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Despite the growing interest in investigating the pedagogical application of Automated Writing Evaluation (AWE) systems, studies on the process of AWE-supported writing are still scant. Adopting activity theory as the framework, this qualitative study aims to examine how students incorporated AWE feedback into their writing in an English as a foreign language setting. We conducted semi-structured interviews with four Chinese students sampled from two classes and collected their AWE submissions and feedback for data analysis. Our findings demonstrate that AWE-supported writing is a tool-mediated, purposive, and collective activity shaped by individual and contextual factors. Students used various strategies to attain their learning goals and to address the tensions arising from their activity systems. This study contributes to the research on the effectiveness of AWE by assuming a process-oriented approach that was informed by activity theory. Our findings also shed light on the complex process of second language writing mediated by new technology innovations. Pedagogical implications of our findings are discussed in the conclusion.

Purpose: In intelligent tutoring systems (ITS), learners were often granted limited authority and are forced to obey the decision of the system which might not satisfy their needs. Failure to grant learners sufficient autonomy could yield unexpected effects that hinder learning, including undermining learners' motivation, priming learners' aversion to the algorithm. On the contrary, granting learners overwhelming autonomy could also be harmful as the absence of learning support would also have a negative impact on learning. As such, this study aims to design and implement an intelligent tutoring system that offers learners proper autonomy. Design/methodology/approach: The main learning activity in the system is doing exercises, and by finishing exercises learners could earn virtual coins. Based on item response theory, exercises are administered to learners with proper difficulty. Based on a recommended difficulty parameter predicted by the system, learners could manually modify the difficulty of the exercises, they could earn more credits by finishing more challenging exercises. Meanwhile, a pedagogical agent is embedded. Learners could customize the agent's personality jointly with the system to create the learning context they prefer. Findings: A intelligent tutoring system with proper learner autonomy (LA) is designed and implemented. Originality/value: Few previous researches have noticed the potentially important role that LA plays in ITS. Learning might be facilitated using such a design.

In collaborative learning, the intuition "the more device, the merrier" is somehow widely acknowledged, but little research has investigated the relationship between device-student ratio and the learning outcome. This study aims to investigate not only the main effect of different device-student ratio, also to identify the moderators in the learning context including task complexity, external script availability and students' familiarity to the collaboration settings. A three-round quasi-experiment was conducted in a primary school in mainland China, 130 fifth-grade students from four classes participated. Group worksheet including conceptual understanding and problem-solving tasks were used to collect participants' inquiry performance. Repeated measures ANOVA was employed in data analysis. Findings indicate that 1:m device-student ratio could be beneficial, and external scripts, and prior collaboration experience could moderate such effect. The different effect of 1:m device-student ratio to 1:1 is only significant in the situation when students are faced with relatively simple task, and the effect size is larger when external script is present. When the task is more complicated, such effect of device-student ratio would only emerge after a period of collaboration. This finding challenged the intuition that one-to-one device-student ratio could be better. Related discussions and recommendations to teaching were made.

Background: Learners' writing skills are critical to their academic and professional development. Previous studies have shown that learners' self‐assessment during writing is essential for assessing their writing products and monitoring their writing processes. However, conducting practical self‐assessments of writing remains challenging for learners without help, such as formative feedback. Objectives: To facilitate learners' self‐assessment in writing, we developed a writing analytics toolkit and used data visualisation and cutting‐edge machine learning technology that provides real‐time and formative feedback to learners. Methods: To investigate whether our newly‐developed tool affects the accuracy and process of learners' self‐assessment, we conducted a lab study. We assigned 59 learners to complete writing (2 h) and revising (1 h) tasks. During the revision stage, we randomly assigned the learners to two groups: one group used the writing analytics toolkit while the second group was not granted access to the toolkit. Learners' self‐assessment accuracy and process of self‐assessment were compared between the two groups. Results: In our study, we found the toolkit helped learners in the experimental group improve the self‐assessment accuracy of their writing products compared to the learners in the control group. In addition, we also found that the affordances of the toolkit affected the learners' self‐assessment process, and poor design affordances may have prevented the learners from reflecting by themselves. Conclusions: Together, our empirical study shed light on the design of future writing analytics tools which aim at improving learners' self‐assessment during formative writing processes. Lay Description: What is already known about this topic: Learners' self‐assessment during writing is essential for assessing their writing products and monitoring their writing processes.Formative feedback is vital for improving learners' self‐assessment capabilities.Writing analytics tools offer feedback to learners, however, how these tools facilitate self‐assessment remains under investigated. What this paper adds: We developed a writing analytics toolkit and used data visualisation and cutting‐edge artificial intelligence technology that provides real‐time and formative feedback to learners.We found the toolkit helped learners improve the self‐assessment accuracy of their writing products.We also found that the affordances of the toolkit affected the learners' self‐assessment process, and poor design affordances may have prevented the learners from reflecting by themselves. Implications for practice and/or policy: It is crucial for learning analytics and educational technology developers to design feedback tools and provide formative assessments.Learning analytics tools should have high affordance so that learners can easily understand the formative feedback, thereby promoting actionable self‐assessment. [ABSTRACT FROM AUTHOR]

Peer review has been commonly applied to university-level online courses, but less at the high-school level. Little is known about high school students' tendencies when they give reviews to their peers, specifically which posts are more likely to receive reviews. This study adopted social network analysis, regression analysis and content analysis to investigate the structure of students' peer review network and tendency characteristics of peer reviews in a high school SPOC (small private online course). We report four findings: (1) Students' peer review networks appeared to be core-periphery structures; (2) Time is a major factor in that students who posted earlier in writing tasks were more likely to receive reviews from their peers. (3) Interpersonal skills significantly affect the number of peer reviews a student receives. In other words, the more reviews a student gives, the more likely that student is to receive reviews from others. (4) Students with assertive arguments and better interpersonal relationships with peers in offline situations received more reviews from others. These findings might provide insights for course designers and education practitioners to design peer review strategies in high school-level SPOCs.

Augmented reality (AR) recently shows great potential to facilitate students' learning, especially their learning performance and motivation. However, few studies considered learners' emotional factors such as learning anxiety, which may influence the learning results. This study aims to explore how different experimental conditions (AR & Non-AR) may affect the learning performance and motivation of students with different levels of learning anxiety. An AR-based magnetism experimental tool entitled "MagAR" was proposed to support junior high school students' physics learning. The results indicated that, when compared to traditional experimental materials, AR can effectively improve students' learning performance and motivation. Moreover, regardless of their anxiety, students in AR condition performed better on transfer test than those in Non-AR group. AR can also significantly motivate students with high anxiety. Besides, all students hold a positive attitude towards MagAR. [ABSTRACT FROM AUTHOR]