Modeling for E-Learning Systems

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Category: IT Education

Modeling for E-Learning Systems Maria Alexandra Rentroia-Bonito Instituto Superior Técnico/Technical University of Lisbon, Portugal Joaquim Armando Pires Jorge Instituto Superior Técnico/Technical University of Lisbon, Portugal

INTRODUCTION Computer-based instruction is touted as an effective tool to support knowledge dissemination within predefined learning environments. Indeed, many see it as a way to overcome geographical or social barriers to knowledge transmission and educational institutions. However, its domain of application has traditionally been restricted to basic skills and educational contexts. Recently, dynamic and complex business environments shaped by technological changes and the downsizing trend of the ’90s placed new constraints on the underlying assumptions (Fuglseth, 2003). Organizations are now pushing for skill flexibility, demanding specialized knowledge and requiring faster learning curves from employees. Many advocate Internet-based education materials as one way to meet those challenges (Bernardes & O’Donoghue, 2003; Karoulis et al., 2004; Storey et al., 2002; Strazzo & Wentling, 2001). However, this raises important questions concerning both effectiveness and efficiency of such tools and materials. Indeed, developing interactive multimedia-based courseware remains pretty much a black art, consuming enormous resources. So far, there is a lack of established models to predict the performance and evaluate how adequately courseware can meet user needs. In fact, developing courseware should take into account the target constituency requirements, organizational context, and the stated educational or training goals. Developing the wrong training materials can lead to costly investments in creating and maintaining content to match the increasing expectations on e-learning. Perhaps this can explain the recent rash of failed e-learning projects—current results do not measure up to business and individual expectations yet. A better understanding of the many factors affecting e-learning performance would allow individuals and organizations to achieve the expected benefits. In so doing, development teams need methods, techniques, and tools to evaluate in advance which features are needed to achieve higher outcomes, namely, performance and satisfaction. Thus, the need to develop predictive models to improve learning effectiveness is in order. This overview includes four sections. “Background” presents a proposed e-learning theoretical framework to guide

our analysis based upon the reviewed literature. “Key Issues” section describes main issues arising from the proposed elearning conceptual framework. “Future Trends” describes our vision on how to approach e-learning initiatives and future trends. Finally, we present a general conclusion.

BACKGROUND Organizational investment in e-learning strategies reflects strategic choices regarding skill development through e-learning. According to Wentling, Waight et al. (2000), e-learning involves acquiring and using distributed knowledge facilitated by electronic means in synchronous or asynchronous modes. As shown in Figure 1, knowledge could be distributed geographically within varying time frames. Thus, the effective use of technology-based instruction would provide to organizations the ability to succeed at operational levels. This justifies the adoption of a holistic approach to courseware evaluation as a diagnostic and managerial tool. We propose a framework, shown in Figure 2, which comprises three basic entities, business processes, people, and information systems, and three main relationships: (a) interaction between people and systems, (b) process-based Figure 1. Proposed types of e-learning in terms of time and place

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Modeling for E-Learning Systems

roles played by people during this interaction, and (c) having the learning task be executed, as part of the e-learning experience, by people performing their process-based roles. This framework could lead to working techniques and approaches that assist development team members in designing work-related e-learning experiences within organizational contexts. To motivate a workable approach, we will now discuss each of these entities and relationships. Reviewed literature strongly suggests that the external and internal fit among business strategies, culture, human resource practices, and leadership styles is critical to worker performance. Moreover, work contexts, for example, physical and technological conditions surrounding individual tasks, affect people’s perceptions and, in turn, influence their motivation to engage into and perform learning tasks (Astleitner, 2001; Bandura, 2000; Chen, 2002; Dix et al., 1998; Kim, 2000; Liu & Dean, 1999; Reeves & Nass, 1996; Strazzo & Wentling, 2001; Vouk et al., 1999; Welbourne et al., 2000; Wentling et al., 2000). Within the e-learning experience, business processes provide yardsticks to define educational or training goals and monitor outcomes. However, we need also to consider the roles people perform when interacting with courseware. Such process-based roles could be as diverse as e-learners, e-instructors, e-speakers, systems and courseware designers, supervisors, reviewers, human resource managers, and information technology officers among many others. Human-computer interaction can model parts of the elearning experience in accordance with Norman’s extended model (Dix et al., 1998). Furthermore, the experience is also shaped by the way people relate to systems. This is supported by Reeves’ and Nass’ (1996) work, which suggests that people relate to media as they would relate to real people, treating them with affection and courtesy. Building on these findings, we argue that the more e-learning systems themselves are easy to use and learn and are “nicely behaved,” the likelier

