Search

This paper introduces a Patron Counting and Analysis (PCA) system that leverages Wi-Fi-connection data to monitor space utilization and analyze visitor patterns in academic libraries. The PCA system offers real-time crowding information to the public and a comprehensive visitor analysis dashboard for library administrators. The system's development was driven by the need for occupancy restrictions during the pandemic, ensuring a spacious environment for library visitors as well as balancing between efficient utilization and adhering to social distancing regulations. Traditional methods of patron behavior performance and library spatial analysis, such as manual head counting or card-swiping systems, often incur additional costs for labor, hardware installation, or software subscription. The PCA system, however, utilizes existing Wi-Fi-connection data, providing a cost-effective solution to represent patron demographics and spatial usage. Limitations may arise when patrons do not carry Wi-Fi-enabled devices or during periods of low Wi-Fi service functionality. Implemented in Node.js and integrated with Python Flask framework and related libraries, the PCA system was piloted at the King Library in Miami University, successfully demonstrating a high validity compared to manually collected data. It filters out noise and redundancy, visualizes the occupancy index meter in real time, and generates statistical reports by linking user IDs with demographic information. The PCA system's reliability was validated through manually head counting data collected at the King Library in Miami University, establishing it as a reliable tool for library space management and patron analysis.

Recommender systems in education aim to help students make good decisions about the direction of their learning. The design of such systems in conventional research has treated the decision making process of students as a black box and assumes the best recommendations to be those that accurately predict student choices. Such an approach overlooks potentially valuable use cases for supporting optimal decision making, especially in self-directed learning contexts which present such challenges as identifying all available options, accurately evaluating the options against selection criteria, and selecting the best choice. This qualitative study aims to understand the areas where students struggle in the context of planning an open-ended project in order to inform the design of educational recommender systems. Data from interviews with 7 students at an international engineering school in Japan are analyzed to examine choice behaviors, influences on choice, and difficulty to choose in a self-directed learning context. The results illustrate considerations for designing educational recommender systems that can support the divergent thinking and convergent thinking demands of decision making. We provide case-based examples where the use of different recommender metrics, such as novelty and diversity, may provide value to users with different approaches to the decisionmaking process.

Many students in the Unites States enter college without having decided on a focus for their studies, and thus are faced with choosing from a large number of potential majors and associated very complex sets of degree requirements which can include many courses in other areas of study. Academic advisors use academic planning tools to help students make decisions about class schedules, selecting an academic major or minor, planning for graduation, and many other academic related activities. There is a dearth of decision support systems for degree planning, mainly due to the complexity of degree requirements, and thus many existing academic planning tools utilize static documents or PDF files for displaying information pertaining to degree requirements and course prerequisites. This work considers the complexity of degree requirements and presents the design and implementation of an efficient interactive decision support system that helps students explore degree completion paths. [The page range cited (pp. 101-116) on the .pdf is incorrect. The correct page range is pp. 101-115.]

As the drive to move from traditional face-to-face classroom learning to e-learning is ever in demand, the knowledge corpus exposed to students can be overwhelming because there is a need to automate certain functions of the e-learning framework. One of these functions is the course recommendation feature. Course recommendations help students save time and effort to explore the courses from a large pool of resources while considering multiple attributes such as social influence, prior knowledge, and learning style. These numerous criteria make the course recommendation a complex process that requires the researcher to promote online education and intelligently assist learners in identifying the relevant online courses. Although various researchers have put forward strategies to address course recommendation problems, learning style, a critical element in ensuring effective learning, has not been considered part of the course recommendation framework. This paper puts forward a learning style-based course recommendation framework that is expected to provide highly automated decision support for learners in identifying the most suitable course to improve their efficiency in e-learning. Additionally, based on this framework, instructors can analyze and re-evaluate the courses according to students' learning styles. The proposed framework reduces the time and effort involved in seeking relevant courses, thereby improving the learning experience.

