanding of student acceptance of eLearning technological systems.

INTRODUCTION

The contemporary trend that has seen the ever-increasing demand for university education, overstretching the few residential facilities and the need for advanced learning, has led to the emergence of Open and Distance Learning (ODL) in most higher education institutions in Kenya. This transition is highly influenced by the swift development of information and communication technologies (ICTs) all over the world. In recent years, many of these institutions have invested extensively in their technological infrastructure. This has drawn attention towards a greater use of the technological infrastructure for education purposes. Effective integration of technology in learning and teaching involves the interaction of the knowledge areas of technology, better methods of teaching, and adding to the content on the part of the teacher (Pierson 2010: 24). Paradigm shifts that have occurred in the field of offering instruction through technology has shown a growing emphasis on curriculum integration of technology (Margrave & Hsu 2010: 15). Massey (2009: 78) states that “it is the promise and anticipation of what technology can do in the future that is now affecting attitudes and ideas about how we can teach and learn”.

The magnitude of new technologies introduced over the last ten years or so has also impacted tremendously on Open and Distance Learning practices (Weumin & Dhanarajan 2006). Bollag and Overland (2001), assert that many educational institutions are answering the challenge of increased enrolment and lack of physical space, by developing distance learning programs. The acquisition of quality higher education through technology within distance education has found remarkable levels of praise from various scholars. Moore et.al (1990) argue that through the integration of technology in distance learning, quality education has been made accessible at very low cost to people who are engaged in other activities of daily living that are likely to bar them from attending schooling on a regular basis (Bollag & Overland 2001).

Kenya has witnessed an unprecedented expansion of distance learning programmes to cater for the great number of people determined to enhance their skills and positions in the work place while still desirous of working and supporting their families. Technology has been hailed in the context of distance education as a variable that maximizes the use of limited physical and human resources and facilities used in these accepted institutions (Ayot 2005).

The increased trend towards use of distance education among conventional higher education institutions has been fuelled by two major factors: institutions are seeking to enrol non-resident learners; and the increasing need by adult learners to seek and acquire qualifications while overcoming the constraints inherent in conventional education (MOEST 2006). The Africa Nazarene University (ANU) heeded this need by establishing The Institute of Open and Distance Learning (IODL). The Institute was founded in 2008 to deal specifically with distance education, targeting students in Kenya and beyond. The Institute was created to provide learning opportunities for those aspiring to study at university level but who are unable to commit their time to study through the conventional mode of study; to provide alternative and innovative education which is not limited by space and time; to provide opportunities for people to learn at their own pace, and to provide the much needed manpower for development (Athoye2013).

This does not in itself guarantee the use of the technology to support learning and teaching without challenges. It appears that technology has not brought about the widespread changes in teaching methodologies that was hoped for. The successful implementation of the use of technology in learning and teaching is a complex process, attributed to pedagogical values, attitudes, curriculum needs and physical infrastructure (Granger et al. 2002). The way in which students are taught and what they are taught, requires adjustments to and around technology (Watson 2003). However, most distance learning programs in Africa are seemingly failing due to lack of technological pedagogical knowledge among students and lecturers. Although students and lecturers acknowledge the value of technology, difficulties continue to be experienced in adopting and using technology for learning and teaching. Balanskat et al. (2006) and Mueller et al. (2008) argue that, although many students are becoming aware of technology in general, they still may not be ready or capable to use it.

A substantial body of research asserts that students have difficulty in using technology because of various obstacles or barriers (Balanskat et al. 2006 and Becta 2004). These difficulties and barriers, such as lack of computer skills, time and accessibility of technological devices, if not addressed, could result in profound impacts on the use of technology to support learning and teaching in main stream educational systems and open educational systems. At Africa Nazarene University there have been many e-leaning platforms that have been implemented but which have not lasted the test of time because there has never been a formal framework for adoption of new technologies.

Despite the ubiquity of technologies for learning and teaching in education today, evidence supporting their use is said to be anecdotal especially in developing countries. Particularly, very little research has been done on use of technologies to support learning in ODL and how instructors and trainers have influenced student learning in Kenya.

Many researchers have cited lack of theoretically grounded and extensively done research as a key challenge to be addressed (Alavi & Leidner 2001; Piccoli, Ahmad & Ives 2001).

The successful implementation of the use of technology in learning and teaching is a complex process, determined by teaching values, attitudes, curriculum needs and physical infrastructure consequently impacting on the rate of its adoption (Granger et al. 2002). A substantial body of research asserts that obstacles such us lack of electricity, computer skills, network configuration and accessibility to technological devices have a profound impact on the use of technology to support learning and teaching (Balanskat et al 2006); Becta (2004; Nchunge et al 2013). Most distance learning programs in Africa seemingly fail due to lack of technological pedagogical knowledge among both students and lecturers.

The infrastructural challenges facing developing countries in relation to the developed world such as lack of connections in the rural areas, frequent power interruptions, the high initial cost of technology for learning and teaching in universities has not in itself guaranteed the adoption and use of technology to support learning and teaching in higher learning institutions. Although students acknowledge the value of technology, various higher learning institutions including ANU found that their students are not adopting and using technology optimally for improved learning and teaching.

The way in which students are taught, instructional methods used, competence of their lecturers and what they are taught directly impacts on their attitude and perception towards the use of technology in learning and teaching, thus calling for constant adjustments to and around technology (Watson 2003). It may also be true that other factors such as gender, age, background in computing technology, individual intention and effort to play a role in the students’ adoption of technology may be impacting on adoption of technology in learning and teaching in Africa Nazarene University. This may lead to the assertion made earlier that most distance learning programs in Africa are seemingly failing due to lack of technological pedagogical knowledge among students and lecturers.

Although students acknowledge the value of technology, various higher learning institutions such as Africa Nazarene University still experience difficulties in adopting and using technology for learning and teaching.

This is partly because E-leaning platforms that have been implemented do not last to the test of time because of lack of a formal framework for adoption of new technologies among other possible factors that this study unveils and hence inadequate support and guidance for both students and lecturers in making the transition from contact to e-learning. Therefore this study determines how students at a distance education institution in Kenya perceive the use of ICTs to support learning and teaching in distance education and establishes the extent and use of ICTs in their learning.

