The role of learning goals in the design of ILEs: some issues to consider

Part of the motivation behind the evolution of learning environments is the idea of providing students with individualized instructional strategies that allow them to learn as much as possible. It has been suggested that the goals an individual holds create a framework or orientation from which they react and respond to events. There is a large evidence-based literature which supports the notion of mastery and performance approaches to learning and which identifies distinct behavioural patterns associated with each. However, it remains unclear how these orientations manifest themselves within the individual: an important question to address when applying goal theory to the development of a goal-sensitive learner model. This paper exposes some of these issues by describing two empirical studies. They approach the subject from different perspectives, one from the implementation of an affective computing system and the other a classroom-based study, have both encountered the same empirical and theoretical problems: the dispositional/situational aspect and the dimensionality of goal orientation

Stretching the limits in help-seeking research

This special section focuses on help seeking in a wide range of learning environments, from classrooms to online forums. Previous research has rather restrictively focused on the identification of personal characteristics that predict whether or not learners seek help under certain conditions. However, help-seeking research has begun to broaden these self-imposed limitations. The papers in this special section represent good examples of this development. Indeed, help seeking in the presented papers is explored through complementary theoretical lenses (e.g., linguistic, instructional), using a wide scope of methodologies (e.g., teacher reports, log files), and in a manner which embraces the support of innovative technologies (e.g., cognitive tutors, web-based environments)

Self regulated learning in flipped classrooms: A systematic literature review

The flipped classroom is considered an instructional strategy and a type of blended learning instruction that focused on active learning and student engagement. Over the years, flipped classroom studies have focused more on the advantages and challenges of flipped instruction and its effectiveness, but little is known about the state of self-regulation in flipped classrooms. This study investigates the self-regulation strategies as well as the supports proposed for self-regulated learning in flipped classrooms. Findings show that relatively few studies have focused on self-regulated learning in flipped classrooms compared to the overall research and publication productivity in flipped classrooms. Also, the existing solutions and supports have only focused on either self-regulation or online help-seeking, but have not focused on other specific types of self-regulation strategies. Our study proposed some future research recommendations in flipped classrooms

Automatic Identification of Ineffective Online Student Questions in Computing Education

This Research Full Paper explores automatic identification of ineffective learning questions in the context of large-scale computer science classes. The immediate and accurate identification of ineffective learning questions opens the door to possible automated facilitation on a large scale, such as alerting learners to revise questions and providing adaptive question revision suggestions. To achieve this, 983 questions were collected from a question & answer platform implemented by an introductory programming course over three semesters in a large research university in the Southeastern United States. Questions were firstly manually classified into three hierarchical categories: 1) learning-irrelevant questions, 2) effective learning-relevant questions, 3) ineffective learningrelevant questions. The inter-rater reliability of the manual classification (Cohen's Kappa) was .88. Four different machine learning algorithms were then used to automatically classify the questions, including Naive Bayes Multinomial, Logistic Regression, Support Vector Machines, and Boosted Decision Tree. Both flat and single path strategies were explored, and the most effective algorithms under both strategies were identified and discussed. This study contributes to the automatic determination of learning question quality in computer science, and provides evidence for the feasibility of automated facilitation of online question & answer in large scale computer science classes

Digital Assistants for Self-Regulated Learning: Towards a State-Of-The-Art Overview

We observe a continuous shift from face-to-face to hybrid or online education. Today, learners are exposed to a high level of autonomy and, at the same time, have less contact with peers and teachers. In this environment, the ability to self-regulate one’s learning is becoming more relevant to achieve positive learning results and academic success. However, the application of self-regulated learning is not trivial. A potential solution for this challenge comes in the form of digital assistants like chatbots or pedagogical agents that provide structure for the learners. Existing research on digital assistants for self-regulated learning is scattered across several fields. In this research-in-progress paper, we present preliminary results of a systematic literature review (SLR) study providing an overview of the state-of- the-art of digital assistants supporting SRL. Our results show that future research in this domain should focus on affect, behavioral, and context regulation and more longitudinal studies are required

Carelessness and Affect in an Intelligent Tutoring System for Mathematics

We investigate the relationship between students’ affect and their frequency of careless errors while using an Intelligent Tutoring System for middle school mathematics. A student is said to have committed a careless error when the student’s answer is wrong despite knowing the skill required to provide the correct answer. We operationalize the probability that an error is careless through the use of an automated detector, developed using educational data mining, which infers the probability that an error involves carelessness rather than not knowing the relevant skill. This detector is then applied to log data produced by high-school students in the Philippines using a Cognitive Tutor for scatterplots. We study the relationship between carelessness and affect, triangulating between the detector of carelessness and field observations of affect. Surprisingly, we find that carelessness is common among students who frequently experience engaged concentration. This finding implies that a highly engaged student may paradoxically become overconfident or impulsive, leading to more careless errors. In contrast, students displaying confusion or boredom make fewer careless errors. Further analysis over time suggests that confused and bored students have lower learning overall. Thus, their mistakes appear to stem from a genuine lack of knowledge rather than carelessness