Massive open online courses (MOOCs; online courses delivered over the Internet) enable distance learning without time and place constraints. MOOCs are popular; however, active participation level among students who take MOOCs is generally lower than that among students who take in-person courses. Students who take MOOCs often lack guidance, and the courses often fail to provide personalized learning materials. Artificial intelligence (AI) has been applied to manage increasing amounts of learning data in learners' online activity records. Driven by the trend in big data, AI technology has drawn increasing attention in various fields. AI-based recommendation systems (RSs) are powerful tools for improving resource acquisition through supply customization, and they can provide personalized learning materials as study guides. In this study, a personalized learning path for MOOCs based on long short-term memory (LSTM) was proposed to meet students' personal needs for learning. According to students' video-watching behaviors, we proposed an MOOC material RS that identifies students with similar learning behaviors through clustering and then uses the clustering results and the learning paths of each group of students to construct an LSTM model to recommend learning paths. The system's learning path recommendations can effectively improve the online participation of learners, and students who received recommendations progressed from the slow-progress group to the medium-progress or fast-progress group. In addition, the learning attitude questionnaire results indicated that the proposed system not only motivated learners to continue learning and achieve high learning capacity but also supported their study planning according to their individual learning needs.

Automated decision-making is becoming increasingly common in the public sector. As a result, political institutions recommend the presence of humans in these decision-making processes as a safeguard against potentially erroneous or biased algorithmic decisions. However, the scientific literature on human-in-the-loop performance is not conclusive about the benefits and risks of such human presence, nor does it clarify which aspects of this human--computer interaction may influence the final decision. In two experiments, we simulate an automated decision-making process in which participants judge multiple defendants in relation to various crimes, and we manipulate the time in which participants receive support from a supposed automated system with Artificial Intelligence (before or after they make their judgments). Our results show that human judgment is affected when participants receive incorrect algorithmic support, particularly when they receive it before providing their own judgment, resulting in reduced accuracy. The data and materials for these experiments are freely available at the Open Science Framework: https://osf.io/b6p4z/ Experiment 2 was preregistered.

Purpose: The current student selection process for short-term mobility actions under the Erasmus + program (i.e. intensive programs and blended intensive programs) is based exclusively on the students' order of enrolment and their grades. This study offers an alternative approach using the analytic hierarchy process based on a four-layer model that collects information about the specificities of each project and the profile of the students and also promotes greater inclusion and homogenization of the project teams. Design/methodology/approach: A decision support system was built by decomposing it into three stages--the predesign stage, in which the problem is characterized, and the user requirements are identified; the design stage, in which the models, the database and the interfaces are formulated; and the field stage, in which six test scenarios were built to validate the proposed solution. Findings: The results show that this model can be applied with various selection criteria among students and consider both their hard and soft skills. It can also be applied to help build teams in which the students' knowledge is aligned with the technical skills required by the projects. Originality/value: The proposed approach is innovative in that it responds to the emerging challenge of short-term European mobility programs that aim to involve students with multidisciplinary competencies. The solution considers both hard and soft skills in the selection of students, which allows changing the student selection paradigm and obtaining potentially more homogeneous multicultural teams with greater learning potential.

The introductory course in information systems plays an important role in a business school's curriculum. The soul of that course should be information and systems. The introductory course is where all business students, information systems majors and non-majors alike, learn why information and the technologies associated with it are driving organizations' strategy, processes, and successes. The content in that course and how it is taught profoundly shape students' perceptions of our discipline. Over the years there has been a reduction of focus on information and systems and increased focus on the latest technological innovation. We must get back to teaching how information systems drive decision making.

Purpose: In this study, Logistic Regression (LR), CHAID (Chi-squared Automatic Interaction Detection) analysis and data mining methods are used to investigate the variables that predict the mathematics success of the students. Research Methods: In this study, a quantitative research design was employed during the data collection and the analysis phases. Findings The findings obtained in this study showed that the variables, which had significant effects on the mathematical success of the students in each method, differ from each other. Although the independent variables with a significant effect on the dependent variable were different according to different methods, the findings indicated that the importance order of the variables did not change according to the method used. In this study, the correct classification ratios obtained by the class concerning PISA mathematics literacy differed by different methods. Implications for Research and Practice: CHAID analysis and REPTree algorithm may be an alternative for one another in the studies that aimed to classify individuals concerning their success. However, LR analysis should not be considered as an alternative method since it will provide significantly different results compared to the other two methods.

The purpose of this study is to compare decision trees obtained by data mining algorithms used in various areas in recent years according to different criteria. In the study, similar and different aspects of the decision trees obtained by different methods for classifying the students as successful and unsuccessful in terms of science literacy were revealed with the help of 12 independent variables included in the PISA 2015 student survey. Data collected across Turkey, from a total of 5895 students in the age group of 15, were analyzed in Java-based Weka software, which has an open source code. As a result of the analysis, it was found that the most successful algorithms in terms of correct classification rate were respectively Logistic Model, Hoeffding Tree, J.48, REPTree and Random Tree. In addition, regarding the decision trees obtained by different learning algorithms, variables that have been effective in the classification were found to be different. According to the results, it was concluded that independent variables found to be effective in the classification of the students for the decision trees obtained by different algorithms differed from each other and it was suggested to report the finding of more than one algorithm instead of those of only one algorithm.