Research Design

While one can adopt various technological strategies and use different tools to support the learning environment, deployment of technologies in distance education is known to result in more effective and efficient practices within institutions. Research indicates that effective use of technologies in distance education promotes learner centeredness (Mudasiru 2006). This can improve the efficiency and effectiveness of learning processes and outcomes. According to Masizana et al. (2008), universities are increasingly introducing learning platforms that enable learners to access course materials and communicate among themselves. This, it is said, improves communication and collaboration between students and saves time as students are capable of engaging in learning opportunities outside the face-to-face context (Cole 2005). Lecturers in distance education can effectively integrate technology into learning and teaching activities in a systematic way which is important for transforming pedagogical activities. Lecturers who have adopted technologies in teaching can support distance learners more effectively. Online learning provides students with better accessibility to learning material. Similarly students can share their concerns or passion of the subjects with their peers.

Research studies indicate that distance learning is equally or even more effective than traditional instructional methods when the appropriate technologies are used in the instructional tasks; when there is student-to-student interaction and when there is timely lecturer-to-student feedback (Moore & Thompson 1990; Verduin & Clark 1991).

This aspect of accessibility helps students to continue learning irrespective of their professional obligations and on top of saving their time, while cutting down their financial expenses. In addition, courses offered in distance learning are usually cheaper than regular learning (Singh & Means 2000).

It is for the above reasons that this study seeks to investigate the adoption of technology to support learning and teaching in a distance learning program at Africa Nazarene University as experienced by students when engaged in learning and teaching. The concept of distance learning has been frequently debated in developing countries in the recent past. Most developing countries previously offered distance learning correspondence courses where printed learning materials used to be dispatched to the students at regular intervals. The basic philosophy was that teachers would be separated physically from their students but could still conduct the teaching process from a distance. With the development of the computing sector of the technological industry and internet networks during the recent decades, things have changed and global communication has reached greater levels (Sagarmay 2011). With these great developments, great opportunities have come to the surface to impart learning efficiently and interactively.

Use of technology in media and internet connections have changed the whole philosophy of learning and distance learning and provided us with the opportunity for close interaction between learners and their teachers with better enhanced standard of learning facilities in comparison to the traditional facilities; which were often limited only to the printed media. This has led to creation of virtual classrooms where teachers and students are scattered all over the world. This developing countries are faced with an up-hill task of purchasing these facilities due to the higher costs, placing developed countries in a better position to take advantage to impart better learning facilities to those students in the developing world. This has resulted in an increasing degree of cross-border higher education provision offered by institutions in the developed economies. Due to low internet connectivity and computerization in the developing countries, e-learning remains greatly challenged, however. (Sagarmay 2011).

This aspect of accessibility helps students to continue learning irrespective of their professional obligations. This not only saves time but also cuts down on financial expenses for students (for example through savings on accommodation, travel and materials costs). Moreover, most courses offered as part of distance learning method are cheaper than their regular counterpart according to Singh and Means (2000).

Therefore this study sought to find out how technology adoption can be harnessed to support students who are studying at a distance. The online method of delivery does not only save time and money often associated with travelling costs to places of learning but it also offers flexibility in learning schedules. Distance learning programs typically attract mature students and many of the courses offered are skills- based. This makes it easier for the student to relate the content with day to day work and own experiences.

The Kenya Institute of Open Learning (KIOL) is an example of a leading model distance learning institution founded on a theme of reaching everyone who aspires to learn. Its core pillars include flexibility, accessibility and affordability while maintaining quality (Kiol, 2013).

Emerging trends in technology have shown greater signs of curbing the barriers that have for a long time restricted access to higher education. The use of computer applications and proper learning strategies, together with more expansion on the content delivery, increase the effectiveness of the existing academic programmes. Through emerging trends in communication technology, the effectiveness of computer-delivered coursework can be improved at the same time developing access to scientific and technical information (Anyona, 2009).

Some of the renowned open universities in the world include the UK Open University, Korea National Open University, Anadolu University’s Open Educational Faculty in Turkey, and the Open University of Japan which all began as institutions of this second generation. When these institutions started operating, they selected broadcast media, television and radio, as a mode of instruction (Anyona, 2009) that enabled them to reach a mass audience, and supported their mission to expand educational opportunities for many students who were unable to attend full time study (Bates, 2005 and Peters, 1994). Unlike the previous technologies that were used to deliver content, the internet has made it possible to enable interaction as well as provision of more varied content. These new technologies have made it possible to provide interactivity between learner and content, as in CD-ROM and web-based materials provision while maintaining interactivity between lecturers and students through email and/or online forums. This method facilitates content personalization to match learning preferences, according to Bates (2005).

Over the years, distance education has been involved in using technology to deliver content and to improve interaction between the students and the lecturer. Taylor (2001) suggested five distance education generations which started with the Correspondence model which was based on traditional technology, in this case, print; the second model was technology on media model which used print, audio, and video. The third model was referred to as the Telelearning model which used telecommunications that provided fast communication and the fourth, Flexible learning model, was based on Internet delivery. The last was referred to as an Intelligent, flexible learning model which focused on the interactivity of the Internet.  

METHODOLOGY

Theoretical And Conceptualframeworks

Introduction

This chapter aims to look at different models that focus on the adoption of technology use. These models are used to provide a framework that guides the research design, implementation and analysis and interpretation of results. According to Eisenhardt (1989) there are three distinct uses of theory: as an initial guide to research design and data collection; as part of an interactive process of data collection and analysis; and as a final product of the research. Since ICT adoption mainly employs positivist approaches, such theories and models have been used at the beginning stage of the research in order to guide the research and interpret its results (Punch 2005). The chapter includes adoption theories and models, Limitations of Previous Theories and Research Findings and a chapter summary.

For the purpose of understanding this study, it is important to understand that although this study is based on the Technology Acceptance Model (TAM), it also discusses other theories and models that demonstrate willingness or desire of a user group to employ Information Technology for the tasks it is designed to support.

There are several user acceptance theories and models such as: Innovation Diffusion Theory (Adoptions of Innovation Model), Theory of Reasoned Action, Technology, Technology Acceptance Theory and Theory of Transactional Distance (Baraghani 2007: 19). Although this study touches on all of these models and theories, it is grounded on the Technology Acceptance Model (TAM).

