Attitudes and practices to improve english language skills through mobile applications: in the case of secondary schools english language teachers awi zone, Amhara Region, Ethiopia

Asratie, Mekuriaw Genanew; Aylet, Yibeltal Tadele; Alebachew, Metages Gebeyehu

doi:10.1007/s10639-022-10905-z

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Gamage and Lanka (2020) discovered that the use of GTM was still relevant, however, he suggested that to incorporate other methods or techniques to support GTM and make the teaching and learning processes more comprehensive. This is maybe because there is no practice when using GTM while the rule of learning a language is the students have to apply and practise the language (Asratie, Aylet, & Alebachew, 2022). This is where the use of technology in the process of teaching and learning of syntax comes in.…”

Section: Traditional Vs Computer-media Elements In Teaching and Learn...mentioning

confidence: 99%

Integrating ARCS Motivational Model with Computer-Based Syntax Learning

Shahabani

Jais

Khairuddin

et al. 2022

5007

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Motivation is still one of the factors that affected the teaching and learning of the English language. This study would like to investigate the students’ confidence level in syntax learning via media-based elements and to identify the factors that contribute to the level of students’ satisfaction in syntax learning via media-based elements. This study employed a quantitative approach and the instrument used was questionnaire. There were 75 participants who volunteered to participate. The results showed that the students’ level of confidence and satisfaction had increased after learning the syntax subject with the use of media-based elements in the lessons. The implication from the results obtained is for the stakeholders to help educators to be more creative and critical in planning their lessons. At the same time, the students would also be exposed to different types of lessons, and it would help them to be more interested in learning the subject.

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Section: Traditional Vs Computer-media Elements In Teaching and Learn...mentioning

confidence: 99%

Integrating ARCS Motivational Model with Computer-Based Syntax Learning

Shahabani

Jais

Khairuddin

et al. 2022

5007

View full text Add to dashboard Cite

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Evaluation model of students’ English learning ability based on fuzzy logic system

You

2024

IFS

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Students’ English learning ability depends on the knowledge and practice provided during the teaching sessions. Besides, language usage improves the self-ability to scale up the learning levels for professional communication. Therefore, the appraisal identification and ability estimation are expected to be consistent for different English learning levels. This paper introduces Performance Data-based Appraisal Identification Model (PDAIM) to support such reference. This proposed model is computed using fuzzy logic to identify learning level lags. The lag in performance and retains in scaling-up are identified using different fuzzification levels. The study suggests a fuzzy logic model pinpointing learning level gaps and consistently evaluating performance across various English learning levels. The PDAIM model gathers high and low degrees of variance in the learning process to give students flexible learning knowledge. Based on the student’s performance and capacity for knowledge retention, it enables scaling up the learning levels for professional communication. The performance measure in the model is adjusted to accommodate the student’s diverse grades within discernible assessment boundaries. This individualized method offers focused education and advancement to students’ unique requirements and skills. The model contains continuous normalization to enhance the fuzzification process by employing prior lags and retentions. Several indicators, including appraisal rate, lag detection, number of retentions, data analysis rate, and analysis time, are used to validate the PDAIM model’s performance. The model may adjust to the various performance levels and offer pertinent feedback using fuzzification. The high and low variation levels in the learning process are accumulated to provide adaptable learning knowledge to the students. Therefore, the performance measure is modified to fit the student’s various grades under distinguishable appraisal limits. If a consistent appraisal level from the fuzzification is observed for continuous sessions, then the learning is scaled up to the next level, failing, which results in retention. This proposed model occupies constant normalization for improving the fuzzification using previous lags and retentions. Hence the performance of this model is validated using appraisal rate, lag detection, number of retentions, data analysis rate, and analysis time.

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Attitudes and practices to improve english language skills through mobile applications: in the case of secondary schools english language teachers awi zone, Amhara Region, Ethiopia

Cited by 2 publications

References 18 publications

Integrating ARCS Motivational Model with Computer-Based Syntax Learning

Integrating ARCS Motivational Model with Computer-Based Syntax Learning

Evaluation model of students’ English learning ability based on fuzzy logic system

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