IDENTIFYING THE CRITICAL FUNCTIONALITIES OF eLEARNING SYSTEMS: RELATIONSHIPS BETWEEN TEACHERS PERSONAL VALUES AND eLEARNING SYSTEM FUNCTIONALITIES

This paper summarises the findings of a research project which applied the meansend chain methodology to analyse the relationship between the personal values of teachers and eLearning system functionalities. While eLearning is being promoted for implementation in learning and teaching settings, there is a lack of understanding of the critical functionalities of eLearning systems which teachers perceive are useful so that there is a likelihood that teachers will continue using eLearning systems. This paper reports that the most critical functionalities of an eLearning system for teachers can be categorized in terms of two dimensions – instruction presentation and student learning management. The instruction presentation dimension requirements include eSyllabus and electronic whiteboard, while the student learning management dimension requirements include online discussions, online roll call, threaded discussions, and assignment management. Therefore, this research indicates that where these critical functionalities are met, then teachers are more likely to develop a sense of accomplishment, self-fulfilment, fun and enjoyment, which will motivate them to continue using eLearning systems for teaching and learning. THE IMPORTANCE OF CRITICAL FUNCTIONALITIES OF eLEARNING SYSTEMS While eLearning is being promoted for implementation in learning and teaching settings, there is a lack of understanding of the critical functionalities of eLearning systems which teachers perceive are useful. This assertion starts from the premise that if teachers perceive functionalities of an eLearning system to be useful, then there is a likelihood that teachers will continue using eLearning systems. This paper provides a summary of research undertaken to identify where these critical functionalities are met and reports that the most critical functionalities of an eLearning system for teachers can be categorized in terms of two dimensions – instruction presentation and student learning management. Due to the improved connectivity in schools, eLearning is being adopted in many schools and education systems, resulting in teachers and their students engaging in eLearning environments. While this is relatively new areas for research, eLearning studies have already identified that usage of an eLearning system is determined largely by the users’ perceived usefulness of the system. Subsequently, the architecture and design of an eLearning system should be informed by the usefulness of the functionalities as perceived by its users. The challenge is that identifying the critical functionalities usually cannot be identified until after users engage and actually use the eLearning system. Studies (Chiu et al., 2005) suggest that a major factor for the continued usage of the system is the perceived usefulness formed through the actual usage experience. Evident in the relevant literature, three main approaches have been adopted to identify the critical functionalities of an eLearning system; namely 1. Traditional systems analysis and design methodology to identify and design instructor’s requirements that will fulfill the instructional goals. For example, Govindasamy (2002) favors this approach and believes that the instructor has five major requirements for an eLearning system including developing content, storing and managing content, packaging content, student support, and assessment; 2. Pedagogies as the basis of requirements to develop an eLearning environment suitable for instruction. For example, Mishra (2002) believes that constructivism is the most suitable theory for eLearning system design, suggesting an integrated framework to transform learning theories to basic instructional approach and online approach. In addition, Ainsworth and Fleming (2006), from an analysis of different pedagogies, develop authoring tools that allow teachers to create learning environments by customizing imported computer-based training domain content; and 3. Problem-oriented approach to identify requirements. This approach undertakes a process to list the problems that will be encountered in the teaching and learning process, which then informs systems design according to the solutions to those problems identified (Chou, 2003). Difficulties occur as a result of analyses of requirements cannot precede users’ usage experience. As Major (1995) indicates, irrespective of which approach listed above is adopted, the eLearning system often doesn’t meet teachers’ needs. Without evidence of the analysis of real usage experience, Govindasamy (2002) indicates that the eLearning system adopted is often the system with the most functionalities, with many of those functionalities not used at all, and incurring unnecessary costs. Moreover, some functionalities might be provided which are not easy to use, creating instructional difficulty and increasing the cognitive burden of the teachers. These unexpected negative effects that are difficult to foresee during the design phase have significant impact on teachers’ perceived value of the system and diminish their willingness to continue to use the eLearning system. MEANS-END CHAIN THEORY In contrast to those approaches, means-end chain theory can show the relationship among the attributes of an object, the consequences of using the object by an individual, and the personal values derived from these consequences. The means-end chain analysis has been successfully