e-learners will engage in the experience and profit from their outcomes. The interplay among these three relationships (processbased role, learning task, and interaction) relates to a justin-time learning concept. Strategic knowledge acquisition should be enmeshed in current activities to support employees in learning new skills when performing day-to-day business tasks. We believe this concept can foster gradual alignment between learning outcomes, and technology with strategic aspects of business.

Key Issues We identify structure and relationship as the main issues within our framework as presented in the previous section. Figure 1 shows different modes of e-learning regarding the use of technology in education, both in terms of distance and time. As technology gets more extensively used for delivery, the need for course structure becomes higher and the relationship between instructor and e-learner turns increasingly weaker. Figure 1 also shows this relationship as defining three types of e-learning, which are set apart from conventional classroom instruction. This shows that using technology to support learning requires higher course structure than traditional classroombased instruction to be effective (Karoulis et al., 2004; Liu & Dean, 1999). However, current approaches take a onesize-fits-all method to provide courseware delivery regardless of differences in place and time. We cannot argue strongly enough that delivery needs to be tailored to context (space and time) to overcome the barriers imposed by structure and to improve the e-learning experience. This should be done differently for different students with diverse cognitive styles, roles, and tasks within organizational contexts. We will now discuss factors affecting structure and relationship.

Figure 2. Proposed e-learning framework

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As for structure, organizations identify training needs taking into account work context, business process dynamics, individual tasks, objectives, and areas for performance improvement. A business-process approach driving the design and the development of interactive course contents should focus on skill gaps to define instructional objectives in order to meet performance standards. In this way, setting up appropriate goals for training evaluation poses the same requirements both for electronic and traditional media. However, as courseware becomes available and distributed through the Internet, quality of service (QoS) becomes an increasingly important factor to e-learner satisfaction. Thus, technology becomes another structural issue. From the technology standpoint, three aspects are critical to e-learner satisfaction. The first is courseware evaluation (Chen, 2002; Karoulis et al., 2004; Kim, 2000; Liu & Dean, 1999; Storey et al., 2002; Strazzo & Wentling, 2001; Wentling et al., 2000). Indeed, users’ perceptions of mismatch between content and structure reduce their motivation to learn and perform (Astleitner, 2001). Usability is a second aspect affecting both engagement and acceptance. It measures the extent to which a computer system can be used to complete well-defined tasks or achieve specified goals productively and satisfactorily for the intended users in a given context (Dix et al., 1998). Last, but not the least, user modeling completes the set of key technological aspects for e-learners’ satisfaction. User modeling is the knowledge a system has about the user’s level of knowledge and intentions, processed as users interact with systems. Knowledge of both user and task domains should allow intelligent and adaptable systems to properly respond to the competence levels and needs of the tasks within contexts of use (Dix et al., 1998). This holistic understanding would help developers take into consideration users’ expectations at the early stages of system design. In this way, expected learner performance would supplement the metrics suggested by the literature (Dix et al., 1998; Wentling et al., 2000) concerning the implementation of strategies and quality-of-service goals. Regarding relationship issues, two factors are relevant for this overview: cognitive styles and motivation. Cognitive styles are individual characteristics that serve as stable indicators of how learners perceive, think of, remember, and solve problems (Kim, 2000; Liu & Dean, 1999). This characteristic is consistent with major dimensions of individual differences and explains stable individual performance across tasks over time. Thus, it is a key variable in designing effective systems for a particular user group, especially at early stages of the interaction. Indeed, results show that cognitive styles can help in explaining usability problems when browsing hypermedia documents (Chen, 2002; Kim, 2000; Liu & Dean, 1999). However, research results are numerous and mixed and give no consistent evidence about the relationship between cognitive styles and learners’ performance in computer-based settings (Shih & Gamon, 2001; 2648