There exist quite a number of theories and models employed in studying individuals’ ICT adoption and post-adoption behaviours. Social psychologists have applied theories and models that havebeen mainly used in this strand of research(Youngseek and Crowston 2011).

These theories and models focus on people’s intention to engage in a certain behaviour as a major theoretical foundation. Both Theory of Reasoned Action (TRA) and Theory of Planned Behaviour (TPB) have been widely used in ICT adoption and use research.

As two of the major intention-based theories they provide the basic theoretical backgrounds for other adoption theories including the Technology Acceptance Model (TAM) and enhanced TAM (Zolait & Hussein 2014: 3).

The basic assumption of TRA and TPB is that people consciously determine whether they engage in or do not engage in certain behaviour. In this sense, the adoption and use intentions are usually conceptualized as a major outcome variable that is influenced by various independent variables. The purpose of this chapter is to review the major adoption theories including TRA and TPB and their applied theories, Innovation Diffusion Theory, and Social Cognitive Theory.

Adoptions of Innovation Model

The adoption process model was first introduced by Rogers in 1962 based on the fact that an individual goes through a series of steps which are: knowledge, persuasion, decision, implementation, confirmation. Rogers defines diffusion as a process by which an innovation is communicated through certain channels over time among the members of a social system. Rogers (2003: 36) argues that Adoption is similar to diffusion except that it deals with the psychological processes an individual goes through, rather than an aggregate market process.

According to Baraghani (2007: 19), diffusion is the process through which a new idea or new product is accepted by the market. Consequently, according to Rogers (2003) diffusion research focuses on five elements: the characteristics of an innovation which may influence its adoption; the decision-making process that occurs when individuals consider adopting a new idea, product or practice; the characteristics of individuals that make them likely to adopt an innovation; the consequences for individuals and society of adopting an innovation; and communication channels used in the adoption process (Baraghani 2007 and Rogers 2003).

Owing to the relatively new nature of technology use in higher education, some scholars contend that its essential to use the Adoptions of Innovation model to explain the interactions between the environment and the strategic choices organizations make via strategies to control the resource dependence condition (Rogers 2003: 34).

Choosing to adopt a particular innovation has both positive and negative outcomes for individuals or organizations. Rogers (2003: 32) states that this is an area that needs further research because of the biased positive attitude that is associated with the adoption of a new innovation.

The Innovation Adoption Curve Diagram is shown in Figure one below:   –

Figure 3.1: Rogers Adoption and Innovation Curve

Source: www.valuebasedmanagement.net/methods rogers innovation adoption curve.h

Rogers’ diffusion of technological innovation model suggests that large numbers of faculty are quite slow in adopting technological innovation in their teaching. It identifies five categories of technological innovation adopters.

Ronkowski (2000: 25) identifies two main sub-groups as “mainstream” faculty and “larggards”. Assuming eventual 100% adoption of technology, 16% are likely to be “laggards” who are highly suspicious of the innovation, prefer traditional approaches, and will adopt only if they can be certain it will not fail (Ronkowski 2000: 26). In this study, this model is used to establish the level of adoption amongst students. Analysis of this theory however shows it as a more general theory that looks at grouping or categories at the various levels of adoption of technology. This scarcely explains why each individual falls under the various categories such as innovators, early adopters, early majority, late majority, and the laggards mentioned by the proponents. What actually makes the laggards to be slow in adopting new technologies?

Innovation Diffusion Theory

Following the development of a model, Rogers developed an innovation diffusion theory which posits that for any idea, practice, or object that is perceived as new by an individual or other unit of adoption to be communicated through certain channels over time among the members of a social system, there must be four elements of the innovation itself: communication channels for that innovation, time, and the social system (context) which all determine its rate of adoption (Rogers 2003: 436/1995: 1-2). The theory adds that an innovation’s adoption rate is affected by relative advantage, compatibility, complexity, trialability and observability to those individuals within the social system. The more the participants of such an innovation create and share information with one another in order to reach a mutual understanding, the faster the adoption rate of the new innovation.

Rogers (1995: 1-2) and Hernandez, Jimenez and Martin (2010) add that because a communication channel is the means by which messages get from one individual to another, mass media channels are more effective in creating knowledge of innovations, whereas interpersonal channels are more effective in forming and changing attitudes toward a new idea, and thus in influencing the decision to adopt or reject a new idea. Most individuals evaluate an innovation, not on the basis of scientific research by experts, but through the subjective evaluations of near-peers who have adopted the innovation. Rogers (2003) argues that for a new innovation to be adopted, time has to elapse for the process ofinnovationdecision-makingwhich is the mental process through which individuals or institutions move from their initial knowledge about the innovation to forming an attitude towards the innovation, decision to adopt or reject, implementation of the innovation, and finally confirming the benefits of such innovation.

The Innovation Diffusion Theory (IDT) is concerned with the manner in which a new technological idea, artefact or techniques or a new use of an old technique, migrates from creation to use (Rogers 1995; 2003). In this theory technological innovation is communicated through particular channels, over time, among the members of a social system (Clerk 1999). The main goal of IDT is to understand the adoption of innovation in terms of four elements of diffusion including innovation, time, communication channels, and social systems (Clerk 1999). According to this theory, an individual’s behaviour in relation to adoption of technology is determined by his or her perceptions regarding the relative advantage, compatibility, complexity, trial ability, and observation ability of the innovation, as well as social norms (Rogers 2003: 436).

A number of studies have used the IDT as their theoretical framework such as Youngseek Kim and Kevin Crowston (2012) who, in their study on Technology adoption and use of theory review for studying scientists’ continued use of cyber-infrastructure, identified factors that might increase the likelihood of adoption. Another study by Surry, D.W. & Farquhar, J.D. (May 1997) in their study of Diffusion theory and instructional technology discusses how theories of innovation diffusion have been incorporated into instructional technology.

