A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning
TL;DR: A learning path recommendation model is designed for satisfying different learning needs based on the multidimensional knowledge graph framework, which can generate and recommend customized learning paths according to the e-learner’s target learning object.
Abstract: E-learners face a large amount of fragmented learning content during e-learning. How to extract and organize this learning content is the key to achieving the established learning target, especially for non-experts. Reasonably arranging the order of the learning objects to generate a well-defined learning path can help the e-learner complete the learning target efficiently and systematically. Currently, knowledge-graph-based learning path recommendation algorithms are attracting the attention of researchers in this field. However, these methods only connect learning objects using single relationships, which cannot generate diverse learning paths to satisfy different learning needs in practice. To overcome this challenge, this paper proposes a learning path recommendation model based on a multidimensional knowledge graph framework. The main contributions of this paper are as follows. Firstly, we have designed a multidimensional knowledge graph framework that separately stores learning objects organized in several classes. Then, we have proposed six main semantic relationships between learning objects in the knowledge graph. Secondly, a learning path recommendation model is designed for satisfying different learning needs based on the multidimensional knowledge graph framework, which can generate and recommend customized learning paths according to the e-learner’s target learning object. The experiment results indicate that the proposed model can generate and recommend qualified personalized learning paths to improve the learning experiences of e-learners.
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TL;DR: The TAM-based proposed scale has been successfully explained factors predicting the use of e-learning among Indonesian sport science students during the pandemic and the finding of significant relationships between facilitating condition and perceived ease of use and between facilitatingcondition and perceived usefulness was reported.
Abstract: This study was to explore factors predicting the use of e-learning during Corona Virus Disease 2019 (Covid-19) among sport science education students In Indonesia Higher Education Institutions (HEIs). The study was conducted through survey with 974 participating students from five Indonesian HEIs. An extended Technology Acceptance Model (TAM) with facilitating condition as the external factor was implemented to be the theoretical framework of this study. An analysis method through Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to measure and assess the proposed model. The findings informed that: (1) the TAM-based proposed scale has been successfully explained factors predicting the use of e-learning among Indonesian sport science students during the pandemic; (2) the finding of significant relationships between facilitating condition and perceived ease of use and between facilitating condition and perceived usefulness was reported; and (3) the significant relationships among core components of TAM were found except for one, relationship between perceived usefulness and attitude.
180 citations
Cites background from "A learning path recommendation mode..."
...Plethora studies have addressed the use of e-learning as the objects of research (e.g. Megahed and Mohammed, 2020; Kasraie and Kasraie, 2010; Pham et al., 2019; Ramírez-Correa et al.,. 2015; Shi et al., 2020)....
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TL;DR: This survey is the first to provide an inclusive definition to the notion of domain KG, and a comprehensive review of the state-of-the-art approaches drawn from academic works relevant to seven dissimilar domains of knowledge is provided.
Abstract: Knowledge Graphs (KGs) have made a qualitative leap and effected a real revolution in knowledge representation. This is leveraged by the underlying structure of the KG which underpins a better comprehension, reasoning and interpretation of knowledge for both human and machine. Therefore, KGs continue to be used as the main means of tackling a plethora of real-life problems in various domains. However, there is no consensus in regard to a plausible and inclusive definition of a domain-specific KG. Further, in conjunction with several limitations and deficiencies, various domain-specific KG construction approaches are far from perfect. This survey is the first to offer a comprehensive definition of a domain-specific KG. Also, the paper presents a thorough review of the state-of-the-art approaches drawn from academic works relevant to seven domains of knowledge. An examination of current approaches reveals a range of limitations and deficiencies. At the same time, uncharted territories on the research map are highlighted to tackle extant issues in the literature and point to directions for future research.
138 citations
TL;DR: The most significant challenges of the methods that are applied to personalize learning paths need to be tackled in order to enhance the quality of the personalization.
Abstract: A learning path is the implementation of a curriculum design. It consists of a set of learning activities that help users achieve particular learning goals. Personalizing these paths became a significant task due to differences in users’ limitations, backgrounds, goals, etc. Since the last decade, researchers have proposed a variety of learning path personalization methods using different techniques and approaches. In this paper, we present an overview of the methods that are applied to personalize learning paths as well as their advantages and disadvantages. The main parameters for personalizing learning paths are also described. In addition, we present approaches that are used to evaluate path personalization methods. Finally, we highlight the most significant challenges of these methods, which need to be tackled in order to enhance the quality of the personalization.