Human resources training is considered an effective solution in empowering human resources. Organizations try to have effective educational planning for this precious resource by identifying shortcomings through a need assessment. This study provides a model based on organizational data analysis to achieve a unique and appropriate training planning for each staff. Therefore, job performance, organizational promotion and lay-off have become the basis for staff training planning. For this purpose, the tax assessor's information was investigated. Then, the CRISP-DM methodology was selected, and the project was implemented. Furthermore, a decision tree model was selected to extract unknown rules and patterns in the educational decision-making staff; the neural network model was selected as the predictive model to predict the target variables. The results revealed the decision tree for predicting job performance variables and organizational promotion status, and the neural network model was more effective in predicting service lay-off variables.

Deforestation is an environmental stressor that dramatically threatens biodiversity while having adverse, deep-rooted, socioeconomic impacts. Interventions to address deforestation require considerable long-term investments; therefore, assessing their impact is crucial. The UN Convention on Biological Diversity encourages establishing protected areas (PAs) as a strategy to conserve biodiversity, secure ecosystem services, support local livelihoods, and reduce climate change impacts. The Global Environment Facility (GEF) has supported the conservation and management of PAs, yet site accessibility and data availability present challenges in assessing PAs using traditional evaluation methods. We present a novel application of satellite Earth observations for evaluating the effectiveness and impact of PAs as a deforestation mitigation strategy. Geospatial analysis and ecological forecasting methodologies provide an efficient way to quantify land cover change, estimate above ground carbon stock, and evaluate ecosystem services provided by the PAs ex ante. We demonstrate the value of a geospatial approach through the evaluation of GEF-supported PAs in Kenya.

Student evaluation surveys provide educational institutions with important feedback regarding the student experience of teaching and courses; however, qualitative comments can contain offensive, insulting or threatening content. Large educational institutions generate thousands of comments per academic term; therefore, manual screening processes to find potentially harmful comments are not generally feasible. We developed a methodology for semi-automated screening of student comments that incorporates a machine learning decision support system and a detailed psychological assessment protocol. In a case study at a large public Australian university, our system identified 4,258 out of 62,049 (6.9%) comments as potentially harmful and requiring further review. Feedback from stakeholders demonstrates that this methodology is useful in reducing staff workload and could be broadly applied to different settings.

With the progress and innovation of digital technology, people's knowledge acquisition, information exchange, and communication modes have changed significantly. The universality of emerging technology media enables instructional designers to use digital media to provide different teaching materials and teaching methods to meet the needs of individual learners according to their differences. Accordingly, this study takes the Introduction to Network course as an example, proposes a Fuzzy Analytic Hierarchy Process combined with Rule-Space Model to guide and analyze students' learning status, and imports Microsoft Technology Association international certification as empirical analysis. The study revealed that, the experts believe that "understanding protocols and services" is the most important part of the basic knowledge of the Internet, followed by "understanding network infrastructure" and "understanding network hardware." In the section on learning performance, the average progress rates of two class were 69.44% and 62.20%, respectively, the students' scores were significantly improved. In the section on obtaining Microsoft Technology Association certification, the passing rates were 93.44% and 93.10%, which were significant improvements in certification passing rate. In the future, this study can be applied to other fields to improve the relative learning effect and make it broader and more diversified.

In three experiments, we sought to understand when and why people use an algorithm decision aid. Distinct from recent approaches, we explicitly enumerate the algorithm's accuracy while also providing summary feedback and training that allowed participants to assess their own skills. Our results highlight that such direct performance comparisons between the algorithm and the individual encourages a strategy of selective reliance on the decision aid; individuals ignored the algorithm when the task was easier and relied on the algorithm when the task was harder. Our systematic investigation of summary feedback, training experience, and strategy hint manipulations shows that further opportunities to learn about the algorithm encourage not only increased reliance on the algorithm but also engagement in experimentation and verification of its recommendations. Together, our findings emphasize the decision-maker's capacity to learn about the algorithm providing insights for how we can improve the use of decision aids.