Information Systems scholars mentioned that in the context of end-user computing many of the classical diffusion assertions were valid (Ritu, Agarwal & Prasad 1997; Brancheau & Wetherbe 1990). The five main constructs of IDT were employed and found to have significant relationships with other factors in ICT adoption. Relative advantage was found to have a positive relationship with attitude (Agarwal & Prasad 2000), and relative usage intention (Lin, Chan & Wei 2006). Compatibility was found to influence Perceived Usefulness (Bhattacherjee & Hikmet 2007), PEOU (Hernandez, Jimenez & Martin 2010), attitude (Ritu Agarwal & Prasad, 2000; Lee, Kozar & Larsen 2003) and intention (Saeed & Muthitacharoen 2008; Wu & Wang 2005). Complexity was found to have a negative relationship with the technology adoption intention (Beatty, Shim & Jones 2001; Son & Benbasat ( 2007).

Moreover, innovation has been described as an idea, a product, a technology, or a program that is new to the adopting unit. The diffusion of innovation theory suggests that perceptions of technology characteristics, such as its relative advantage, compatibility, complexity, trialability, and observability impact the adoption of any new product. A number of researchers have applied Rogers’ theory in their studies, for instance Raisinghani and Schkade (1998) to explain the adoption of Internet, intranet, extranet technologies for electronic commerce applications, and Tan and Teo (2000) to describe factors influencing the adoption of internet banking in Singapore.

The Innovation Diffusion Theory (IDT) developed by Rogers (2003) has also been employed by students studying individuals’ technology adoption. While this theory can be seen to be bringing in the dimension of the reasons as to why individuals adopt technology, Scholars such as Damanpour (1996), Plsek and Greenhalgh (2001), Downs and Mohr (1976), and Lyytinen and Damsgaard (1998) argue that technologies are discrete packages developed by independent and neutral innovators, and technologies diffuse in a homogenous fixed social ether called a diffusion arena which is separate from the innovations locale.

The diffusion rate is a function of push and pull; it is difficult to quantify diffusion because humans and human networks are complex, Damanpour (1996), Plsek and Greenhalgh (2001), Downs and Mohr (1976), and Lyytinen and Damsgaard (1998). Measuring what exactly causes adoption of an innovation is extremely difficult, if not impossible. The same scholars also assert that diffusion theories can never account for all variables, and therefore might miss critical predictors of adoption and thevariety of variables which has led to inconsistent results in research and has consequently reduced its heuristic value. Green (2004) critiqued IDT by arguing that the diffusion of a practise depends on the decisive justifications used to rationalise.Compagni ,Mela and Ravasi (2000) stated that early experience with the implementation of an innovation influences later adoptions. These practices eventually trigger and support the isomorphic diffusion of the innovations even in the presence of persistent and certainty about its technical or economic benefits (Compagni ,Mela and Ravasi 2000).

RESEARCH METHODOLOGY

Quantitative Study

Introduction

The quantitative approach was used in the study for several reasons: First, data can be gathered from a large sample of students; second, it provides data that are precise and can be analysed statistically and are broadly comparable; and third, results can be generalised beyond the confines of the research location. The aim was to explain variations in Kenyan distance learners’ conceptions of learning and the quantitative findings were used in identifying the patterns students use in describing and defining their conceptions of learning. Quantitative research has also been used successfully in other studies that examined objective descriptive data and statistical explanations of patterns of behaviour in terms of some predefined factors (Corbetta 2003).

In quantitative research, it is necessary to make sure that the research process of both developing and testing the instrument is not only valid, but also reliable. Reliability is concerned with the consistency of the responses to the questions while validity refers to whether a study measures what it claims to measure. However, “reliability is not a measure of validity” warns Hosker (2002). According to Hosker (2002: 71), “It is possible to design a questionnaire that is reliable because the responses are consistent, but it may be invalid because it fails to measure the concept you think it is measuring”.

This chapter presents the quantitative study, the objectives of the quantitative study, target population, sample and sampling design, data collection instrument (development of survey questionnaire), pilot study, data collection procedure and data analysis.

The quantitative part of this thesis seeks to:

• To establish the level of adoption of technology amongst lecturers and students in an ODL environment.
• To examine how students are using technology in learning and teaching processes.
• To establish factors influencing implementation of technology to support learning and teaching in an ODL environment

Quantitative Study

To establish the extent to which the IODL uses technology for learning and teaching and to establish the extent of technological training/skills adoption at the IODL, data was collected using questionnaires. As Kombo and Tromp (2006) point out, an effective sample population should be diverse, representative, accessible and knowledgeable on the topic being investigated. Respondents were assured of confidentiality and anonymity when reporting the findings of the study. To spell out clearly the purpose of this study and full assurance of the confidentiality of the data collected, the questionnaire was accompanied with a covering letter.

Data was collected from 234 (39%) of 600 IODL students. After the administration of the questionnaires, the data collected was organized, collated, summarized, statistically treated and drafted in tables with the help of Statistical Package for Social Sciences (SPSS).

Data was analysed descriptively and inferentially with the help of Statistical Package for Social Sciences (SPSS) computer software version 22.0. This was analysed using means, frequencies and percentages and data was also analysed using the t- test, chi square, factor analysis and multiple regression analysis.

Statistical Package for Social Sciences is widely accepted and used by researchers in different disciplines, for data screening, coding and reliability tests. In addition, SPSS is widely applied in generation of descriptive statistics such as frequencies, percentages, mean values, and standard deviations.

These analyses were performed for each variable separately and appropriately to get a summary of the demographic profile of the respondents to obtain preliminary information and the feel of the data. Important summary statistics were then obtained and associations examined using factor analysis and the chi-square test. Significance level of 0.05 (that is, P< 0.05) was used to determine the significance of associations being examined.

Data Collection Instrument

Development of Survey Questionnaire

There are wide varieties of instruments designed to measure students’ experience of technology use. Two research instruments were developed and employed by the using original entries and adaptations from existing instrument items. The development of the questionnaire and focus group guide was based on upon the research questions for this work, the focus topic areas of technology adoption to support learning and teaching, relevant literature, and existing instruments.