applied in product development and marketing (Wansink, 2003), and more recently in information systems development and user requirements gathering for web communities (Aschmoneit & Heitmann, 2003). For the purposes of this research, means-end chain analysis is applied with the objectives of examining teachers’ cognitive structure toward eLearning system usage, and illuminates the relationship between teachers’ perceived values of the functionalities of an eLearning system. This informs the identification of the critical functionalities of eLearning systems. Due to limitations of the length of this paper, a summary is presented of the means-end chain theory used in research to elicit the critical functionalities of a successful eLearning system for instructors, and an overview of the research methodology. Subsequently, key findings and discussion drawn from the analysis is presented, and practical implications are provided. The means-end chain model constructs a hierarchical value map (HVM) to systematically obtain information about individuals’ perception of an object under consideration by analysing the attributes of the object with consequences and values accrued to individuals (Gutman, 1982). The means-end chain model enables the categorisation of the attributes, consequences, and values. In the context of eLearning systems, attributes are functionalities provided by the system. Consequences are defined as any result accruing to the individuals after experiencing the object, including functional consequences and psychosocial consequences (Gutman, 1982). Functional consequences are the benefits of experiencing the object, while psychosocial consequences for individuals can be psychological or sociological in nature. For example, suitable eLearning software, in catering for the individual differences of learners, thereby ensuring the learning rights of learners. Values, including instrumental values and terminal values, refer to the psychological needs of individuals accomplishing important goals through the object. The instrumental value reflects an external orientation relating to how we are perceived by others (e.g., “makes me feel more important” or “makes me feel accepted”), whereas the terminal value is concerned with the desirable end-states of existence (e.g., happiness, security, and accomplishment). RESEARCH METHOD To identify critical functional requirements of successful eLearning systems by understanding the cognitive structure toward system usage and values of using the eLearning system of those teachers who have used such systems, we researched thirty-one teachers from southern Taiwan who have used eLearning systems in teaching, and who agreed to participate in our study. Table 1 shows the background of the teachers in this study. Details about the interview were emailed to the participants prior to the conduct of face-to-face, telephone, Skype, or MSN interviews, chosen on the basis of convenience for the interviewees. Table 1: Background of the Teachers (N=31) Characteristics Information Age Mean = 36 years old; Range = 25-54 years old Gender Male = 64% Female = 36% Years of teaching Mean = 9.4 years Frequency of offering online courses Once = 16% 2-3 times = 55% 4 or more times = 29% Method of Data Collection A ladder in laddering, a common analysis methodology of means-end chain to collect and analyse data, represents a linkage between the key perceptual elements across the range of attributes, consequences, and values of the interviewee. The subject might not form a ladder or might form many ladders. Laddering can be classified into soft laddering and hard laddering (Hofstede et al., 1998). Using one-to-one in-depth interviews, soft laddering explores the hidden values of the subject in order to find the factors affecting those values. Hard laddering relies on large samples from questionnaires, and performs validity analysis using statistical methods. Soft laddering was selected in this study, since we sought to gain indepth understanding of the values and perceptions of teachers who have used various functionalities of eLearning systems. To enable sufficient linkages, the collection of data from thirty-one teachers exceeded the required twenty respondents reported in the study of Reynolds et al. (2001). On average, five ladders were generated for each participant. With three elements in each ladder, this generated 465 data points, which also exceeding the required number of samples to generate useful linkages. The limitation of this approach is that skilful in-depth interview techniques were a very time-consuming process, with the average interview time, excluding the preamble, taking from forty to sixty minutes for each educator. The interview agenda of this study was to ask the teachers to: 1. compare the traditional teaching environment and online learning environment, and describe which functionalities of the eLearning system are useful; 2. why such functionalities are useful for instruction; and 3. explain why such consequences are of value to them. Summary of Content Analysis and Findings – The Critical Functionalities The data collected from the interview were coded and categorised independently by six teachers who have experience of using eLearning systems to produce the summary content code table of attributes, consequences, and values, as displayed in Table 2. The coders were unable to communicate with each other during the coding process. Disagreements on coding were resolved after consultation with area experts. Table 2: Content Codes Summary