Wentling et al., 2000). Motivation can be defined as the internal set of processes (both cognitive and behavioral) through which human energy becomes focused on the direction of effort (Welbourne et al., 2000). This definition is twofold. First, the internal set of processes by which human energy becomes focused describes the “agentic” nature of individuals in interaction with their environment (Bandura, 2000; Liu et al., 2002). A second relevant aspect of motivation is the direction of effort, which implies individual goal orientation assessed using defined internal success criteria. These two motivation-related aspects require that development teams actively involve different user roles at early stages of system design to get high usability, learnability, acceptance, and usage levels in order to likely match specific individual, task, and contextual characteristics. In this way, organizations could be more effective in creating the conditions to foster knowledge acquisition, transfer, and reutilization across different learner groups.

FUTURE TRENDS Currently, people perceive learning as a product rather than a process. Adopting the latter view would require a holistic approach to analyze e-learner experience. This may lead to a paradigm shift in dealing with process, especially since learning, as an activity, is deeply enmeshed in interdependent and rapidly evolving organizational processes. Our vision is to pursue useful endeavors to effectively support learning by people within organizational contexts during their performance of work-related tasks. In this sense, e-learning systems would become tools for people to use, as often as needed, to acquire new knowledge related to current or future tasks. For this to happen, we need to develop a close people-process-system fit. To achieve that, cost-effective, integrated tools for courseware and competence-building evaluation within specific knowledge domains are in order. This requires coordination of efforts, information, and competencies among the key players, including universities, companies, government, and research communities. Such coordination is fundamental to achieve skill development at individual, organizational, and societal levels, articulated with defined strategies. Table 1 summarizes the topics identified in our analysis and describes specific research goals toward the fulfillment of this vision.

CONCLUSION We have discussed a holistic approach covering business processes, e-learners, and information systems fit, while stressing the need for quantitative models, especially in evaluation. The interplay among these entities defines the

Modeling for E-Learning Systems

Table 1. Identified research topics Issues Learning task E-learner

Interaction

M

Proposed research topics How do e-learning initiatives relate to organizational knowledge management practices? In what conditions could e-learning be effective for everybody? Are there usability metrics universal across roles, levels of technology experience, cognitive styles, job levels, organizational contexts, and cultures? Does task complexity level mediate users’ perception about usability? Would the initial role assigned by people to media hold steadily throughout the learning process, or does it change over time, motivated by learners’ habits? To what extent is learners’ trust and performance affected by perceived support for privacy, confidentiality, and security at the system level? Could software agents be the new “hidden persuaders” (Packard, 1957/1981) to get learners to go further into the skill-development cycle, overcoming obstacles along the way? Should such concerns be incorporated into a design discipline centered on ethical and deontological roles?

organizational space for the just-in-time learning concept. The expected benefits lie in aligning learning outcomes with business strategies.

programs: A preliminary investigation. In C. Ghaoui (Ed.), Usability evaluation of online learning programs (pp. 8497). Hershey, PA: Idea Group Publishing.

REFERENCES

Kim, K. (2000). Individual differences and information retrieval: Implications on Web design. Retrieved September 2001 from http://citeseer.nj.nec.com/update/409393

Astleitner, H. (2001). Web-based instruction and learning: What do we know from experimental research? Retrieved November 2001 from http://rilw.emp.paed.uni-muenchen. de/2001/papers/astleitner.html

Liu, Y., & Dean, G. (1999). Cognitive styles and distance education. Online Journal of Distance Learning Administration, II(III). Retrieved September 2001 from http://www. westga.edu/~distance/and23.html

Bandura, A. (2000). Cultivate self-efficacy for personal and organizational effectiveness. In E. A. Locke (Ed.), Handbook of principles of organizational behavior (pp. 20-136). Oxford, United Kingdom: Blackwell.

Liu, Y., Lavelle, E., & Andris, J. (2002). Experimental effects of online instruction on locus of control. USDLA Journal, 16(6). Retrieved April 2004 from http://www.usdla. org/html/journal/JUN02_issue/article02.html

Bernardes, J., & O’Donoghue, J. (2003). Implementing online delivery and learning support systems: Issues, evaluation and lessons. In C. Ghaoui (Ed.), Usability evaluation of online learning programs (pp. 19-39). Hershey, PA: Idea Group Publishing.