The survey method used is titled Technology Adoption to Support Learning and teaching (TASTL). This questionnaire adapted from and contextualised from TAM was self-designed by the researcher after in-depth study of related research literature and it consisted of closed ended structural questions. The questionnaire selected various elements that were deemed essential, primarily from TAM while also incorporating other information from all the other theories and models that were perceived to have the possibility of filling the identified gaps in the study. Its design involved selection of major elements from TAM while also including some elements identified by other models and theories for comprehensive coverage of the problem under study. The researcher used this instrument because of its ability to solicit information from respondents within a short time as supported by Gupta (1999: 35).

The survey questionnaire was developed based on the study questions as well as the guidelines recommended for better response outcomes. The questionnaire was divided into 2 parts:

Part 1 containing biographic data related to user background and usage of the system in general.

Part 2 of the instrument consisted of 52 questions which were subdivided into three sections namely; Adoption Level where questions related to establishing the level of technology adoption amongst lecturers and students in an ODL environment will be stated.

The second section contained questions related to the actual usage of technology and they were aimed at examining how lecturers and students are using technology in learning and teaching processes. The last section was about attitudes and perceptions about using technology by lecturers and students. In this section, using 5-point Liker scales and closed-ended response, the survey questions formulated and then subjected to critical item-by-item scrutiny. The Likert response scale is: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree. A survey instrument entitled Technology Adoption to Support Learning and teaching (TASTL) Appendix (A) contains 60 questions that were aimed at getting information as needed to address this study’s research questions was finally designed.

The variables on quantitative research instrument, TASTL were broadly categorised into the independent variables and the dependent variables. The Independent variable (IV) is a variable that the researcher manipulates (i.e. changes) and is assumed to have a direct effect on the dependent variable while the Dependent variable (DV) is the variable the researcher measures, after making changes to the IV that are assumed to affect the DV. For this study, the learners’ attitudes and perceptions towards using technology in teaching and learning, access to computer were the independent variables while technology usage and technology adoption levels were the dependent variables.

Pre-testing the instrument

Prior to sending the questionnaire to a bigger sample, the questionnaire was tested in advance with a small group of people. Pre-testing refers to the preliminary assessment of a questionnaire with a group of respondents for the purpose of detecting problems in the questionnaire contents, wording, or layout, whether the respondents have any difficulty in understanding questions or whether there are any ambiguous or biased questions (Sekaran, 2003).

According to Baines and Chansarkar (2002), pre-testing is vital since it affects all aspects of the questionnaire design.Pre-test is essential for parts of questionnaire survey design. According to Sekaran (2009: 35), pre-tests must be conducted prior to the initial data collection phase or main survey in order to validate the instrument and to ensure that the survey questionnaire is free of errors and ambiguities. Therefore, one pre-test was conducted prior to using the survey questionnaire in the main study. The purpose of pre-testing is to avoid participants’ confusion and misinterpretations as well as to identify and detect any errors and ambiguities (Ogula & Onsongo 2009; Kombo & Tromp 2006).

In this research study, the pre-test was done by giving out questionnaires to students from the IODL who were not included in the actual study. Fifteen out of twenty questionnaire returns generated a very good response rate of (about 83%). The reason behind using these subjects was because all of them were from IODL and therefore were exactly like their colleagues who were sampled during the actual study. In addition to that, respondents were required to identify problems they thought would arise with the questionnaire design in order to obtain feedback for improving the survey questionnaire. Questionnaire pre-testing made it possible to obtain interesting comments from the respondents. The questionnaire was also reviewed by experts in ICT. This was to ensure that the questions asked concentrated on essential issues, the right questions with proper ingredients were be asked, and increased the reliability of answers and their consistency.

Other respondents highlighted some possible problems with wording and improper sequencing of the questionnaire design and identified some ambiguities. During this process, the wording was changed as required and ambiguous questions were either clarified or deleted. Consequently, the questionnaire was substantially revised according to suggestions of the respondents in the pre-test.The initial version of the questionnaire was developed from the previous literature (Ballester and Alemán, 2001; Cronin et al., 2000; Hoare and Butcher, 2008, Harris and Ezeh, 2008; Imrie et al., 2002) and refined through consultation with academics with experience in questionnaire design and scale development. Scale response categories were altered as comfortably felt by the respondents with five-point responses as opposed to the original seven-point responses. The final version of the questionnaire was evaluated in terms of instructions, ease of use, reading level, clarity, item wording.

Validity and Reliability in Quantitative Research

Quantitative research has various objectives including generalisation of the findings, and validity is used to examine the degree to which the outcomes of the study are generalizable or transferable (Bryman & Cramer 1990; Corbetta 2003). Validity is best examined through face validity which is usually achieved through examining the wording or structure of the constituent items or through examining the content of the instrument. Content validity concentrates on the test’s ability to include or represent all of the content of a particular construct that it is supposed to be measuring (Adcock & Collier 2001; Corbetta 2003).

It tests whether the items on the data represent the entire range of possible items the data should cover. Construct validity puts more focus on the degree to which a test measures the construct at which it is aimed (Bryman &Cramer 1990). It is done using factor analysis to examine whether the scale scores in the instrument define more global dimensions (Richardson 2004).

While reliability looks at the accuracy of the measuring instrument or the procedure used, validity is the degree to which it accurately reflects what the study set out to measure. Validity is vital for the test because it does not focus only on the statistic; it looks at the relationship between the test and the behaviour it intends to measure (Adcock & Collier 2001).

Convergent validity measures the same factors that are measured by other instruments, while discriminative validity describes the degree to which the measured observation differs from observations. This refers to the extent to which an instrument yields different scores on groups of participants who would be expected to differ in the underlying traits (Richardson 2004). Validity can be examined through using criterion validity which uses the correlation between the scores on an instrument and the scores obtained on some independent criterion. The criterion measures may be obtained at the very time the instrument is administered. This process is referred to as concurrent validity where the test scores accurately estimate an individual’s current state regarding the criterion. In criterion-related validity, the test has to demonstrate that it is effective in predicting indicators of a construct. Predictive validity has to do with criterion measures that are obtained at a time after the test (Richardson 2004).

Face and content validity of the instrument was ascertained by giving copies of the questionnaire to the supervisor and other experts from the College of Higher Degrees to examine the questionnaires to ensure face validity and the content to meet the VV specifications of Presser (2004).