55 citations
TL;DR: A novel exercise recommendation method is proposed, which uses Recurrent Neural Networks (RNNs) to predict the coverage of knowledge concepts, and uses Deep Knowledge Tracing to predict students’ mastery level ofknowledge concepts based on the student’s exercise answer records.
Abstract: Good recommendation for difficulty exercises can effectively help to point the students/users in the right direction, and potentially empower their learning interests. It is however challenging to select the exercises with reasonable difficulty for students as they have different learning status and the size of exercise bank is quite large. The classic collaborative filtering (CF) based recommendation methods rely heavily on the similarities among students or exercises, leading to recommend exercises with mismatched difficulty. This paper proposes a novel exercise recommendation method, which uses Recurrent Neural Networks (RNNs) to predict the coverage of knowledge concepts, and uses Deep Knowledge Tracing (DKT) to predict students’ mastery level of knowledge concepts based on the student’s exercise answer records. The predictive results are utilized to filter the exercises; therefore, a subset of exercise bank is generated. As such, a complete list of recommended exercises can be obtained by solving an optimization problem. Extensive experimental studies show that our proposed approach has advantages over some existing baseline methods, not only in terms of the evaluation of difficulty of recommended exercises, but also the diversity and novelty of the recommendation lists.
27 citations
TL;DR: In this article, a multi-objective optimization model was proposed to generate an appropriate learning path for learners based on their background and job goals, which satisfies several learner criteria, such as the critical learning path, number of enrollments, learning duration, popularity, rating of previous learners and cost.
Abstract: Online learning platforms, such as Coursera, Edx, Udemy, etc., offer thousands of courses with different content. These courses are often of discrete content. It leads the learner not to find a learning path in a vast volume of courses and contents, especially when they have no experience in advance. Streamlining the order of courses to create a well-defined learning path can help e-learners achieve their learning goals effectively and systematically. The learners usually ask the necessary skills that they expect to earn (query). The need is to develop a recommender system that can search for suitable learning paths. This study proposes a multi-objective optimization model as a knowledge-based recommender. Our model can generate an appropriate learning path for learners based on their background and job goals. The recommended studying path satisfies several learner criteria, such as the critical learning path, number of enrollments, learning duration, popularity, rating of previous learners, and cost. We have developed Metaheuristic algorithms includes the Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), to solve the proposed model. Finally, we tested proposed methods with a dataset consisting of Coursera’s courses and Vietnam work’s jobs. The test results show the effectiveness of the proposed method.
22 citations
References
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TL;DR: While the kappa is one of the most commonly used statistics to test interrater reliability, it has limitations and levels for both kappa and percent agreement that should be demanded in healthcare studies are suggested.
Abstract: The kappa statistic is frequently used to test interrater reliability. The importance of rater reliability lies in the fact that it represents the extent to which the data collected in the study are correct representations of the variables measured. Measurement of the extent to which data collectors (raters) assign the same score to the same variable is called interrater reliability. While there have been a variety of methods to measure interrater reliability, traditionally it was measured as percent agreement, calculated as the number of agreement scores divided by the total number of scores. In 1960, Jacob Cohen critiqued use of percent agreement due to its inability to account for chance agreement. He introduced the Cohen's kappa, developed to account for the possibility that raters actually guess on at least some variables due to uncertainty. Like most correlation statistics, the kappa can range from -1 to +1. While the kappa is one of the most commonly used statistics to test interrater reliability, it has limitations. Judgments about what level of kappa should be acceptable for health research are questioned. Cohen's suggested interpretation may be too lenient for health related studies because it implies that a score as low as 0.41 might be acceptable. Kappa and percent agreement are compared, and levels for both kappa and percent agreement that should be demanded in healthcare studies are suggested.
9,097 citations
TL;DR: This paper used search engine data to forecast near-term values of economic indicators, such as automobile sales, unemployment claims, travel destination planning, and consumer confidence, and showed how to use this information to forecast future economic indicators.
Abstract: In this paper we show how to use search engine data to forecast near-term values of economic indicators. Examples include automobile sales, unemployment claims, travel destination planning and consumer confidence.
1,619 citations
Book•
16 Nov 2000TL;DR: E-Learning explains the basic principles of a comprehensive Web-based learning strategy -- how to link your organization's Web sites, Web- based training, courseware, and all the other components of online learning.
