10,856 research outputs found

    Student Perception Of General Education Program Courses

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    The purposes of this study were to: (a) determine, for General Education Program (GEP) courses, what individual items on the student form are predictive of the overall instructor rating value; (b) investigate the relationship of instructional mode, class size, GEP foundational area, and GEP theme with the overall instructor rating value; (c) examine what teacher/course qualities are related to a high (Excellent) overall evaluation or a low (Poor) overall evaluation value. The data set used for analysis contained sixteen student response scores (Q1-Q16), response number, class size, term, foundational area (communication, cultural/historical, mathematics, social, or science), GEP theme (yes/no), instructional mode (face-to-face or other), and percent responding (calculated value). All identifying information such as department, course, section, and instructor was removed from the analysis file. The final data set contained 23 variables, 8,065 course sections, and 294,692 student responses. All individual items on the student evaluation form were related to the overall evaluation item score, measured using Spearman\u27s correlation coefficients. None of the examined course variables were selected as significant when the individual form items were included in the modeling process. This indicated students employed a consistent approach to the evaluation process regardless of large or small classes, face-to-face or other instructional modes, foundational area, or percent responding differences. Data mining modeling techniques were used to understand the relationship of individual item responses and additional course information variables to the overall score. Items one to fifteen (Q1 to Q15), class size, instructional mode, foundational area, and GEP theme were the independent variables used to find splits to create homogenous groups in relation to the overall evaluation score. The model results are presented in terms of if-then rules for \u27Excellent\u27 or \u27Poor\u27 overall evaluation scores. The top three rules for \u27Excellent\u27 or \u27Poor\u27 based their classifications on some combination of the following items: communication of ideas and information; facilitation of learning; respect and concern for students; instructor\u27s overall organization of the course; instructor\u27s interest in your learning; instructor\u27s assessment of your progress in the course; and stimulation of interest in the course. Proportion of student responses conforming to the top three rules for \u27Excellent\u27 or \u27Poor\u27 overall evaluation ranged from 0.89 to .60. These findings suggest that students reward, with higher evaluation scores, instructors who they perceive as organized and strive to clearly communicate course content. These characteristics can be improved through mentoring or professional development workshops for instructors. Additionally, instructors of GEP courses need to be informed that students connect respect and concern and having an interest in student learning with the overall score they give the instructor

    Application of Particle Swarm Optimization to Formative E-Assessment in Project Management

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    The current paper describes the application of Particle Swarm Optimization algorithm to the formative e-assessment problem in project management. The proposed approach resolves the issue of personalization, by taking into account, when selecting the item tests in an e-assessment, the following elements: the ability level of the user, the targeted difficulty of the test and the learning objectives, represented by project management concepts which have to be checked. The e-assessment tool in which the Particle Swarm Optimization algorithm is integrated is also presented. Experimental results and comparison with other algorithms used in item tests selection prove the suitability of the proposed approach to the formative e-assessment domain. The study is presented in the framework of other evolutionary and genetic algorithms applied in e-education.Particle Swarm Optimization, Genetic Algorithms, Evolutionary Algorithms, Formative E-assessment, E-education

    The effect of student self -described learning styles within two models of teaching in an introductory data mining course