Global food systems contribute to greenhouse gas emissions, and the mitigation of these emissions is a critical component of addressing the challenge of climate change. A variety of decision-support tools are available for agricultural producers to use to estimate how their management decisions affect emissions. These tools, often free and online, can be incorporated into agriculture and natural sciences courses, providing an engaging and interactive way for students to learn about climate change mitigation in online courses. Here, we focus on three tools: COMET-Planner, COMET-Farm, and Cool Farm Tool. Each of these tools link agricultural management with estimated emissions but differ according to the scope of analysis and type of functionality. Our case study navigates how to best incorporate each tool into undergraduate courses - providing detailed examples focused on crop production. Teaching notes provide guidance on pairing these activities with lessons related to agricultural policy, science communication, and farm nutrient budgets. Instructors have considerable opportunity to incorporate agricultural decision-support tools into courses to support students connecting scientific concepts to real-world application.

Educational data mining is an emerging research field concerned with developing methods for exploring the unique types of data that come from educational context. These data allow the educational stakeholders to discover new, interesting and valuable knowledge about students. In this paper, we present a new user-friendly decision support tool for predicting students' performance concerning the final examinations of a school year. Our proposed tool is based on a hybrid predicting system incorporating a number of possible machine learning methods and achieves better performance than any examined single learning algorithm. Furthermore, significant advantages of the presented tool are that it has a simple interface and it can be deployed in any platform under any operating system. Our objective is that this work may be used to support student admission procedures and strengthen the service system in educational institutions.

Purpose: The purpose of this paper is to provide a decision support model for optimizing the composition of portfolios of market-driven academic programs, primarily in schools offering market-driven academic programs. This model seeks to maximize financial performance during a desired planning time period while also achieving targets for other non-financial dimensions of the portfolio (e.g. mission alignment, student demographics and faculty characteristics) by deciding the types of programs to be added, redesigned and/or removed for each year of the planning period. Design/methodology/approach: This paper introduces an integer linear program (i.e. mathematical optimization) to describe the portfolio optimization problem. Integer linear programs are widely used for optimizing portfolios of financial and non-financial products and services in non-educational settings. Additionally, in order to use an integer linear program for the model, qualitative data must be incorporated into the quantitative model. To do so, this paper first discusses two methods of quantifying qualitative information related to market-driven program dimensions in the following section. Findings: The paper provides empirical insights related to the impact of this model through an illustrative case from a school offering market-driven academic programs at a prestigious private university in the USA. The results of the case highlight the potential positive impact of utilizing a similar model for planning purposes. Financially, the model results in almost double financial surplus than without the model while also achieving higher scores for all non-financial dimensions measured for the portfolio analyzed. Originality/value: This paper provides a unique and impactful model for decision support in strategic planning for market-driven academic programs, an area of intense discussion and focus in higher education today.

Mining social web data is a challenging task and finding user interest for personalized and non-personalized recommendation systems is another important task. Knowledge sharing among web users has become crucial in determining usage of web data and personalizing content in various social websites as per the user's wish. This paper aims to design a framework for extracting knowledge from web sources for the end users to take a right decision at a crucial juncture. The web data is collected from various web sources and structured appropriately and stored as an ontology based data repository. The proposed framework implements an online recommender application for the learners online who pursue their graduation in an open and distance learning environment. This framework possesses three phases: data repository, knowledge engine, and online recommendation system. The data repository possesses common data which is attained by the process of acquiring data from various web sources. The knowledge engine collects the semantic data from the ontology based data repository and maps it to the user through the query processor component. Establishment of an online recommendation system is used to make recommendations to the user for a decision making process. This research work is implemented with the help of an experimental case study which deals with an online recommendation system for the career guidance of a learner. The online recommendation application is implemented with the help of R-tool, NLP parser and clustering algorithm.This research study will help users to attain semantic knowledge from heterogeneous web sources and to make decisions.

The evolution of multi-tier systems of support (MTSS) for both academics and behavior has reflected the diverse interests of those leading implementation efforts, the influence of various state and local regulatory requirements, and differing funding methods and priorities. These variations have naturally led to many different pathways for implementing MTSS. Although the role of the district in MTSS has varied, many leaders in the field of education consider district leadership and involvement an essential component for successful MTSS implementation. District leadership in MTSS is used to provide schools with political and administrative support, training and technical assistance, layered in-service curricula, data-based decision making systems for ongoing evaluation, and access to interagency relationships for supporting student health and wellbeing. This article addresses the key district mechanisms that are used to integrate academic and behavioral interventions as school personnel learn new strategies for improving outcomes for students.

