Personalized feedback for self assessment in lifelong learning environments based on semantic web

Belcadhi, Lilia Cheniti

doi:10.1016/j.chb.2015.07.042

Cited by 31 publications

(8 citation statements)

References 10 publications

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“…Second, the AAUKG approach can automatically construct knowledge graphs and provide personalised feedback in real time, which is an efficient method to improve collaborative learning performance. Personalised feedback is more effective and efficient than a one‐size‐fits‐all approach (Deeva et al, 2021), and it is considered an important factor for knowledge gains (Belcadhi, 2016). Hence, adopting the AAUKG approach to improve CSCL performance is strongly recommended.…”

Section: Discussionmentioning

confidence: 99%

An automatic knowledge graph construction approach to promoting collaborative knowledge building, group performance, social interaction and socially shared regulation in CSCL

Zheng

Niu

Long

et al. 2022

Brit J Educational Tech

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Computer‐supported collaborative learning (CSCL) has been an effective pedagogy in the field of education. However, productive collaborative learning often does not occur spontaneously, and learners often have difficulties with collaborative knowledge building and socially shared regulation. To address this research gap, this study proposes an automatic knowledge graph construction approach based on deep neural network models. In total, 63 groups comprising 189 college students participated in this study and were assigned to three conditions, namely, the automatic activated and unactivated knowledge graph (AAUKG) condition, the automated activated knowledge graph (AAKG) condition, and the traditional online collaborative learning (TOCL) condition. The findings revealed that the AAUKG approach had more significant and positive impacts on collaborative knowledge building, group performance, social interaction, and socially shared regulation than the AAKG and TOCL approaches. This study provides substantial evidence of utilising both automatically activated and unactivated knowledge graphs to improve collaborative learning performance. What is already known about this topic Knowledge graphs have been widely used in many fields for searches, recommendations, analytics, and automation. Researchers have explored how to construct knowledge graphs. However, few studies have automatically constructed knowledge graphs in the CSCL context. What this paper adds An automatic knowledge graph construction approach is proposed and validated in the CSCL context. The findings indicate that the AAUKG approach has more significant and positive impacts on collaborative knowledge building, group performance, social interaction, and socially shared regulation than the AAKG and TOCL approaches. Implications for practice and/or policy The AAUKG approach is an effective method for improving knowledge building and group performance in the CSCL context. Researchers and practitioners are encouraged to consider how knowledge graphs can be applied to facilitate collaborative learning efficiently.

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Section: Discussionmentioning

confidence: 99%

An automatic knowledge graph construction approach to promoting collaborative knowledge building, group performance, social interaction and socially shared regulation in CSCL

Zheng

Niu

Long

et al. 2022

Brit J Educational Tech

View full text Add to dashboard Cite

show abstract

“…In the educational environment, assessment feedback is a widely researched field (Belcadhi, 2016;Evans, 2013;Krause et al, 2009;Miller, 2009). The term broadly encompasses assessment design in a learning environment so that feedback relating to the posed questions is created at some stage during assessment (Evans, 2013).…”

Section: Related Workmentioning

confidence: 99%

“…To evaluate the proposed progress feedback framework, the properties of effective feedback need to be investigated. Researchers generally agree on the conditions under which assessment feedback has a positive effect on learning (Belcadhi, 2016;Evans, 2013;Krause et al, 2009;Rubel and Jones, 2016;Wiggins, 2012). Academic progress feedback differs from assessment feedback in that it is based on overall student performance in a module, rather than on individual assignments.…”

Section: Related Workmentioning

confidence: 99%

“…Feedback in learning comprises information that a lecturer or teacher provides a learner with, relating to accomplishment or content understanding (Hattie and Timperley, 2007). Although academic performance feedback and its effect on learning have been extensively researched in recent years (Belcadhi, 2016;Semley et al, 2016;Stegemann and Malan, 2016), students' learning methods have started to change. Specifically, computer and internet use in learning are increasingly being adopted (Uğur and Turan, 2018).…”

Section: Introductionmentioning

confidence: 99%

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A Framework for Computerized Progress Feedback in Tertiary Education

Merwe¹,

Toit²,

Kruger³

2020

Journal of Computer Science

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The dawn of democracy in South Africa in 1994 has resulted in challenges in many fields, especially in education. University enrolment rates are high and the output quality of school learners in general is delicate which threatens the graduate quality of tertiary institutions. Contrary to rising student enrolments, the attrition rates have not improved. One contributing factor is the lack of comprehensive insight into students' academic performance throughout the course of a program. New systems are being developed and implemented in both online and on-campus programs and can be utilized to improve student performance feedback. Prior studies served to identify the ideal elements, models and concepts necessary to develop a framework for academic progress feedback in the tertiary education environment. In this paper, a model-based academic progress feedback framework is proposed to improve learning. The framework was implemented in a case study at a South African university and evaluated using a survey. The framework was readily accepted by the end-users and conformed to the features and requirements identified in literature.

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“…The process of combining UMs and content based on a single ontology across multiple applications has been shown to be feasible. This is seen especially in various applications, such as learning environments [27] [28], flexible and reusable user interfaces [29], and social network-based user modeling [30]. Some attempts were even focused on building a complete adaptive application based on ontology and defining rules and inferences on such rules on a semantic level [31].…”

Section: Ontologies For Web Personalizationmentioning

confidence: 99%

Web personalization issues in big data and Semantic Web: challenges andopportunities

Raufi¹,

Ismaili²,

Ajdari³

et al. 2019

Turk J Elec Eng & Comp Sci

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Web personalization is a process that utilizes a set of methods, techniques, and actions for adapting the linking structure of an information space or its content or both to user interaction preferences. The aim of personalization is to enhance the user experience by retrieving relevant resources and presenting them in a meaningful fashion. The advent of big data introduced new challenges that locate user modeling and personalization community in a new research setting. In this paper, we introduce the research challenges related to Web personalization analyzed in the context of big data and the Semantic Web. This paper also introduces some models and approaches that can bridge the gap between the two. Future challenges and opportunities related to Web personalization, analyzed from the big data and Semantic Web perspective, are also presented. The research challenges outlined in this paper involve the scrutability of user models in personalization, generic personalization, meta-personalization, open corpus personalization, and semantic data modeling.

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Personalized feedback for self assessment in lifelong learning environments based on semantic web

Cited by 31 publications

References 10 publications

An automatic knowledge graph construction approach to promoting collaborative knowledge building, group performance, social interaction and socially shared regulation in CSCL

An automatic knowledge graph construction approach to promoting collaborative knowledge building, group performance, social interaction and socially shared regulation in CSCL

A Framework for Computerized Progress Feedback in Tertiary Education

Web personalization issues in big data and Semantic Web: challenges andopportunities

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