Enhancement of student learning performance using personalized diagnosis and remedial learning system

Chen, Ling-Hsiu

doi:10.1016/j.compedu.2010.07.015

Cited by 93 publications

(50 citation statements)

References 35 publications

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“…These steps have been implemented in the form of modules while designing the system architectures. Typical examples of learning theories used for learning system construction are Item Analysis for Norm Referencing [52], Activity Attention Network [49] and Theory of Meaningful Learning [53]. In this context no difference was observed between P-Learning and C-Learning environment.…”

Section: Summary Of Edm Algorithmic Aspectsmentioning

confidence: 99%

“…For both P-Learning and C-Learning environments the efficiency is determined by conducting tests before and after the learning process and applying certain statistical algorithms on these test results. Statistical algorithms include t-tests, ANOVA and multivariate regression analysis [33,35,50,52,65]. A survey is also used to evaluate learner satisfaction.…”

Section: Summary Of Edm Algorithmic Aspectsmentioning

confidence: 99%

“…Extensive use of learning theory for generating architectural modules [49,52,53] Similar procedure used [61] 4.…”

Section: Use Of Learning Theorymentioning

confidence: 99%

“…Focus Area of the developed application Diagnostic and remedial Learning [33,34,49,50,65] Learning a subject using discussion [57,59,60] 7. Efficiency of the algorithms in terms of learning result Regression analysis to estimate the degree of significance between pretest and post test marks; surveys used to estimate learner satisfaction [33,35,50,52,65] Similar procedure used [35,37,59,60]. Statistical algorithms used in evaluating collaboration dynamics [61] Secondly, as indicated in the introductory section, KDD process contains multiple steps like data cleaning, integration, selection, transformation, mining, pattern evaluation and knowledge presentation [2].…”

Section: Cycle Removal Algorithmmentioning

confidence: 99%

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An Educational Data Mining Approach to Concept Map Construction for Web based Learning

Acharya¹,

Sinha²

2017

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Section: Summary Of Edm Algorithmic Aspectsmentioning

confidence: 99%

Section: Summary Of Edm Algorithmic Aspectsmentioning

confidence: 99%

“…Extensive use of learning theory for generating architectural modules [49,52,53] Similar procedure used [61] 4.…”

Section: Use Of Learning Theorymentioning

confidence: 99%

Section: Cycle Removal Algorithmmentioning

confidence: 99%

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An Educational Data Mining Approach to Concept Map Construction for Web based Learning

Acharya¹,

Sinha²

2017

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“…In the second approach to obtaining proximity data, students are asked to prepare a document (e.g., write an essay) without a predefined set of concepts (Chen 2011, Cooke, Neville & Rowe 1996, Davis, Curtis & Tschetter 2003, DeChurch & Mesmer-Magnus 2010, Goldsmith et al 1991. Proximity data can then be computed from the document using the Analysis of Lexical Aggregates (ALA-Reader) software (Koul, Clariana & Salehi 2005).…”

Section: Obtaining Proximity Datamentioning

confidence: 99%

Exploring the Mobile Structural Assessment Tool: Concept Maps for Learning Website

Filiz

Trumpower

Vanapalli

2014

Rev. colomb. estad.

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In this paper, we describe how the pathfinder algorithm converts relatedness ratings of concept pairs to concept maps; we also present how this algorithm has been used to develop the Concept Maps for Learning website (www.conceptmapsforlearning.com) based on the principles of effective formative assessment. The pathfinder networks, one of the network representation tools, claim to help more students memorize and recall the relations between concepts than spatial representation tools (such as MultiDimensional Scaling). Therefore, the pathfinder networks have been used in various studies on knowledge structures, including identifying students' misconceptions. To accomplish this, each student's knowledge map and the expert knowledge map are compared via the pathfinder software, and the differences between these maps are highlighted. After misconceptions are identified, the pathfinder software fails to provide any feedback on these misconceptions. To overcome this weakness, we have been developing a mobile-based concept mapping tool providing visual, textual and remedial feedback (ex. videos, website links and applets) on the concept relations. This information is then placed on the expert concept map, but not on the student's concept map. Additionally, students are asked to note what they understand from given feedback, and given the opportunity to revise their knowledge maps after receiving various types of feedback. ResumenEn este artículo se describe cómo el algoritmo de búsqueda de ruta convierte puntajes de conceptos pareados en mapas conceptuales. También se presenta cómo este algoritmo ha sido utilizado para desarrollar estos mapas conceptuales para aprendizaje (www.conceptmapsforlearning.com) basados en los principios del aseguramiento formativo efectivo.Las redes de búsqueda de ruta, una de las herramientas de representación de redes, ayudan a memorizar a los estudiantes y enunciar las relaciones entre mapas más que las herramientas de expresión espacial (tales como el escalonamiento multidimensional). Por tanto, las redes de búsqueda de rutas han sido usadas en varios estudios de estructura del conocimiento incluyendo la identificación de malos conceptos usados por los estudiantes. Para lograr esto, cada mapa de conocimiento tanto del estudiante como del experto son comparados vía el software de búsqueda de ruta y se remarcan las diferencias entre éstos. Después que los malos conceptos son identificados, el software de búsqueda falla en entregar una retroalimentación en estos nodos conceptuales. Para superar esta debilidad, se desarrolla una herramienta de mapa conceptual móvil que manda retroalimentaciones visuales, textuales y remediales (e.g. vídeos, enlaces a páginas web y applets) en las relaciones de los conceptos. Adicionalmente, los estudiantes son preguntados acerca de qué entienden de la retroalimentación brindada y se les da la oportunidad de revisar sus mapas de conocimiento después de recibir varios tipos de retroalimentación.Palabras clave: aseguramiento estructural, mapas conceptua...

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Building an efficient artificial intelligence model for personalized training in colleges and universities

Xiao

2020

Comp Applic In Engineering

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Higher education provides a common educational pattern to all students in colleges and universities. However, under the general law of higher education, the teaching and education of a certain category or subject has its special regularity. Besides, students in colleges and universities have different demands of education. To address the challenges in higher education, it is very important to carry out higher education reform in colleges and universities. Personalized education has been considered to be a new educational model that is a result of the individual desire of students and the development of society. Traditional methodologies of teaching in colleges and universities cannot fulfill the implementation of personalized training. Hence, it is very urgent to develop new methodologies for personalized training. Among the methodologies of realization of personalized training, artificial intelligence is one of the most important methodologies. We exploit artificial intelligence for personalize education reform. First, we analyze the information of students before entering the colleges and universities. Then, we propose a method to extract modeling features of the student information. Second, we propose a method to build a personalized training model based on artificial intelligence. Third, we propose a method to predict the development track of students based on the personalized training model. On the basis of above designs, we design personalized training for students in colleges and universities. Furthermore, we implement our design by using artificial intelligence. Besides, our design can be applied to the career planning or related areas.

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Enhancement of student learning performance using personalized diagnosis and remedial learning system

Cited by 93 publications

References 35 publications

An Educational Data Mining Approach to Concept Map Construction for Web based Learning

An Educational Data Mining Approach to Concept Map Construction for Web based Learning

Exploring the Mobile Structural Assessment Tool: Concept Maps for Learning Website

Building an efficient artificial intelligence model for personalized training in colleges and universities

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