Landscape-scale conservation planning performed in a systematic and transparent manner is becoming more common as it is increasingly evident that ecological processes are being affected at large spatial scales. The Appalachian Landscape Conservation Cooperative undertook a 15-state landscape conservation planning project, resulting in a landscape conservation design called "NatureScape." To facilitate "NatureScape's" implementation by groups and individuals participating in on-the-ground landscape conservation, we developed an online decision support tool. This tool has the potential to assist Extension services in delivering research-based information to varied stakeholders as they make land use decisions.

Information integration is great for military operations because the range of pertinent information sources is significantly distinct and dynamic. This article develops an intelligent knowledge treasure comprised of military resource ontology and procedures, as a learning model for better interoperability of heterogeneous resources of the Indian military. This model can interpret and learn the context of military information automatically, thereby facilitating the military commanders with decision making in several operations, such as command and control, teaching and training, military coalition, situation awareness and many more. To design the military resource ontology, this article specifies the core concepts of the ontology based on terms derived from heterogeneous resources. WWW standard ontology language, OWL has been used to codify the ontology. This article develops an intelligent tool--"QueryOnto"--as an interface to the military resource ontology that provides a commander decision support service and demonstrates how to apply the military ontology in practice. The developed ontology has been verified and validated with the best known approaches and metrics available. The presented model is helpful for military commanders to train their juniors in a systematic way and will provide an efficient web-based learning of different military tasks in future.

Data-driven decision making, popularized in the 1980s and 1990s, is evolving into a vastly more sophisticated concept known as big data that relies on software approaches generally referred to as analytics. Big data and analytics for instructional applications are in their infancy and will take a few years to mature, although their presence is already being felt and should not be ignored. While big data and analytics are not panaceas for addressing all of the issues and decisions faced by higher education administrators, they can become part of the solutions integrated into administrative and instructional functions. The purpose of this article is to examine the evolving world of big data and analytics in American higher education. Specifically, it will look at the nature of these concepts, provide basic definitions, consider possible applications, and last but not least, identify concerns about their implementation and growth. (Contains 1 table and 2 figures.)

Community colleges continue to experience growth in online courses. This growth reflects the need to increase the numbers of students who complete certificates or degrees. Retaining online students, not to mention assuring their success, is a challenge that must be addressed through practical institutional responses. By leveraging existing student information, higher education institutions can build statistical models, or learning analytics, to forecast student outcomes. This is a case study from a community college utilizing learning analytics and the development of predictive models to identify at-risk students based on dozens of key variables. (Contains 4 tables and 3 figures.)

The authors describe the University of Central Florida's top-down/bottom-up action analytics approach to using data to inform decision-making at the University of Central Florida. The top-down approach utilizes information about programs, modalities, and college implementation of Web initiatives. The bottom-up approach continuously monitors outcomes attributable to distributed learning, including student ratings and student success. Combined, this top-down/bottom up approach becomes a powerful means for using large extant university datasets to provide significant insights that can be instrumental in strategic planning. (Contains 7 tables and 8 figures.)

Developments in computer and information technologies continue to give opportunities for designing advanced E-learning systems while entailing objective and technical evaluation methodologies. Design and development of E-learning systems require time-consuming and labor-intensive processes; therefore any decision about these systems and their analysis needs systematic and structured guidance to lead to better decisions. Multi-Criteria Decision Analysis (MCDA) techniques are applicable in instructional technology-related research areas as well as in other academic disciplines. In this study, a conceptual domain model and a decision activity framework is proposed for E-learning systems. Instructional, technological, and administrative decision domains are included in this model. Finally, an illustrative example is given to show that AHP is an effective MCDA method for E-learning-related decisions. (Contains 4 figures and 9 tables.)

The complex nature and uncertain information in social problems required the emergence of fuzzy decision support systems in social areas. In this paper, we developed user-friendly Fuzzy Group Decision Support Systems (FGDSS) software. The software can be used for multi-purpose decision making processes. It helps the users determine the main and sub evaluation criteria, their weights, and evaluate the performance according to the number of decision makers and evaluation weights of criteria. It also allows the user to use two different fuzzy inference methods. In the fuzzyfication unit, universe of discourse is made up of three different membership functions. The software, which has four main screens, is developed by using Delphi programming language and is used for the purpose of performance assessment of research assistants at Marmara University, Technical Education Faculty. (Contains 7 figures and 1 table.)

