Contextual Diversity and Rule-based Adaptive E-learning System Scheme

Madhusudhana, Kalla

doi:10.3991/ijet.v10i5.4799

Cited by 3 publications

(3 citation statements)

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“…However, it might also lead to information overload and confusion to the students [14], especially to those students who previously had few contacts with APPs used for English learning as described above. Consequently, a reasonable choice of APPs that are alike is necessary.…”

Section: Discussionmentioning

confidence: 99%

“…Students' "addiction" to mobile phones has long been criticized in the educational setting of China [19]- [20] and mobile phones are strictly prohibited in classrooms in many universities in the country. However, owing to the evolution of mobile technology, mobile phones have become indispensable to modern society [14]. We should thus instruct students to reasonably and wisely use them rather than ban their uses.…”

Section: Discussionmentioning

confidence: 99%

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Mobile English Learning: An Empirical Study on an APP, English Fun Dubbing

Zhang

2016

Int. J. Emerg. Technol. Learn.

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Abstract-The availability of smart phones connected to mobile network and the occurrence of APPs developed for the educational purposes provide us with the possibility and feasibility of mobile teaching and learning. English Fun Dubbing, an APP designed for its users to practice oral English, was employed in this one-academic-year empirical study to evaluate the benefits of mobile APPs in the field of pedagogy. When the study ended, an anonymous online questionnaire was distributed to the 123 participants to survey the effects of English Fun Dubbing on their English language learning. The results showed that the majority of the respondents were satisfied with it in many aspects including its conveniences, flexibility, user-friendliness, rich materials, authentic language context, etc. and also its functions to inspire learning interests, foster learner autonomy, help realize personalized learning, and so on. We concluded that a reasonable and wise choice of APPs would not only be useful for students' English learning, but also help lead them to use mobile phones in a positive way.Index Terms-mobile English learning; APP; empirical study; anonymous online questionnaire I. INTRODUCTIONThe popularity of mobile devices such as iPods, iPads and smart phones has made mobile teaching and learning both possible and feasible. The employment of mobile devices for pedagogical purposes has long been a focus in the academic circles, starting in the 20 th century in the West and currently also prevailing in this century in China and other developing countries [1]- [3]. Mobile devices are in essence devices for ICTs (information and communication technologies). If teachers have a deepened understanding of information technology and if they can change their philosophy of teaching, the ICT-based teaching mode can be ensured [4]. Compared with traditional ICT devices like laptops and computers, mobile devices have transparent advantages due to their lighter weight, orientation flexibility, instant-on capacity, fast switching, mobility, long battery life and touch interface [5]- [6]. These features provide possibilities to implement ubiquitous, interactive, collaborative, informal, individualized, situated and self-regulated teaching and learning [7]-[8]. However, although some empirical studies on mobile teaching and learning have recently occurred, this kind of pedagogical mode is still rare, especially in the setting of higher education [9]- [11]. Consequently, we need more studies on mobile pedagogical strategies and how these strategies can contribute to student's learning process.The present study is based on "English Fun Dubbing" (EFD), an APP applicable to both iPhone and Android operating systems (see http://www.qupeiyin.com/ for more information). It was developed by a Chinese SciTech company to assist its users' oral English practices free of charge. As shown in Fig. 1 below, it provides abundant learning materials including movies, animations, short videos, classical textbooks and songs, all with native English speaker...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Mobile English Learning: An Empirical Study on an APP, English Fun Dubbing

Zhang

2016

Int. J. Emerg. Technol. Learn.

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“…• Pure technical contextualization: This category represents the opposite side of the contextualization approaches, in which the definition of the context is solely based on the devices and technological solutions used to capture learners' data. Papers in this category focus on the means of measuring contextual data through sensors and technological observations, and therefore define the context of learning directly from that angel, as one can see in Chorfi et al (2012), Gallego et al (2013), Hwang (2013), Niemann and Wolpers (2013), Qiuyan et al (2013), Ku (2014), Clarkes-Nias et al (2015), Madhu Sudhana (2015), Baccari andNeji (2016), andSupic (2016). Table 2 shows the distribution of surveyed papers over the context-origin categories.…”

Section: Context-factor Originsmentioning

confidence: 99%

Context based learning: a survey of contextual indicators for personalized and adaptive learning recommendations – a pedagogical and technical perspective

Abu-Rasheed,

Weber,

Fathi

2023

Front. Educ.

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Learning personalization has proven its effectiveness in enhancing learner performance. Therefore, modern digital learning platforms have been increasingly depending on recommendation systems to offer learners personalized suggestions of learning materials. Learners can utilize those recommendations to acquire certain skills for the labor market or for their formal education. Personalization can be based on several factors, such as personal preference, social connections or learning context. In an educational environment, the learning context plays an important role in generating sound recommendations, which not only fulfill the preferences of the learner, but also correspond to the pedagogical goals of the learning process. This is because a learning context describes the actual situation of the learner at the moment of requesting a learning recommendation. It provides information about the learner’s current state of knowledge, goal orientation, motivation, needs, available time, and other factors that reflect their status and may influence how learning recommendations are perceived and utilized. Context-aware recommender systems have the potential to reflect the logic that a learning expert may follow in recommending materials to students with respect to their status and needs. During the last decade, several approaches have emerged in the literature to define the learning context and the factors that may capture it. Those approaches led to different definitions of contextualized learner-profiles. In this paper, we review the state-of-the-art approaches for defining a user’s learning-context. We provide an overview of the definitions available, as well as the different factors that are considered when defining a context. Moreover, we further investigate the links between those factors and their pedagogical foundations in learning theories. We aim to provide a comprehensive understanding of contextualized learning from both pedagogical and technical points of view. By combining those two viewpoints, we aim to bridge a gap between both domains, in terms of contextualizing learning recommendations.

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A rule‐based self‐regulated learning assistance scheme to facilitate personalized learning with adaptive scaffoldings: A case study for learning computer software

2020

Comp Applic In Engineering

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Many studies indicate that most students find it difficult to learn effectively in computer‐based learning environments because of a lack of self‐regulated learning (SRL) abilities. Therefore, based on the design approach of the rule‐based system, this paper proposes a rule‐based self‐regulated learning assistance scheme (SRL‐RuAS) to intelligently facilitate personalized learning with SRL‐based adaptive scaffolding support for learning computer software. In the SRL‐RuAS scheme, SRL‐based adaptive scaffolding strategies (SRL‐AS) are defined according to SRL behaviors, the subject knowledge is formatted using the concept ontology technique, the relations of learning resources and the portfolio of students are modeled by the proposed learning analysis data model, and the learning analysis rules of adaptive scaffolding are defined according to SRL‐AS. Consequently, an intelligent learning environment with SRL‐RuAS can intelligently give learning support to actively assist students in understanding the learning status and guiding learning improvement. Hence, the learning process with SRL‐based assistance is able to improve learning motivation and effectiveness. Experimental results of a case study for learning computer software show that the SRL‐RuAS scheme is beneficial for learning achievement and the satisfaction of students, and its scaffolding strategies and application domain can thus be manageable and extended because of the rule‐based system design.

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Contextual Diversity and Rule-based Adaptive E-learning System Scheme

Cited by 3 publications

References 7 publications

Mobile English Learning: An Empirical Study on an APP, English Fun Dubbing

Mobile English Learning: An Empirical Study on an APP, English Fun Dubbing

Context based learning: a survey of contextual indicators for personalized and adaptive learning recommendations – a pedagogical and technical perspective

A rule‐based self‐regulated learning assistance scheme to facilitate personalized learning with adaptive scaffoldings: A case study for learning computer software

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