Collaborating with Mature English Language Learners to Combine Peer and Automated Feedback: a User-Centered Approach to Designing Writing Support

Liaqat, Amna; Munteanu, Cosmin; Epp, Carrie Demmans

doi:10.1007/s40593-020-00204-4

Cited by 17 publications

(6 citation statements)

References 92 publications

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“…However, the fact that such programmed responses are not a determinant of personal dissimilarity allows the shortcoming not to be established by instructional factors rather technical abilities [53]. Liaqat, et al [54], demonstrated how automatic feedback could meet English language learners' needs and practices. The study discovered a strong relationship between learning strategy and epistemic belief.…”

Section: ) What Technologies Were Utilized By the Scholars In The Pre...mentioning

confidence: 99%

“…Highlighting the importance of communication skills, the collaborative learning model has been strongly encouraged to enhance the students' interaction rate and language usage. Liaqat, et al [54] in their study on collaboration with proficient English language learners, in conjunction with the integration of both peer and automated feedback, revealed that engaging in collaborative learning led to a decrease in students' anxiety levels as opposed to individual learning. The utilization of technology-assisted language learning underscores its potential to advocate for a collaborative learning approach, presenting educational benefits in the realms of English as a foreign language and English as a second language instructional methodology.…”

Section: E Collaborative Learning Modelsmentioning

confidence: 99%

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Technology-Assisted Language Learning Systems: A Systematic Literature Review

Buddha,

Shuib,

Idris

et al. 2024

IEEE Access

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This study provides a systematic review of technology-assisted language learning. This study provides a summary content of the reviewed articles in the aspects of technology usage, language, and learning skills, and the benefits offered by technology in language learning. The study focused on the published articles between 2012 and 2022. Out of 5719 articles initially retrieved from five academic databases and reviewed, twenty-seven (27) research articles were selected. Based on the review findings, the most used technology is the intelligent system (n=7). The study also revealed that the most common target language is English (n=22), whereas skills such as vocabulary, writing, and grammar gained the most attention in the selected studies. The review also identified and analyzed the empirical evidence on the benefits of technology in language learning, such as language performance development, motivation, metacognitive skills, positive attitudes towards learning, enhancement of students' learning retention, collaborative learning model, and extensive learning opportunity. Barriers to the implementation of the technology, such as learning anxiety, insufficient technology literacy, and technical limitations, were also recognized, and some suggestions were provided to overcome those barriers. Thus, this review can be used as a guide for educators and researchers who intend to design technology-assisted language learning and teaching in the future.

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Section: ) What Technologies Were Utilized By the Scholars In The Pre...mentioning

confidence: 99%

Section: E Collaborative Learning Modelsmentioning

confidence: 99%

Technology-Assisted Language Learning Systems: A Systematic Literature Review

Buddha,

Shuib,

Idris

et al. 2024

IEEE Access

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“…Prior work has researched writers' trust in feedback created by humans and machines. For example, Liaqat et al [44] studied the confidence of English language learners in the feedback to English essay writing provided by instructors, peers, and machines. They found that English language learners accept machine feedback significantly more than peer feedback.…”

Section: 22mentioning

confidence: 99%

Experiments of arsenic (V) adsorption on birnessite: implications on arsenic cycling

Huang¹,

Yang²,

Huang³

et al. 2023

Preprint

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<p>Birnessite occurs as a major manganese oxide in sediments. It is characterized by a high adsorption capacity for trace elements, including arsenic. However, the effects of birnessite on arsenic cycling were less intensively investigated than that of goethite, a commonly recognized arsenic host. Therefore, this study utilizes arsenic-bearing solutions containing 0.1&#8211;50 ppm arsenic to synthesize birnessite and uses arsenic sequential extraction procedure (SEP) analysis to quantify its arsenic co-precipitation and adsorption capacity.<br />&#160; &#160; SEPs showed that adsorbed/structural arsenic ratio grew from 0:100 to 60:40, with arsenic concentration increasing from 0.1&#8211;50 ppm, implying saturation of structural arsenic. SEPs quantify the adsorbed and structural As of birnessite being 0.831 and 1.308 mg/g, respectively. However, the low arsenic concentrations of < 1% in residual solutions indicate nonattainment of the maximum adsorption capacity. Subsequent adsorption experiments using higher initial arsenic concentrations reaching 250 ppm determined the maximum arsenic adsorption capacity to be 19.81 mg/g at pH 7, comparable to the values of 15.3&#8211;22.5 mg/g at pH 6.5 (Manning et al., 2002; Singh et al., 2010) and that of 3.79&#8211;15.73 mg/g for goethite. It then appears that birnessite adsorption/desorption is more effective than precipitation/dissolution in controlling arsenic cycling. However, the consideration based on the maximum adsorption capacity might overrate the controls of adsorption/desorption relative to precipitation/dissolution because the natural water generally contains less arsenic than that used for the adsorption experiments. Therefore, we conducted adsorption experiments with low arsenic concentrations of 0.5 ppm and a solid/liquid ratio of 0.01 to 0.001. The results showed that adsorption capacity rose abruptly to 1.416 mg/g in two weeks and then slowly increased to 1.523 mg/g after three months. This feature indicated incomplete filling of the adsorption sites on the surface of birnessite at low As concentration despite its large specific surface area. Another controlling factor was the abundance of birnessite in sediments. If 10% of MnO (0.05&#8211;0.15%) in the Chianan sediments in southern Taiwan occurred as birnessite, the adsorbed arsenic was calculated to be 0.0005&#8211;0.022 mg, corresponding to < 2 % of adsorbed arsenic in 1 g sediments, based on the SEP data of Yang et al. (2016). In the extreme case that all the Mn occurred as birnessite, birnessite could account for up to 25% of the adsorbed arsenic. Apparently, birnessite is not a major contributor to surface arsenic cycling. However, this inference must be evaluated by considering the adsorption and co-precipitation data from goethite and goethite proportions in sediments.</p>

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“…Direct corrective feedback, such as grammatical error correction, helps language learners improve their writing proficiency (Liaqat et al, 2020;Sheen, 2007). In addition to that, feedback that contrasts erroneous utterances with correct ones facilitates the acquisition of accurate language structures.…”

Section: Writing Feedbackmentioning

confidence: 99%

Negative language transfer in learner English: A new dataset

Wanderley¹,

Zhao²,

Epp³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Automatic personalized corrective feedback can help language learners from different backgrounds better acquire a new language. This paper introduces a learner English dataset in which learner errors are accompanied by information about possible error sources. This dataset contains manually annotated error causes for learner writing errors. These causes tie learner mistakes to structures from their first languages, when the rules in English and in the first language diverge. This new dataset will enable second language acquisition researchers to computationally analyze a large quantity of learner errors that are related to language transfer from the learners' first language. The dataset can also be applied in personalizing grammatical error correction systems according to the learners' first language and in providing feedback that is informed by the cause of an error.

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Collaborating with Mature English Language Learners to Combine Peer and Automated Feedback: a User-Centered Approach to Designing Writing Support

Cited by 17 publications

References 92 publications

Technology-Assisted Language Learning Systems: A Systematic Literature Review

Technology-Assisted Language Learning Systems: A Systematic Literature Review

Experiments of arsenic (V) adsorption on birnessite: implications on arsenic cycling

Negative language transfer in learner English: A new dataset

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