Collaborative filtering for expansion of learner’s background knowledge in online language learning: does “top-down” processing improve vocabulary proficiency?

Yamada, Masanori; Kitamura, Satoshi; Matsukawa, Hideya; Misono, Tadashi; Kitani, Noriko; Yamauchi, Yuhei

doi:10.1007/s11423-014-9344-7

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…In their study of the effectiveness of collaborative filtering on learners' English vocabulary, Yamada et al (2014) emphasized that their model enabled learners to read articles and score higher on topical vocabulary tests. This 2-month long study showed that with their collaborative model, learners were able to expand their interests while also learning English vocabulary.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

MALL in Collaborative Learning as a Vocabulary-Enhancing Tool for EFL Learners: A Study Across Two Universities in Saudi Arabia

Al-Ahdal

Alharbi

2021

Sage Open

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Vocabulary, as important as it is, is largely relegated to the domain of memorization in the English as a foreign language (EFL) situations in the Kingdom of Saudi Arabia (KSA). Assessment and achievement tests have proven time and again that this strategy is not doing any good for the learners’ proficiency. This study was conceived to suggest means of improving critical knowledge application. In pilot studies with intermediate EFL learners at Majmaah University and Qassim University, preliminary findings after using mobile-assisted language learning (MALL) as a tool of collaborative learning indicated improved vocabulary retention. The study employed a mixed-method approach by comparing pre- and posttest results across genders and obtaining direct teacher–learner feedback using questionnaires, with 80 participants from two Universities over a period of 3 months. The questions were centered around seeking their opinion on collaborative learning and using smart devices as study tools on the campuses. Results showed that, the experimental group’s use of mobile devices for collaboration helped them for better retention of vocabulary, postintervention, and group performance was improved drastically with more learners scoring closer to the mean value, while the control group showed no remarkable difference in performance.

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Section: Theoretical Frameworkmentioning

confidence: 99%

MALL in Collaborative Learning as a Vocabulary-Enhancing Tool for EFL Learners: A Study Across Two Universities in Saudi Arabia

Al-Ahdal

Alharbi

2021

Sage Open

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“…A learner can demonstrate the acquisition of knowledge and skills as a product of their learning process. Most key studies in this category operationalize affected learning through grades or test scores or scores [29], [33]- [35], [37], [49], [50], [53], [54], [57], [61], [63], [67]- [70], [74]- [76], which is a direct assessment of learning as performance on a task (e.g., an exam or final test) [94]. Although grades may seem to be a direct operational definition, this is debatable.…”

Section: Analysis and Refinement Of The Classification Schemementioning

confidence: 99%

Where is the Learning in Learning Analytics? A Systematic Literature Review on the Operationalization of Learning-Related Constructs in the Evaluation of Learning Analytics Interventions

Knobbout

Stappen

2020

IEEE Trans. Learning Technol.

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Learning technologies enable interventions in the learning process aiming to improve learning. Learning analytics provides such interventions based on analysis of learner data, which are believed to have beneficial effects on both learning and the learning environment. Literature reporting on the effects of learning analytics interventions on learning allows us to assess in what way learning analytics improves learning. No standard set of operational definitions for learning affected by learning analytics interventions is available. We performed a systematic literature review of 1932 search hits, which yielded 62 key studies. We analyzed how affected learning was operationalized in these key studies and classified operational definitions into three categories: 1) learning environment; 2) learning process; and 3) learning outcome. A deepening analysis yielded a refined classification scheme with 11 subcategories. Most of the analyzed studies relate to either learning outcome or learning process. Only nine of the key studies relate to more than one category. Given the complex nature of applying learning analytics interventions in practice, measuring the effects on a wider spectrum of aspects can give more insight into the workings of learning analytics interventions on the different actors, processes, and outcomes involved. Based on the results of our review, we recommend making deliberate decisions on the (multiple) aspects of learning one tries to improve by applying learning analytics. Our refined classification with examples of operational definitions may help both academics and practitioners doing so, as it allows for a more structured, grounded, and comparable positioning of learning analytics benefits.

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“…Researchers have been applying these resources to learning systems to clarify the effects of recommender systems for learning (e.g., Liang et al, 2006;Chen et al, 2007;Yamada et al, 2014). Many techniques for a recommender system have also been proposed.…”

Section: Introductionmentioning

confidence: 99%

K-Tips: Knowledge Extension Based on Tailor-Made Information Provision System

Nakayama

Shimada²,

Minematsu³

et al. 2019

Proceedings of the 16th International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2019)

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Thanks to an increase in the amount of information on the Internet and the spread of ICT-supported educational environments, much attention has been paid to learning support based on "smart" recommendation technologies. In this study, we propose an education improvement model based on the recommender system using the human-in-the-loop design strategy. Our proposed model enhances not only learners via recommendation, but also teachers and the system itself through the interaction between teachers and the system. In this paper, we introduce the details of the proposed model and implementation strategy followed by a report of preliminary experimental results.

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Collaborative filtering for expansion of learner’s background knowledge in online language learning: does “top-down” processing improve vocabulary proficiency?

Cited by 7 publications

References 33 publications

MALL in Collaborative Learning as a Vocabulary-Enhancing Tool for EFL Learners: A Study Across Two Universities in Saudi Arabia

MALL in Collaborative Learning as a Vocabulary-Enhancing Tool for EFL Learners: A Study Across Two Universities in Saudi Arabia

Where is the Learning in Learning Analytics? A Systematic Literature Review on the Operationalization of Learning-Related Constructs in the Evaluation of Learning Analytics Interventions

K-Tips: Knowledge Extension Based on Tailor-Made Information Provision System

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