Packard, V. (1981). The hidden persuaders. Penguin Books. (Original work published 1957)

Chen, S. (2002). A cognitive model for non-linear learning in hypermedia programmes. British Journal of Educational Technology, 33(4), 449-460. Dix, A., Finlay, J., Abowd, G., & Beale, R. (1998). Humancomputer interaction (2nd ed.). Prentice Hall Europe. Fuglseth, A. M. (2003). A tool kit for measurement of organizational learning: Methodological requirements and an illustrative example. Journal of Universal Computer Science, 9(12), 1487-1499. Karoulis, A., Tarnanas, I., & Pombortsis, A. (2003). An expert-based evaluation concerning human factors in ODL

Reeves, B., & Nass, C. (1996). The media equation: How people treat computers, television, and new media like real people and places. Cambridge University Press. Shih, C., & Gamon, J. (2001). Web-based learning: Relationships among student motivation, attitude, learning styles, and achievement. Journal of Agricultural Education, 42(4). Retrieved April 2004 from http://pubs.aged.tamu.edu/jae/ pdf/Vol42/42-04-12.pdf Storey, M. A., Phillips, B., Maczewski, M., & Wang, M. (2002). Evaluating the usability of Web-based learning tools. Educational Technology and Society, 5(3). Retrieved April 2004 from http://ifets.ieee.org/periodical/Vol_3_2002/ storey.html Strazzo, D., & Wentling, T. (2001). A study of e-learning 2649

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practices in selected Fortune 100 companies. Retrieved September 2001 from http://learning. ncsa.uiuc.edu/papers/elearnprac.pdf Vouk, M., Bilzer, D., & Klevans, R. (1999). Workflow and end-user quality of service issues in Web-based education. NC: North Carolina State University, Department of Computer Science. Retrieved September 2001 from http://www. computer.org/tkde/tk1999/k0673abs.htm Welbourne, T., Andrews, S., & Andrews, A. (2000). Back to basics: Learning about energy and motivation from running on my treadmill. Retrieved September 2001 from http://www. eepulse.com/pdfs/treadmill%20adobe%203.1.01.pdf Wentling, T., Waight, C., Gallager, J., La Fleur, J., Wang, C., & Kanfer, A. (2000). E-learning: A review of literature. Retrieved September 2001 from http://learning.ncsa.uiuc. edu/papers/elearnlit.pdf

KEY TERMS Business Process: A set of organized work-related tasks and resources to pursue a specific organizational objective influencing learning experiences by defining two specific relationships: process-based roles (between business process and people) and learning tasks (between business process and information systems). E-Learning Experience: A process by which people identify work-related learning needs, formulate related goals and the associated internal level-of-success criteria, search for feasible online options to achieve defined learning goals, select and acquire choices, and engage into and complete them successfully by achieving the related goals in a productive and satisfactory manner.

E-Learning Framework: A formal construct to diagnose and manage learning outcomes in terms of the operational dynamic of three basic entities: business process, information systems, and people. Just-In-Time Learning: Strategic knowledge acquisition enmeshed in business activities to support employees in learning new skills when performing day-to-day tasks, while fostering the alignment between learning outcomes, technological and strategic business issues. Learning Task: A set of steps with a defined learning goal addressing specific training needs identified within business processes driving the definition of proper instructional design and e-learning system requirements. Motivation to E-Learn: An individual variable denoting an internal set of processes (both cognitive and behavioral) by which human energy becomes focused on learning particular work-related content (whether by actively interacting with courseware, participating in a virtual class, self-studying, doing e-homework alone or in group) to achieve specific learning goals. Process-Based Role: The combination of a set of expected work-related behaviors, responsibilities, and the associated set of required competencies to perform within business settings in order to achieve organizational goals. People-Process-System Fit: A degree of consistency among learner groups, business processes, and e-learning systems that (a) reflects the target constituency requirements, organizational context, and the stated educational or training goals, (b) applies the principles and approaches of constructivism, user-centeredness, participatory design, quality management, and organizational development to instructional design of courseware, and (c) translates into expected performance levels.

This work was previously published in Encyclopedia of Information Science and Technology, edited by M. Khosrow-Pour, pp. 1996-2000, copyright 2005 by Information Science Reference, formerly known as Idea Group Reference (an imprint of IGI Global).

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