A Weighted Concept Map Approach to Generate Learning Guidance in Science Courses
01 Jan 2015-Advances in intelligent systems and computing (Springer, New Delhi)-Vol. 340, pp 143-152
TL;DR: A Weighted Concept Map based approach is proposed to provide remedialLearning guidance to the learners by generating automated Concept Map and assigning weights to the concepts based on their importance for computation of best remedial learning path.
Abstract: During the last two decades it has been observed that there has been substantial advancement in the domain of E-Learning systems. This may be attributed to development in the field on networking and communication. However most of these systems lack face to face interaction between the learner and the tutor and hence are unable to pinpoint the learner deficiencies. In this context, several researchers have used Concept Maps for identifying student learning barriers. However a major drawback of Concept Map approach is that all concepts are given equal degree of importance from the learner point of view. In this work a Weighted Concept Map based approach is proposed to provide remedial learning guidance to the learners. The system works by generating automated Concept Map and assigning weights to the concepts based on their importance for computation of best remedial learning path. The approach has been tested with a set of middle school students for identifying their learning deficiencies in science courses and the results found to be satisfactory.
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TL;DR: The use of CMs as an educational tool has been widely accepted in Computer Science and it has been extensively investigated due to support in teaching and learning.
Abstract: Context: concept Maps (CMs) enable the creation of a schematic representation of a domain knowledge. For this reason, CMs have been applied in different research areas, including Computer Science. Objective: the objective of this paper is to present the results of a systematic mapping study conducted to collect and evaluate existing research on CMs initiatives in Computer Science. Method: the mapping study was performed by searching five electronic databases. We also performed backward snowballing and manual search to find publications of researchers and research groups that accomplished these studies. Results: from the mapping study, we identified 108 studies addressing CMs initiatives in different subareas of Computer Science that were reviewed to extract relevant information to answer a set of research questions. The mapping shows an increasing interest in the topic in recent years and it has been extensively investigated due to support in teaching and learning. Conclusions: based on our results we conclude that the use of CMs as an educational tool has been widely accepted in Computer Science.
26 citations
Additional excerpts
...…(2014) 58 Arruarte et al. (2012) 59 Roy (2010) 60 Hilbert et al. (2008) 61 Buendía-García and Benlloch-Dualde (2012) 62 Gaines and Shaw (1995) 63 Acharya and Sinha (2015b) 64 Anohina-Naumeca et al. (2010) 65 Molinari et al. (2008) 66 Atapattu et al. (2014) 67 Leake et al. (2014) 68 Rueda et al.…...
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...…et al. (2008) 48 Chen et al. (2008) 49 León et al. (2008) 50 Portmann et al. (2012) 51 Fan et al. (2009) 52 Coffey (2007) 53 Tseng et al. (2007) 54 Acharya and Sinha (2015a) 55 Graudina and Grundspenkis (2011) 56 Iqbal et al. (2013) 57 Yoon et al. (2014) 58 Arruarte et al. (2012) 59 Roy (2010) 60…...
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TL;DR: A modern design of a dynamic learning environment that goes along the most recent trends in e-Learning is proposed, and an overall performance superiority of a support vector machine model in evaluating the knowledge levels is illustrated.
Abstract: Electronic Learning has been one of the foremost trends in education so far. Such importance draws the attention to an important shift in the educational paradigm. Due to the complexity of the evolving paradigm, the prospective dynamics of learning require an evolution of knowledge delivery and evaluation. This research work tries to put in hand a futuristic design of an autonomous and intelligent e-Learning system. In which machine learning and user activity analysis play the role of an automatic evaluator for the knowledge level. It is important to assess the knowledge level in order to adapt content presentation and to have more realistic evaluation of online learners. Several classification algorithms are applied to predict the knowledge level of the learners and the corresponding results are reported. Furthermore, this research proposes a modern design of a dynamic learning environment that goes along the most recent trends in e-Learning. The experimental results illustrate an overall performance superiority of a support vector machine model in evaluating the knowledge levels; having 98.6%of correctly classified instances with 0.0069 mean absolute error.
