Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle

Chen, Chin‐Ming; Chung, Ching-Ju

doi:10.1016/j.compedu.2007.06.011

Cited by 352 publications

(187 citation statements)

References 15 publications

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“…Kim et al [18] summarize the educational benefits of m-learning as threefold: "(a) mobility, which is associated with the advantage of accessing information anytime, anywhere; (b) information management capacity, which is associated with the digitization of information and electronic-based management; and (c) beaming capability, which allows the sharing of files instantly and in real-time." The pedagogical impact of mobile applications has been evaluated in several different contexts, including language education [19][20][21], business education [8], and employee training programs [3,22]. With respect to science education, the pedagogical impact of mobile applications has been evaluated in primary education [23][24][25][26] and in some disciplines of science in higher education, including physics [17], and medicine [27].…”

Section: Contextmentioning

confidence: 99%

Student use and pedagogical impact of a mobile learning application

Teri

Acai

Griffith

et al. 2013

Biochem Molecular Bio Educ

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Mobile learning (m-learning) is a relevant innovation in teaching and learning in higher education. A mobile app called NutriBiochem was developed for use in biochemistry and nutrition education for students in a second year Biochemistry and Metabolism course. NutriBiochem was accessed through smartphones, tablets, or computers. Students were surveyed upon completion of the final exam (n 5 88). Survey questions assessed frequency of use, motivations for use, and perceptions of app usefulness. The pedagogical impact of NutriBiochem was evaluated by measuring the relationship between frequency of use and final course grade. Just over half of the students used the app, and 80% of users accessed the app moderately or infrequently. Smartphones were the most common device and the preferred device on which to access the app. There were no statistical differences in mean final grade between users and nonusers.Students with higher comfort levels with technology accessed the app more broadly than those with lower level of comfort with technology. Over 75% of students agreed that NutriBiochem was a useful learning tool, but fewer ( 45%) felt it helped them perform better in the course. The findings of this study are important, as they suggest that NutriBiochem is an effective study tool for students who are comfortable with technology, and access it regularly. Overall, the use of mobile applications in science education has been shown to be: 1) effective in enhancing students' learning experience; 2) relevant and important as an emergent method of learning given modern pressures facing higher education; and, 3) met with positive student attitudes and perceptions in terms of adopting and using such technology for educational purposes. V C 2013 by The International Union of Biochemistry and Molecular Biology, 42(2):121-135, 2014.

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Section: Contextmentioning

confidence: 99%

Student use and pedagogical impact of a mobile learning application

Teri

Acai

Griffith

et al. 2013

Biochem Molecular Bio Educ

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“…Folse (2004) asserts that it is a myth to say that learning vocabulary is not as important as learning grammar or other areas of language learning, when in reality vocabulary learning has an extremely important role in English language learning (Chen & Chun, 2008;Shoebottom, 2007). This is because the more words that students know, the more they are able to understand what they hear and read, hence the better they are able to say what they want when speaking or writing (Shoebottom, 2007).…”

Section: Importance Of Vocabulary Learningmentioning

confidence: 99%

Second Language Learners’ Attitudes towards the Methods of Learning Vocabulary

Ali¹,

Mukundan²,

Roselan³

et al. 2012

ELT

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The paper aims at investigating students' learning attitudes after they are exposed to three vocabulary learning methods, namely Contextual Clues, Dictionary Strategy and ComputerAssistedLanguageLearning (CALL). The study involves 123 undergraduates, who were surveyed to identify the factors that affect their attitudes in learning vocabulary using these methods. Findings from the study using ANOVA revealed that students who were exposed to CALL showed more positive attitudes in learning vocabulary compared to other methods. This is due to the tools matching their aptitude for learning. In fact, the tools are regarded as 'part and parcel' of the students' lives. The results of the study also showed that the usefulnessand limitation of the respective methods depend on students' approaches in using them to learn vocabulary. The implications for language teaching and learning of vocabulary demand practitioners to incorporate the use of computers in their lessons. Nonetheless, they may also integrate the use of Contextual Clues, Dictionary Strategy and CALL in their teaching to accelerate the expansion of students' vocabulary.

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“…Chen et al [10] also proposed a personalized mobile English vocabulary learning system based on Item Response Theory and learning memory cycles, which recommends appropriate English vocabulary for learning according to individual learner's vocabulary ability and memory cycle. Chen et al [11] also, present a personalized intelligent tutoring system to recommend courseware with suitable difficulty levels for learners and generate the content of courseware using fuzzy-based Item Response Theory.…”

Section: Special Focus Paper Towards Adaptive E-learning Using Decisimentioning

confidence: 99%

“…Where Pi() denotes the probability that the learner responds correctly to the i th item at a level below the ability level , Q i ()= 1-P i () represents the probability that the learner responds incorrectly to the i th item at a level below the ability level , u i =1 if the answer of i th is correct and u i =0 if the answer of i th is incorrect [10].The learner ability is updated based on test results learned from the item response theory.…”

Section: B Irt Analysermentioning

confidence: 99%

Towards Adaptive E-Learning using Decision Support Systems

Yarandi

Jahankhani

Tawil

2013

Int. J. Emerg. Technol. Learn.

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Abstract-The significance of personalization towards learners' needs has recently been agreed by all web-based instructional researchers. This study presents a novel ontology semantic-based approach to design an e-learning Decision Support System (DSS) which includes major adaptive features. The ontologically modelled learner, learning domain and content are separately designed to support personalized adaptive learning. The proposed system utilise captured learners' models during the registration phase to determine learners' characteristics. The system also tracks learner's activities and tests during the learning process. Test results are analysed according to the Item Response Theory in order to calculate learner's abilities. The learner model is updated based on the results of test and learner's abilities for use in the adaptation process. Updated learner models are used to generate different learning paths for individual learners. In this study, the proposed system is implemented on the "Fraction topic" of the mathematics domain. Experimental test results indicated that the proposed system improved learning effectiveness and learner's satisfaction, particularly in its adaptive capabilities.

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Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle

Cited by 352 publications

References 15 publications

Student use and pedagogical impact of a mobile learning application

Student use and pedagogical impact of a mobile learning application

Second Language Learners’ Attitudes towards the Methods of Learning Vocabulary

Towards Adaptive E-Learning using Decision Support Systems

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