Abstract: From the Publisher:
It isn't just the promise of impressive technology that is driving people to e-learning. Business need to get rapidly changing information to large numbers of people faster than ever. They need to lower the overall costs of creating a workforce that performs faster and better than the competition, and they need to do this around the clock. It's no longer a question of whether organizations will one day implement online learning, but whether they will do it well.
Most organizations that need to train their employees are experimenting with some form of Web-delivered learning. But most organizations have focused on the technological challenges, buying the right software, getting enough bandwidth allocated for Web-based training, designing courseware, etc. These are important steps, but the larger strategic issues remain unsolved: how to make e-learning part of the daily work culture and fully implement its power. E-Learning is the first book in this exciting new field that addresses not just the technological challenges of Web-based training and knowledge management, but how to develop a comprehensive organization-wide learning strategy.
Author Marc Rosenberg discusses the technological issues but, more importantly, assesses the dramatic strategic, organizational, and political issues involved in the process of making e-learning a reality. Written for professionals responsible for leading the revolution in workplace learning, E-Learning takes a broad, strategic perspective on corporate learning. This wake-up call for executives everywhere discusses: Requirements for building a viable e-learning strategy; How e-learning will change the nature of training organizations; Knowledge management and other new forms of e-learning.
E-Learning explains the basic principles of a comprehensive Web-based learning strategy -- how to link your organization's Web sites, Web-based training, courseware, and all the other components of online learning. With an underlying focus on the "why" -- and not just the "how" -- Rosenberg provides a roadmap for growing and sustaining an e-learning culture that's based on his twenty years of observations, best (and worst) practices, and conversations with leaders in the learning technology fields. Divided into three parts, E-Learning offers an essential balance between building great e-learning (design and technology issues) and implementing it (acceptance and support issues). Within each chapter, examples illustrate many key components of an effective e-learning framework.
1,532 citations
TL;DR: Experimental results indicated that applying the proposed genetic-based personalized e-learning system for web-based learning is superior to the freely browsing learning mode because of high quality and concise learning path for individual learners.
Abstract: Personalized curriculum sequencing is an important research issue for web-based learning systems because no fixed learning paths will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized learning mechanisms to assist on-line web-based learning and adaptively provide learning paths in order to promote the learning performance of individual learners. However, most personalized e-learning systems usually neglect to consider if learner ability and the difficulty level of the recommended courseware are matched to each other while performing personalized learning services. Moreover, the problem of concept continuity of learning paths also needs to be considered while implementing personalized curriculum sequencing because smooth learning paths enhance the linked strength between learning concepts. Generally, inappropriate courseware leads to learner cognitive overload or disorientation during learning processes, thus reducing learning performance. Therefore, compared to the freely browsing learning mode without any personalized learning path guidance used in most web-based learning systems, this paper assesses whether the proposed genetic-based personalized e-learning system, which can generate appropriate learning paths according to the incorrect testing responses of an individual learner in a pre-test, provides benefits in terms of learning performance promotion while learning. Based on the results of pre-test, the proposed genetic-based personalized e-learning system can conduct personalized curriculum sequencing through simultaneously considering courseware difficulty level and the concept continuity of learning paths to support web-based learning. Experimental results indicated that applying the proposed genetic-based personalized e-learning system for web-based learning is superior to the freely browsing learning mode because of high quality and concise learning path for individual learners.
353 citations
TL;DR: This article analyzed the effect of team size and field and task variety on the creativity of research results and found that increasing team size has an inverted-U shaped relation with novelty, and that the size-novelty relationship is largely due to the relation between size and team field or task variety, consistent with the information processing perspective.
Abstract: The increasing dominance of team science highlights the importance of understanding the effects of team composition on the creativity of research results. In this paper, we analyze the effect of team size, and field and task variety on creativity. Furthermore, we unpack two facets of creativity in science: novelty and impact. We find that increasing team size has an inverted-U shaped relation with novelty. We also find that the size–novelty relationship is largely due to the relation between size and team field or task variety, consistent with the information processing perspective. On the other hand, team size has a continually increasing relation with the likelihood of a high-impact paper. Furthermore, variety does not have a direct effect on impact, net of novelty. This study develops our understanding of team science and highlights the need for a governance approach to scientific work. We also advance the creativity literature by providing an ex ante objective bibliometric measure that distinguishes novelty from impact, and illustrate the distinct team-level drivers of each. We conclude with a discussion of the policy implications of our findings.
207 citations