Introduction: The rapid growth of the Internet has modified the boundaries of information acquisition (tracking) in environmental scanning. Despite the numerous advantages of this new medium, information overload is an enormous problem for Internet scanners. In order to help them, intelligent agents (i.e., autonomous, automated software agents that are theoretically able to make inferences) have been developed. In this context, this paper examines how information acquisition activities based on intelligent agents could be structured in the long term in an environmental scanning process. Method: A longitudinal case study (action research) was conducted. As part of this qualitative approach, the data acquisition strategy employs three different methods (i.e., semi-structured interviews, observations and examinations of internal and external documents), which allows the collected information to be triangulated. Analysis: The data analysis strategy employed two different methods. Semi-structured interviews were recorded, transcribed and systematically coded. A thematic dictionary has been used for analysing the content (open and axial codings). Observations and internal or external documents were summarized. We then extracted the relevant data. These data were triangulated with those of semi-structured interviews. Results: Based on the information provided from our case study, it appears that our initial model must be revised. Three elements seem to be missing from the model: "information system infrastructure", "external entities with an organizational influence" and "environmental scanning service providers". Moreover, we highlight new relationships between components of our initial model. Conclusions: This action-research emphasizes the importance of the components, external entities with an organizational influence, information systems infrastructure and environmental scanning service providers for understanding the structuring mechanism of the tracking phases based on intelligent agents. (Contains 2 figures and 5 tables.)

Educational Theory into Practice Software (ETIPS) is an online case study program in the testbed stage of development intended for use by professors of education administration. The program is being developed by Sara Dexter and Pamela D. Tucker of the University of Virginia and is being tested by various other universities throughout Virginia. As a testbed member, the author will outline the theoretical framework, elaborating on the advantages of interactive, authentically contextualized online case studies over traditional print scenarios. Emphasis will be given to the outcomes of the program, which are to strengthen candidates' skills in data analysis, problem solving, and collaborative decision making. ETIPS enhances practical leadership skills for those who serve on the front lines. With the rapid growth of online principal preparation programs, this tool clearly represents a change in preparation.

When the human element is introduced into decision support system design, entirely new layers of social and ethical issues emerge but are not always recognized as such. This paper discusses those ethical and social impact issues specific to decision support systems and highlights areas that interface designers should consider during design with an emphasis on military applications. Because of the inherent complexity of socio-technical systems, decision support systems are particularly vulnerable to certain potential ethical pitfalls that encompass automation and accountability issues. If computer systems diminish a user's sense of moral agency and responsibility, an erosion of accountability could result. In addition, these problems are exacerbated when an interface is perceived as a legitimate authority. I argue that when developing human computer interfaces for decision support systems that have the ability to harm people, the possibility exists that a moral buffer, a form of psychological distancing, is created which allows people to ethically distance themselves from their actions. (Contains 2 figures.)

The concept of Electronic Performance Support Systems (EPSS) is containing multimedia or computer based instruction components that improves human performance by providing process simplification, performance information and decision support system. EPSS has become a hot topic for organizational development, human resources, performance technology, training, and educational development professionals. A conceptual framework of EPSS is constructed under five interrelated and interdependent domains for educational implications. The domains of the framework are online collaboration, cost-effectiveness, motivation, service management, and performance empowering. IBM Lotus Notes 6 (LN6) is used as an example application tool to illustrate the power of this framework. The framework describes a set of relevant events based upon deductive analyses for improving our understanding of the EPSS and its implications on education and training. The article is also pointed out that there are some similarities between the EPSS' and the LN6's specific features within this conceptual framework. It can provide some guidelines and benefits to researchers, educators, and designers as well. (Contains 1 table and 1 figure.)

Advances in computers and multimedia technology have changed traditional methods for learning and skills training. Online learning continues to play a major success of any academic program. Such learning can personalize learning needs for students, it can provide an environment where virtual reality techniques are used to create interactive interfaces and real-time software can monitor every response made by the user. This paper presents an attempt to introduce the concepts of fuzzy set theory in the design of an online educational module. Such a module can deal with uncertainties in the knowledge acquisition, representation and decision making. The fuzzy logic principles are used in creating the learner model and to provide the appropriate teaching material to each learner according to his/her learning level.