25 citations
Cites background from "A Weighted Concept Map Approach to ..."
...Moreover, language teaching and practical training needs incorporating the users in a different manner, for example voice/motion recognition and evaluation....
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TL;DR: A method for development of concept map in web-based environment for identifying concepts a student is deficient in after learning using traditional methods and it was found that posttest results are directly proportional to the quality of traditional learning.
Abstract: The aim of this article is to propose a method for development of concept map in web-based environment for identifying concepts a student is deficient in after learning using traditional methods. D...
10 citations
Cites background or methods from "A Weighted Concept Map Approach to ..."
...It was found that modulo division method generates the most compact hash table (Acharya and Sinha, 2015a)....
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...A study may be taken up in which these confidences may be used in computation of learning sequence (Acharya & Sinha, 2015a)....
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01 Oct 2017
TL;DR: It is found that personalized learning environment uses EDM techniques more exhaustively compared to collaborative learning for concept map construction in web based environment.
Abstract: This aim of this article is to study the use of Educational Data Mining (EDM) techniques in constructing concept maps for organizing knowledge in web based learning systems whereby studying their synergistic effects in enhancing learning. This article first provides a tutorial based introduction to EDM. The applicability of web based learning systems in enhancing the efficiency of EDM techniques in real time environment is investigated. Web based learning systems often use a tool for organizing knowledge. This article explores the use of one such tool called concept map for this purpose. The pioneering works by various researchers who proposed web based learning systems in personalized and collaborative environment in this arena are next presented. A set of parameters are proposed based on which personalized and collaborative learning applications may be generalized and their performances compared. It is found that personalized learning environment uses EDM techniques more exhaustively compared to collaborative learning for concept map construction in web based environment. This article can be used as a starting point for freshers who would like to use EDM techniques for concept map construction for web based learning purposes.
4 citations
Cites methods from "A Weighted Concept Map Approach to ..."
...Acharya and Sinha[61] Learning by concept map construction by exchanging SMS Collaborative Learning System (CLS) developed using Android emulator ‘Extended’ Theory of Meaningful Learning Two groups of students, one learning theory and another practical collaborate to develop association rules which form the edges of the concept map Equally important is the inputs required for construction of concept map....
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...As examples Tseng et al. [49] have used PHP and MySQL for a P-Learning application whereas Android emulator has been used by Acharya and Sinha [53,61] for both types of applications....
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...Acharya and Sinha [62] proposed the use of weighted concept maps to compute the learning path that has highest degree of importance from learner point of view....
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...Similarly Acharya and Sinha [53] have used the theory of meaningful learning for their P-Learning system....
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...Acharya and Sinha [53] in their work have used Direct Hashing and Pruning (DHP) algorithm to generate ‘optimized’ concept map of learning....
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09 Nov 2018
TL;DR: A tree-based approach (i.e., FP tree algorithm) is adopted in this project to overcome the drawbacks of the Apriori algorithm in the construction of concept maps for adaptive learning systems.
Abstract: A concept map is a diagram that depicts suggested relationships between concepts. The relationships are marked by a relevance degree that denotes the level of correlation between any two concepts. Concept map is a graphical tool used to structure and organize knowledge. In this project, a concept map will be generated based on a real-life dataset of how questions are answered by students (correctly or incorrectly) and the weight of the concepts in the questions. Several algorithms have been proposed to automatically construct concept maps. However, all these algorithms use Apriori algorithm to discover the frequent itemsets and get the association rules. Apriori algorithm requires several database scans, and thus, it is not efficient. A tree-based approach (i.e., FP tree algorithm) adopted in this project to overcome the drawbacks of the Apriori algorithm in the construction of concept maps for adaptive learning systems.