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    This dissertation examines the roles of learning styles and teaching methodologies within a data mining educational program designed for non-Computer Science undergraduate college students. The experimental design is framed by a discussion of the history and development of data mining and education, as well as a vision for its future.;Data mining is a relatively new discipline which has grown out of the fields of database management and data warehousing, statistics, logic, and decision sciences. Over the course of its approximately 15 year history, data mining has emerged from its genesis within the academic and commercial research and development arenas to become a widely accepted and utilized method of exploratory data analysis for management, strategic planning and decision support. Over the first several years of its development, data mining remained the province of computer scientists and professional statisticians at large corporations and research universities around the world. Beginning in about 1989, these data mining pioneers developed many of data mining\u27s standards and methodologies on large datasets using mainframe computing systems. Throughout the 1990s, as both the hardware and software tools required for the realization of data mining have become increasingly accessible, powerful and affordable, the pool of potential data miners has expanded rapidly. Today, even individuals and small businesses can exploit the power of data mining using freely acquirable open source software packages capable of running on personal computers.;During the growth and development of data mining methodologies however, little research has been dedicated specifically to the pedagogical approaches used in teaching data mining. Educational programs that have evolved have largely remained within Computer Science departments and have often targeted graduate students as an audience. This dissertation seeks to examine the possibility of successful teaching data mining concepts and techniques to a non-Computer Science undergraduate audience. The study approached this research question by delivering a lesson on the data mining topic of Association Rules to 86 participants who are representative of the target audience. These participants were randomly assigned to receive the Association Rules lesson through either a Direct Instruction or a Concept Attainment teaching approach. The students completed Kolb\u27s Learning Styles Inventory, participated in the data mining lesson, and then completed a quiz on the concepts and techniques of Association Rules. A t-test was used to determine if significant differences existed between the scores generated under the two teaching models, and an ANOVA was conducted to identify significant differences between the four learning style groups from Kolb\u27s instrument. In addition to these two statistical tests, the data were also mined using Association Rules and Decision Tree methods.;In both statistical tests, we failed to reject the null hypothesis, finding no significant differences in quiz scores between the two teaching models or among the four learning style groups. Further investigation into the differences among learning styles within teaching models however did reveal that the Assimilator learning style students who received their instruction via Direct Instruction did score significantly higher on the quiz than did their learning style counterparts who received the lesson via Concept Attainment. This finding suggests that although we cannot rely solely on one instructional approach as consistently more effective than the other, there may be instances where the correct instructional choice will positively benefit some learners with certain learning styles. The results of the data mining activities also support this assertion. Association Rules mining yielded no strong relationships between teaching models, learning styles and quiz scores, but Decision Tree mining did reveal a similar pattern of higher scores earned by Assimilator learners within Direct Instruction.;The findings of this study show that effectively teaching data mining concepts to undergraduate non-Computer Science students will not be as simple as choosing one teaching methodology over another or targeting a specific learning style group. Rather, designing instructional activities using teaching methodologies which closely align with predominant learning styles in a classroom should prove more effective. Perhaps the most significant finding of the study is that elementary data mining concepts and techniques can be effectively taught to the target audience. Finally, we recommend that additional teaching methodologies and perhaps different learning style assessments could be tested in the same way as those selected for this study

    SEMAG: A Novel Semantic-Agent Learning Recommendation Mechanism for Enhancing Learner-System Interaction

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    In this paper, we present SEMAG - a novel semantic-agent learning recommendation mechanism which utilizes the advantages of instructional Semantic Web rules and multi-agent technology, in order to build a competitive and interactive learning environment. Specifically, the recommendation-making process is contingent upon chapter-quiz results, as usual; but it also checks the students' understanding at topic-levels, through personalized questions generated instantly and dynamically by a knowledge-based algorithm. The learning space is spread to the social network, with the aim of increasing the interaction between students and the intelligent tutoring system. A field experiment was conducted in which the results indicated that the experimental group gained significant achievements, and thus it supports the use of SEMAG

    Data Mining in Online Professional Development Program Evaluation: An Exploratory Case Study

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    This case study explored the potential applications of data mining in the educational program evaluation of online professional development workshops for pre K-12 teachers. Multiple data mining analyses were implemented in combination with traditional evaluation instruments and student outcomes to determine learner engagement and more clearly understand the relationship between logged activities and learner experiences. Data analysis focused on the following aspects: 1) Shared learning characteristics, 2) frequent learning paths, 3) engagement prediction, 4) expectation prediction, 5) workshop satisfaction prediction, and 6) instructor quality prediction. Results indicated that interaction and engagement were important factors in learning outcomes for this workshop. In addition, participants who had online teaching experience could be expected to have a higher engagement level but prior online learning experience did NOT show a similar relationship

    Personalised trails and learner profiling within e-learning environments

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    This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails
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