Their comments and suggestions were used to revise the questionnaires before making the final one. The content validity refers to the representativeness of the item content domain: the manner in which the questionnaire and its items are built to ensure the reasonableness of the claims of content validity (Presser 2004; Sing 2007). Rigorous procedures were used to select the questionnaire constructs to form the initial items, personal interviews with experts, and the iterative procedures of scale purification imply that the instrument has strong content validity.

The construct validity can be demonstrated by validating the theory behind the instrument. Researchers have used various validation strategies to establish it, including item-to-total correlations, factor analysis, and assessment of convergent and discriminant validity, which demonstrates construct validity by showing that an instrument not only correlates with variables with which it should correlate, but also does not correlate with variables from which it should differ (Kombo & Tromp 2006).

Reliability

In order to understand whether the questions in the Technology Adoption to Support Learning and teaching (TASTL) questionnaire all reliably measure the same latent variables (adoption level, Technology Use and Perception about using technology) a Likert scale was constructed, and a Cronbach’s alpha was run on a sample size of 20 respondents. Cronbach’s alpha reliability coefficients were used to measure the internal consistency of each measure (Creswell 2009). So as to generate the general reliability of each of the latent constructs used in the model, Construct reliabilities were calculated by determining Cronbach’s Coefficient Alpha using the following Kunder- Richardson (K-R) 20 formulae;

 Where:

 = Reliability coefficient of internal consistency

 = Number of items used to measure the concept

 = Variance of all scores

 = Variance of individual items

A high coefficient implies that items correlate highly among themselves. This is sometimes referred to as homogeneity of data. A Cronbach’s alpha estimate value above 0.70 is generally considered as acceptable. According to Sekaran (2010: 56), if the value of Cronbach’s alpha reliabilities is less than 0.6, they are considered as poor, if the value is in 0.7 they are acceptable, and the reliabilities value above 0.8 are considered good. Therefore, the closer the Cronbach’s alpha gets to 1.0 the better is the reliability.

Table 5.1 Reliability Statistics

All of the measures used in the testing stage showed an adequate average reliability with Cronbach’s alpha value of 0.819. Group Cronbach alpha ranged between 0.951 and 0.706 that are considered to be good and acceptable except for two items, that is, one item from Technology Adoption (TAL14), and one from Attitude and Perception on the technology (APT3) constructs, which were later dropped in the final survey instrument. They were coded as Strongly Agree =5, Agree = 4, Undecided = 3, Disagree = 2 and Strongly Disagree = 1.

Questions under Technology adoption level among users were coded as “TAL”; hence they ranged from TAL1 to TAL13.

Items under the section on Use of Technology were coded as “UOF” and the ranged from UOT1 to UOT15. Finally, items under Attitude and Perception of students on technology were coded as “APT” and ranged between APT1 to APT26. The function of reliability is to examine whether the instrument measures a trait in the same way each time it is used under the same condition with the same subjects (Richardson 1990). A test is considered reliable if the same results are achieved repeatedly. Reliability of instruments could be estimated by examining the internal consistency; and grouping the items in a questionnaire that measure the same concept (Adcock & Collier 2001, Richardson 2004). The reliability of the instrument is estimated by looking at how well the items that reflect the same construct yield similar results.

Internal consistency reliability can be measured when a single measurement instrument is administered to a group of people on one occasion to estimate reliability (Bryman & Cramer 1990). This is measured by using Cronbach coefficient alpha which aims to estimate the internal consistency of an instrument by comparing the variance of the total scores with the variances of the scores of the constituent items (Richardson 1990; 2004).

Cronbach alpha tends to be higher when there is homogeneity of variances among items than when there are not. The higher the value the greater the indication that the item responses are collectively and empirically consistent with what it is measuring (Field 2000). Gliem & Gliem (2003) point out the following rules of thumb in estimating consistency: α>0.9 should be considered excellent; α>0.8 is good; α>0.7 is acceptable; α>0.6 is questionable; α>0.5 is poor and anything below 5 is unacceptable. When an alpha is 0.70, the standard error for measurement will be over half (0.55) standard deviation.

Although the high value for Cronbach’s alpha indicates good internal consistency, it does not mean that the scale is un dimensional (Gliem & Gliem 2003). Reliability can also be measured by using split-half reliability where all items that purport to measure the same construct are randomly divided into two sets. The entire instrument is administered and a correlation coefficient is calculated between the scores obtained on the two halves (Richardson 1990). The purpose is to check the extent to which the scores obtained on its individual items correlate with one another (Bryman & Cramer 1990; Adcock & Collier 2001).

Richardson (1990) and Adcock and Collier (2001), posit that the test-retest reliability is used to examine the replicability of the instrument It involves calculating the correlation coefficients between scores obtained by the same individuals on successive administrations. It is assumed that there is no change in the underlying condition between the scores of the two tests. The amount of time awarded between the administrations depend in part, by how much time elapses between the two measurement occasions. To avoid the problem of changes that may occur in the longer time gap, the administration should take place within a relatively short interval for the instrument to be reliable (Adcock and Collier 2001). “The correlation coefficient between scores obtained at the two administrations is more a measure of its stability than its reliability, and variability in the scores obtained on different occasions need not cast doubt on the adequacy (Adcock and Collier 2001).

CONCLUSION

The study revealed that prolonged use of technology would propagate the learner’s wish to use technology in learning and teaching. Most learners who possessed laptops were found to prefer accessing their learning materials through soft copy rather than hard copies. Facilitating conditions such as management ability, allocation of resources and institutional support emerged to be affecting the learners’ wish to adopt technology. In cases where the lecturers had low skills and knowledge in e-learning tools, the learners were impacted negatively lowering the adoption of technology in learning and teaching.

Technology infrastructure such as technology features, interactivity stability, convenience and design were found also to be a major determinant in technology adoption by distance learning students.