1 citations
References
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IBM1
TL;DR: The number of candidate 2-itemsets generated by the proposed algorithm is, in orders of magnitude, smaller than that by previous methods, thus resolving the performance bottleneck, and allows us to effectively trim the transaction database size at a much earlier stage of the iterations, thereby reducing the computational cost for later iterations significantly.
Abstract: In this paper, we examine the issue of mining association rules among items in a large database of sales transactions. The mining of association rules can be mapped into the problem of discovering large itemsets where a large itemset is a group of items which appear in a sufficient number of transactions. The problem of discovering large itemsets can be solved by constructing a candidate set of itemsets first and then, identifying, within this candidate set, those itemsets that meet the large itemset requirement. Generally this is done iteratively for each large k-itemset in increasing order of k where a large k-itemset is a large itemset with k items. To determine large itemsets from a huge number of candidate large itemsets in early iterations is usually the dominating factor for the overall data mining performance. To address this issue, we propose an effective hash-based algorithm for the candidate set generation. Explicitly, the number of candidate 2-itemsets generated by the proposed algorithm is, in orders of magnitude, smaller than that by previous methods, thus resolving the performance bottleneck. Note that the generation of smaller candidate sets enables us to effectively trim the transaction database size at a much earlier stage of the iterations, thereby reducing the computational cost for later iterations significantly. Extensive simulation study is conducted to evaluate performance of the proposed algorithm.
1,625 citations
Book•
01 Jan 1988
TL;DR: This book discusses the development of Intelligent Tutoring Systems for Electronic Troubleshooting, the design of Instructional Design environments, and other topics related to knowledge acquisition and representation.
Abstract: Contents: Maj. K.R. Rose, Foreword: What the Army Expects from Intelligent Training Systems. J. Psotka, L.D. Massey, S.A. Mutter, Introduction. Section I:Knowledge Acquisition. W.B. Johnson, Developing Expert System Knowledge Bases in Technical Training Environments. B. Means, S.P. Gott, Cognitive Task Analysis as a Basis for Tutor Development: Articulating Abstract Knowledge Representations. Y.J. Tenney, L.C. Kurland, The Development of Troubleshooting Expertise in Radar Mechanics. D. Kieras, What Mental Model Should be Taught: Choosing Instructional Content for Complex Engineered Systems. Section II:Intelligent Instructional Design. L.C. Kurland, Y.J. Tenney, Issues in Developing an Intelligent Tutor for a Real-World Domain: Training Radar Mechanics. P.L. Pirolli, J.G. Greeno, The Problem Space of Instructional Design. D.M. Russell, T.P. Moran, D.S. Jordan, The Instructional-Design Environment. S.A. MacMillan, D. Emme, M. Berkowitz, Instructional Planners: Lessons Learned. D.C. Wilkins, W.J. Clancey, B.G. Buchanan, Using and Evaluating Differential Modeling in Intelligent Tutoring and Apprentice Learning Systems. Section III:Knowledge Representation. F. Ritter, W. Feurzeig, Teaching Real-Time Tactical Thinking. T. Govindaraj, Intelligent Computer Aids for Fault Diagnosis Training of Expert Operators of Large Dynamic Systems. D.M. Russell, IDE: The Interpreter. J.R. Frederiksen, B.Y. White, A. Collins, G. Eggan, Intelligent Tutoring Systems for Electronic Troubleshooting. L.D. Massey, J. de Bruin, B. Roberts, A Training System for Radar Maintenance. Section IV:Intelligent Tutoring Architectures. J.G. Bonar, R. Cunningham, Bridge: Tutoring the Programming Process. W. Feurzeig, F. Ritter, Understanding Reflective Problem Solving. D. Frye, D.C. Littman, E. Soloway, The Next Wave of Problems in ITS: Confronting the "User Issues" of Interface Design and System Evaluation. D.M. Towne, A. Munro, The Intelligent Maintenance Training System.
