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Researchers have demonstrated that dialogue‐based intelligent tutoring systems (ITS) can be effective in assisting students in learning. However, little research has attempted to explore the necessity of equipping dialogue‐based ITS with one of the most important capabilities of human tutors, that is, maintaining polite interactions with students, which is essential to provide students with a pleasant learning experience. In this study, we examined the role of politeness by analysing a large‐scale real‐world dataset consisting of over 14K online human–human tutorial dialogues. Specifically, we employed linguistic theories of politeness to characterise the politeness levels of tutor–student‐generated utterances, investigated the correlation between the politeness levels of tutors' utterances and students' problem‐solving performance and quantified the power of politeness in predicting students' problem‐solving performance by applying Gradient Tree Boosting. The study results showed that: (i) in the effective tutorial sessions (ie, sessions in which students successfully solved problems), tutors tended to be very polite at the start of a tutorial session and become more direct to guide students as the session progressed; (ii) students with better performance in solving problems tended to be more polite at the beginning and the end of a tutorial session than their counterparts who failed to solve problems; (iii) the correlation between tutors' polite expressions and students' performance was not evident in non‐instructional communication; and (iv) politeness alone cannot adequately reveal students' problem‐solving performance, and thus other factors (eg, sentiment contained in utterances) should also be taken into account. Practitioner notesWhat is already known about this topic Human–human tutoring is acknowledged as an effective instructional method.Polite expression can help strengthen the relationship between tutors and students.Polite expression can promote students' learning achievements in many educational contexts.What this paper adds By considering the students' prior progress on a problem‐based learning task, we demonstrated the extent to which tutors and students display politeness in tutoring dialogues.Tutors' polite expressions might not correlate with students' problem‐solving performance in online human–human tutoring dialogues.Politeness alone was insufficient to predict the students' performance.Implications for practice Tutors might consider using words with positive sentiment values to express politeness to students with prior progress, which might encourage those students to make a further effort.The polite strategy of expressing indirect requests could help tutors mitigate the sense of directness, but this strategy should be carefully used in delivering instructional hints, especially for students without prior progress.To better assist students without prior progress, tutors might consider using more direct expression to explicitly guide students. [ABSTRACT FROM AUTHOR]

Self-regulation is an essential skill for lifelong learning. Research has shown that self-regulated learning (SRL) leads to greater academic achievement and sustainable education, but students often struggle with SRL. Scaffolds are widely reported as an effective and efficient support method for SRL. To further improve digital scaffolds' effectiveness, real-time detection of learning behavior can be used to personalize scaffolds. Therefore, the present study aimed to inform the field of scaffolding SRL by reporting on the design and evaluation of digital scaffolds. We present decisions made during the design process of personalized scaffolds to inform future scaffold designs. We evaluated how scaffolds were personalized based on real-time detection SRL, how university students respond to the scaffolds (i.e., compliance), and how this response is related to learning outcomes (i.e., quality of an essay). The research design was a pre-posttest with three conditions (no, generalized (same for all), or personalized scaffolds). A 45-minute reading and writing task was used, during which SRL processes were recorded in real-time. Findings revealed that different real-time SRL processes could be used to personalize scaffolds, meaning that we were able to personalize the content of scaffold based on students' actual learning behavior. In addition, students in the personalized condition complied more with the scaffolds than students in the other conditions. This compliance with the scaffolds was generally associated with better learning outcomes. To conclude, our approach showed how design decisions could be evaluated and provided insight into the personalization of scaffolds. [ABSTRACT FROM AUTHOR]

Background: Assignments that involve writing based on several texts are challenging to many learners. Formative feedback supporting learners in these tasks should be informed by the characteristics of evolving written product and by the characteristics of learning processes learners enacted while developing the product. However, formative feedback in writing tasks based on multiple texts has almost exclusively focused on essay product and rarely included SRL processes. Objectives: We explored the viability of using product and process features to develop machine learning classifiers that identify low‐ and high‐performing essays in a multi‐text writing task. Methods: We examined learning processes and essay submissions of 163 graduate students working on an authentic multi‐text writing assignment. We utilised learners' trace data to obtain process features and state‐of‐the‐art natural language processing methods to obtain product features for our classifiers. Results and Conclusions: Of four popular classifiers examined in this study, Random Forest achieved the best performance (accuracy = 0.80 and recall = 0.77). The analysis of important features identified in the Random Forest classification model revealed one product (coverage of reading topics) and three process (elaboration/organisation, re‐reading and planning) features as important predictors of writing quality. Major Takeaways: The classifier can be used as a part of a future automated writing evaluation system that will support at scale formative assessment in writing tasks based on multiple texts in different courses. Based on important predictors of essay performance, a guidance can be tailored to learners at the outset of a multi‐text writing task to help them do well in the task. Lay Description: What is already known about this topic?: Both product and process features should be used to inform formative feedback on writing.Providing product‐ and process‐oriented feedback to learners is challenging.Automatic writing evaluation systems have mainly relied upon product features.Automated analysis of learners' trace data and their essay drafts is a promising venue. What this paper adds?: An accurate machine learning classifier that identifies low‐ and high‐scoring essays.The classifier utilized both product and process features.We obtained process features from learners' trace data in digital learning environment.We computed product features using state‐of‐the‐art text analytical methods. Implications for practice and/or policy: The classifier can be used as a part of a future automated writing evaluation system.We revealed learning processes and essay characteristics that influence performance.Based on important predictors of performance, formative feedback can be given to learners. [ABSTRACT FROM AUTHOR]

Collaborative discussions should engage all students, not just a few who dominate ("leaders") while others participate as "followers" (Zhu, 2006). Cunningham (1991) noted that collaborating learners bring, discuss and debate multiple perspectives to develop their own position while acknowledging others' views. Higher levels of knowledge construction emerged when posts stimulated frequent reply by multiple participants (Aviv, Erlich, Ravid, & Geva, 2003) and were strongly content- and task-oriented (Rovai, 2007). So, to help students more actively and productively engage in knowledge-constructing discussions, an instructor needs to detect students' posts that do not stimulate replies, identify content those posts introduce, and guide students to revise posts to encourage peers' responses. However, such monitoring would be very time- and energy-consuming, especially in large-enrolment courses (Hura, 2010). To set a stage for developing a classifier to automate these tasks, we proposed 10 rhetorical moves characteristic of the interactive mode of Chi and Wylie's ICAP framework (2014) and categorized fine-grained content in discussion posts using these moves. We then identified attributes of posts that triggered a greater number of responses. Rhetorical moves of "asking questions," "requesting justification," "building-on," "giving a reason" and "making a claim" triggered more peer responses. Posts with moves of "disagreeing," "comparing" and "making claims" predicted students' achievement on a test and an argumentative writing task. We propose analytics for learners and instructors about forming and revising posts to promote constructive discussions and subsequent achievements. • Providing both frequent and on topic posts is important to advance knowledge construction in online discussions. • We partitioned students' posts into idea units and developed a 10-category coding scheme inspired by the ICAP framework. • Asking questions, requesting justification and building-on trigger more peer responses that contribute to the discussion. • Disagreeing, comparing and making claims predict students' achievement on argumentative writing task and achievement test. [ABSTRACT FROM AUTHOR]