Proactive and reactive engagement of artificial intelligence methods for education: a review

The education sector has benefited enormously through integrating digital technology driven tools and platforms. In recent years, artificial intelligence based methods are being considered as the next generation of technology that can enhance the experience of education for students, teachers, and administrative staff alike. The concurrent boom of necessary infrastructure, digitized data and general social awareness has propelled these efforts further. In this review article, we investigate how artificial intelligence, machine learning, and deep learning methods are being utilized to support the education process. We do this through the lens of a novel categorization approach. We consider the involvement of AI-driven methods in the education process in its entirety-from students admissions, course scheduling, and content generation in the proactive planning phase to knowledge delivery, performance assessment, and outcome prediction in the reactive execution phase. We outline and analyze the major research directions under proactive and reactive engagement of AI in education using a representative group of 195 original research articles published in the past two decades, i.e., 2003-2022. We discuss the paradigm shifts in the solution approaches proposed, particularly with respect to the choice of data and algorithms used over this time. We further discuss how the COVID-19 pandemic influenced this field of active development and the existing infrastructural challenges and ethical concerns pertaining to global adoption of artificial intelligence for education.

The impact of Danmaku-based and synchronous peer feedback on L2 oral performance: A mixed-method investigation

Advancement of research in education has propelled the augmentation of theoretical and practical knowledge in learning-oriented feedback. In recent years, the channels, modes, and orientations of feedback became manifold. Copious empirical evidence from the body of literature supported the strength of feedback in enhancing learning outcomes and promoting the motivation of learners. However, compared to the popularity in implementation and fruitfulness of findings in other educational domains, the application of state-of-the-art technology-enhanced feedback in fostering students' L2 oral abilities remain few and far between. To address the knowledge gap, the present study endeavored to investigate the effect of Danmaku-based and synchronous peer feedback on L2 oral performance and the acceptance thereof among students. Adopting a mixed-method design, the study recruited 74 (n = 74) undergraduate English majors from a Chinese university for a 16-week 2x2 experiment. The collected data were analyzed through statistical and thematic analysis respectively. The findings revealed that Danmaku-based and synchronous peer feed-back was impactful on students' performance in L2 oral production. Furthermore, the impacts of peer feedback on subdomains of L2 competence were statistically analyzed. Regarding students' perceptions, the incorporation of peer feedback was generally favored by participants who were satisfied and motivated in the learning process but lacked confidence in their assessment literacy. Furthermore, students expressed their agreement with the benefit of reflective learning and the subsequent enrichment in knowledge and horizon. The research was significant for its conceptual and practical contribution for follow-up researchers and educators in L2 education and learning-oriented feedback.

A Survey of Current Machine Learning Approaches to Student Free-Text Evaluation for Intelligent Tutoring

Recent years have seen increased interests in applying the latest technological innovations, including artificial intelligence (AI) and machine learning (ML), to the field of education. One of the main areas of interest to researchers is the use of ML to assist teachers in assessing students' work on the one hand and to promote effective self-tutoring on the other hand. In this paper, we present a survey of the latest ML approaches to the automated evaluation of students' natural language free-text, including both short answers to questions and full essays. Existing systematic literature reviews on the subject often emphasise an exhaustive and methodical study selection process and do not provide much detail on individual studies or a technical background to the task. In contrast, we present an accessible survey of the current state-of-the-art in student free-text evaluation and target a wider audience that is not necessarily familiar with the task or with ML-based text analysis in natural language processing (NLP). We motivate and contextualise the task from an application perspective, illustrate popular feature-based and neural model architectures and present a selection of the latest work in the area. We also remark on trends and challenges in the field.

Peer Feedback in Translation Training: A Quasi-Experiment in an Advanced Chinese-English Translation Course

Although research on peer feedback in second language teaching and learning has been developed from various perspectives over the past three decades, less is known about feedback in translation settings. This study reports the results of a quasi-experiment with advanced second language learners in a Chinese–English translation course. It examines how effective peer feedback is in improving the quality of translations. The following data were collected from 30 students: their initial translation drafts, the drafts with the feedback of their peers, and the final corrected translations. The whole process was facilitated by computer assistance and under anonymity. It was found that most students drew on direct or indirect corrective feedback while few students drew on metalinguistic corrective feedback. Text genres were also proved to impact the types and counts of peer feedback. An analysis of the accuracy rate of corrections after peer feedback showed that it had a positive impact on translation quality. The findings shed light on the applicability of peer feedback in other pedagogical activities.