314 citations
TL;DR: A website is created providing functions enabling learning to take place anytime and anywhere with any available learning device, for ubiquitous learning according to various properties of mobile devices and results indicate that the proposed system can enhance three learning performance indicators, namely academic performance, task accomplishment rates, and learning goals achievement rates.
Abstract: The portability and immediate communication properties of mobile devices influence the learning processes in interacting with peers, accessing resources and transferring data. For example, the short message and browsing functions in a cell phone provide users with timely and adaptive information access. Although many studies of mobile learning indicate the pedagogical potential of mobile devices, the screen size, computational power, battery capacity, input interfaces, and network bandwidth are too restricted to develop acceptable functionality for the entire learning processes in a handheld device. Therefore, mobile devices can be adopted to fill the gap between Web-based learning and ubiquitous mobile learning. This study first creates a website, providing functions enabling learning to take place anytime and anywhere with any available learning device, for ubiquitous learning according to various properties of mobile devices. Nowadays, learners' behaviors on a website can be recorded as learning portfolios and analyzed for behavioral diagnosis or instructional planning. A student model is then built according to the analytical results of learning portfolios and a concept map of the learning domain. Based on the student model and learners' available learning devices, three modules are developed to build a ubiquitous learning environment to enhance learning performance via learning status awareness, schedule reminders and mentor recommendation. Finally, an experiment is conducted with 54 college students after implementation of the ubiquitous learning website. Experimental results indicate that the proposed system can enhance three learning performance indicators, namely academic performance, task accomplishment rates, and learning goals achievement rates.
279 citations
TL;DR: In this article, a conceptual map model is proposed to provide learning suggestions by analyzing the subject materials and test results, and a testing and diagnostic system is also implemented on computer networks based on the novel approach.
Abstract: With the recent rapid progress of computer technology, researchers have attempted to adopt artificial intelligence and use computer networks to develop computer-aided instruction systems. Meanwhile, researchers have also attempted to develop more effective programs to test and enhance the learning performance of students. However, conventional testing systems simply give students a score, and do not give them the opportunity to learn how to improve their learning performance. Students would benefit more if the test results could be analyzed and hence advice could be provided accordingly. This study proposes a conceptual map model, which provides learning suggestions by analyzing the subject materials and test results. A testing and diagnostic system is also implemented on computer networks based on the novel approach. Experimental results have demonstrated that the novel approach benefits students and deserves further investigation.
215 citations
TL;DR: This paper proposes a two-phase concept map construction (TPCMC) approach to automatically construct the concept map by learners' historical testing records by applying fuzzy set theory, education theory, and data mining approach to find its grade fuzzy association rules.
Abstract: In recent years, e-learning system has become more and more popular and many adaptive learning environments have been proposed to offer learners customized courses in accordance with their aptitudes and learning results. For achieving the adaptive learning, a predefined concept map of a course is often used to provide adaptive learning guidance for learners. However, it is difficult and time consuming to create the concept map of a course. Thus, how to automatically create a concept map of a course becomes an interesting issue. In this paper, we propose a Two-Phase Concept Map Construction (TP-CMC) approach to automatically construct the concept map by learners' historical testing records. Phase 1 is used to preprocess the testing records; i.e., transform the numeric grade data, refine the testing records, and mine the association rules from input data. Phase 2 is used to transform the mined association rules into prerequisite relationships among learning concepts for creating the concept map. Therefore, in Phase 1, we apply Fuzzy Set Theory to transform the numeric testing records of learners into symbolic data, apply Education Theory to further refine it, and apply Data Mining approach to find its grade fuzzy association rules. Then, in Phase 2, based upon our observation in real learning situation, we use multiple rule types to further analyze the mined rules and then propose a heuristic algorithm to automatically construct the concept map. Finally, the Redundancy and Circularity of the concept map constructed are also discussed. Moreover, we also develop a prototype system of TP-CMC and then use the real testing records of students in junior high school to evaluate the results. The experimental results show that our proposed approach is workable.
104 citations