The study further revealed a high level of technology adoption among the students at ODL at Africa Nazarene University with varied uses of technology during the learning process. The application of technology in teaching and learning varied from Google Search, assignment submission, emailing, video conferencing among others. It is however noted that, students preferred use of technology due its convenience, timeliness, interactivity and its ease of use. A positive attitude towards the use of technology in teaching and learning propelled more of the users to adopt and prefer to use it in further learning process and even in their working environment. The study further confirmed that eLearning students are feeling confident about the future. This is more good news for the Kenya as country, or even the entire Sub-Saharan Africa because the combination of education and technology is clearly a powerful driver for growth. It further emphasized that the prospects for Kenyan education will depend increasingly on good communications and connectivity

There was high level of technology adoption among the IODL students than what the researcher had initially anticipated. The availability of effective resources impacted positively on the learners to adopt the use of technology in teaching and learning. Commitment from the lecturers and supporting staff to help in solving out the problems accelerated the adoption process, as learners were able to conveniently acquire and use e-learning resources without difficulties. The study, however, showed similar results as that on African e-Learning Journal Report (2012) that observed that teachers, lecturers, entrepreneurs and policymakers all have high expectations about the ability of new technologies to scaffold progressive change at both institutional and system-wide levels. The most significant constraint to eLearning at a national level cited by respondents is limited bandwidth. A lack of funds, limited electricity supply and insufficient human resource capacity were additional significant constraints. The government was identified as the most important change agent for accelerating ICT-enhanced learning.

The model adopted by the study, Technology Acceptance Model, showed that the Ease of Use and Perceived Ease of Use of technology are shown as the major aspects considered by users, especially in learning environment while adopting technology. The use mixed method approach methodology has further extensively covered the diverse aspects of the participants in e-learning.

The qualitative data gave a deeper insight on the students’ feelings, attitudes, perceptions and experiences while using technology in teaching and learning. The quantitative data further revealed the existing links among the variables indicating that could be exploited by the relevant organisations for a improvement on the existing systems

REFERENCES

1. Abu-Obaidah, A R, Bakar, R H, & Asmiran, S. 2012. Teachers’ ICT Skills and ICT Integration in the Classroom: The Case of Vocational and Technical Teachers in Malaysia. Creative Education 3:70–76.
2. Akhter, F., Buzzi, M. C., Buzzi, M., & Leporini, B. 2009. “Conceptual framework: How to engineer online trust for disabled users”. In P. Boldi, G. Vizzari, G. Pasi, & R. Baeza-Yates (Eds.), Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology Vol. 3, pp. 614–617 New York, NY: IEEE.
3. Al-alak, A B & Alnawas, I A M. 2009. Measuring the Acceptance and Adoption of ELearning by Academic Staff Knowledge Management & E-Learning: An International Journal, Vol.3 No. 2: 201.
4. Anyona, J K. 2009. The Status and Challenges of Open and Distance Learning in Kenya’s Public Universities. Unpublished Master’s Thesis Nairobi: Kenyatta University.
5. Armstrong, M. (2007).A Handbook of Human Resource Management Practice 8 Edition London: Kogan Page.
6. Athoye, G. O. 2013. African Nazarene University (ANU). URL: http://www.anu.ac.ke (accessed: 12.10.2013).
7. Ayot, H O & Briggs, H. 1992. Economics of Education. Nairobi: Education Research and Publication.
8. Backroad Connections Pty Ltd. 2005.Definition of key terms used in e-learning. Victoria: Australian National Training Authority.
9. Balanskat, A, Blamire, R & Kefala, I S. 2006. The ICT Impact Report.A Review of Studies of ICT Impact on Social Integration and Cultural Diversity. URL: http://www.lmi.ub.es/bridge-it/balanskat-blamire-r-i-kefala-s-2006-ict-impact-report-reviewstudies-ict-impact.html(accessed: 02.07.2013).
10. Bansal, G, Zahedi, F, & Gefen, D. 2008. “The moderating influence of privacy concern on the efficacy of privacy assurance mechanisms for building trust: A multiple-context investigation.” Proceedings of the Twenty-ninth International Conference on Information Systems (1528–1546) Atlanta, GA: Association for Information Systems.
11. Baraghani, S N. 2007.Factors Influencing the Adoption of Internet Banking.Master’s Thesis, Lulea University of Technology.
12. Bartholomew, D J, Steele, F, Galbraith, J & Moustaki, I. 2008. Analysis of Multivariate Social Science Data. Statistics in the Social and Behavioural Sciences Series (2nd ed.). Taylor & Francis.
13. Bates, A. 2005.Technology, E-Learning and Distance Education. London: Routledge.
14. Belawati, T & Zuhairi, A. 2007. The Practice of a Quality Assurance System in Open and Distance Learning: A Case Study of Universities Terbuka Indonesia (The Indonesia Open University). International Review of Research in Open and Distance Learning8 (1) 340-774.
15. Belawati, T. 2005. The Impact of Online Tutorials on Course Completion Rates and Student Achievement. Journal ofLearning, Media and Technology30 (1) 15-25.
16. Bollag, B & Overland M A. 2001. Developing Countries Turn to Distance Education. Chronicle of Higher Education 47 (40) 29-31.
17. Braun, V. and Clarke, V., 2006. Using thematic analysis in psychology. Qualitative research in psychology, 3(2), pp.77-101.
18. British Educational Communications and Technology Agency (BECTA) 2004.A Review of the Research Literature on Barriers to the Uptake of ICT by Teachers. CT Review Journals, URL: http://www.becta.org.uk (accessed 12.06.2014).
19. Carter, D S G. 1990. Knowledge Transmitter, Social Scientist or Reflective Thinker: Three Images of the Practitioner in Western Australian High Schools. Theory and Research in Social Education 28(3) 274-309.
20. Chau, P & Hu, P. 2002. Investigating Healthcare Professionals’ Decisions to accept Telemedicine Technology: An Empirical Test of Competing Theories.Information & Management39: 297-311.
21. Cohen, L, Manion, L & Morrison K. 2000.Research Methods in Education, 5th Edition. London: Routledge Falmer. Cole, J. 2005. Using Moodle: Using the Popular Open Source Course Management System URL: http://inewswire.com/pr40110.html (Accessed: 03.02.2014).
22. Costello, R & Welch, S A. 2014.“A qualitative analysis of faculty and student perceptions of effective online class communities using Herzberg’s motivator-Hygiene factors.” Quarterly Review of Distance Education, 15283518, 2014, Vol. 15, Issue 4
23. Creswell, J W. 2009. Research: Qualitative, quantitative and mixed methods applications. Los Angeles, CA: Sage.
24. Darlington, R B. 2004. Factor Analysis. URL: http://comp.psych.cornell.edu (Accessed: 10.01.2015).
25. Davis, et al., 1989. “Perceived Usefulness, Perceived Ease of use, and User Acceptance of Information Technology.” Journal of MIS Quart 13: 319–339.
26. Davis, F, Bagozzi, R & Warshaw, P. 1989. “User Acceptance of Computer Technology: A Comparison of Two Theoretical Models.” Journal ofManagement Science 35:8, 982-1003.
27. Davison, A J, Burgess, S and Tatnall, A. 2003.Internet technologies and business, Heidelberg, Vic: Data Publishing Pty Ltd.
28. Dela Peña-Bandalaria, M. 2007. Impact of ICTs on Open and Distance Learning in a Developing Country Setting: the Philippine Experience. International Review of Research in Open and Distance Learning 8(1). Available             from: http://www.irrodl.org/index.php/irrodl/article/view/334/792 (accessed on 13-08-2013).
29. Denzin & Lincoln, Y S. (eds). 1978. Handbook of Qualitative Research. London: Sage.
30. Dillon, A & Morris, M. 1996. User acceptance of information technology: Theories and
31. In: M. Williams (ed). Annual Review of Information Science and Technology Vol. Medford, NJ: Information Today.
32. Dillon, A and Morris, M G. 1996. “User Acceptance of Information Technology: Theories and Models,” in M. Williams (ed.) Annual Review of Information Science and Technology, Vol. 31, Medford NJ: Information Today, 3-32.
33. Dishaw, M T, Strong, D M and Bandy, D B. 2002. “Extending the Task-Technology Fit Model with Self-Efficacy Constructs,” Eighth Americas Conference on Information System, 1021-1027.
34. Donnellan, C 2002.The impact of the Internet, Independence. Eason, K D. 1991. “Ergonomics Perspective on Advances in Human-Computer Interaction”Ergonomics, vol.34 (6) 721-741.
35. Eben A, K & Achampong A, K. 2010. Modelling Computer Usage Intentions of Tertiary Students in a Developing Country through the Technology Acceptance Model
36. International Journal of Education and Development Using Information and Communication Technology (IJEDICT) Vol. 6, Issue 1: 102-116.
37. Fishbein, M & Ajzen, I. 1980.Understanding Attitudes and Predicting Social Behaviour. New Jersey: Prentice-Hall.
38. Garner, M, Wagner, C, & Kawulich, B. 2009. Teaching research methods in the social sciences. England: Ashgate Publishing Limited.
39. Goodhue, D L and Thompson, R L. 1995. “Task-Technology Fit and Individual Performance” MIS Quarterly, 19, 2, (June) 213-236.
40. Granger, C A, Morbey, M L, Lotherington, H, Owston, R D& Wideman, H H. 2002.Factors contributing to teachers’ successful implementation of IT.Journal of Computer Assisted Learning 18(4) 480 – 488.
41. Guba, E G, & Lincoln, Y S. 1994. “Competing Paradigms in Qualitative Research” In N K Denzin & Y S Lincoln (Eds.), Handbook of qualitative research 105-117 London: Routledge.
42. Guion, L A. 2002. Triangulation: Establishing the Validity of Qualitative Studies. Available from http://www.rayman- acchus.net/uploads/documents/Triangulation.pdf. (accessed on 10-05-2013).
43. Gursoy, H. 2011. A Critical Look at Distance Education in Turkey. In A. A. Carr-chellman (ed). Global Perspectives on E-Learning: Rhetoric and Reality London: Sage Publications 115-126.
44. Habing, B. (2003). Exploratory Factor Analysis. URL: http://www.stat.sc.edu (accessed: 10.01.2015).
45. Hadley, M & Sheingold, K. 1993.Commonalities and Distinctive Patterns in Teachers’ Integration of Computers.American Journal of Education 101: 261-315.
46. Holzweiss, P C, Joyner, S A, Fuller, M B, Henderson, S and Young, R. 2014.“Online graduate students’ perceptions of best learning experiences.”Distance Education, Vol. 35, No. 3, 311–323.
47. Hussain, I. 2007. A Study of Student’s Attitude towards Virtual Education in Pakistan. Turkish Journal of Distance Learning 8(2).Available from http://tojde.anadolu.edu.tr/. (accessed on 01-06-2013).
48. Hyper dictionary 2005. Internet: definition, Hyperdictionary.com Available from http://www.hyperdictionary.com/dictionary/Internet (accessed on 10-01-2015).
49. Internet World Stats 2006.Internet Usage Statistics-The Big Picture Internet World Stats, Available from http://www.internetworldstats.com/stats.htm (accessed on 10-01-2015).
50. Republic of Kenya (2005). Sessional Paper No. 1 of 2005 on A Policy Framework for Education, Training and Research. Nairobi: Government Printer.
51. Republic of Kenya. (2006). Education Sector Report on SNE in Kenya. Nairobi: Government Press.
52. Republic of Kenya, (2007). Kenya National Commission on Human Rights Occasional Report: Objects of Pity or Individuals with Rights: The Right to Education for Children with Special Disabilities. Nairobi. Government Printer.
53. Republic of Kenya. (2009). The Harmonized Draft Constitution of Kenya. Nairobi: Government Press.
54. Sharif, N., and Naz, F. (2002). The Study of Profile of Hearing Impaired Persons Portrayed by Print Media. University of the Punjab Med Thesis. Unpublished Report.
55. Tomasevski, K. (2003). Education Denied: Costs and Remedies, London: Zed Books.
56. UNESCO (2003) Overcoming Exclusion through Inclusive Approaches in Education: A Challenge and a Vision, Conceptual Paper, Paris: UNESCO.
57. UNESCO (2005). Guidelines for Inclusion: Ensuring Access to Education for All. Paris: UNESCO.
58. UNESCO (1994). The Salamanca Statement and Framework on Special Education Needs. Paris: UNESCO.
59. World Education Forum Report, (2000). Education for All – Meeting Our Collective Commitments: Unpublished Report.
60. Yates, D. S; Moore, D. S & Starnes, D. S. (2008). The Practice of Statistics, 3rd Ed. Freeman ISBN 79-0-71677309-2.