How can the blow of math difficulty on elementary school children’s motivational, cognitive, and affective experiences be dampened? : The critical role of autonomy-supportive instructions

Although teachers are recommended to create a stimulating learning environment in which children can use, perfect, and extend their skills, this is far from easy. In many cases, identifying the optimal difficulty level of learning tasks involves a trial-and-error process during which teachers offer children too difficult tasks, with negative outcomes as a result. This experimental study investigated if autonomy-supportive instructions could dampen or even cancel out these presumed negative outcomes associated with math difficulty in elementary schoolchildren (N = 479; M-age = 9.41). After varying an autonomy-supportive versus a controlling instructional style through a comic book, children solved a series of either easy-medium or difficult math exercises, followed by the completion of questionnaires and the opportunity to choose the difficulty level of a final set of exercises to work on independently. Children who solved difficult, relative to easier, exercises reported less interest, more irritation, and more cognitive disengagement, while also seeking less challenge when asked to work independently. Need-based experiences of competence and autonomy accounted for these effects. Yet, the impairing impact of task difficulty could, at least partially, be dampened through the use of an autonomy-supportive relative to a controlling instructional style, which led to enhanced autonomy satisfaction. These findings largely occurred independent of children's motives for mathematics. The results have high practical value, especially for poor performers and children with mathematical learning disabilities, who find math to be harder overall. Limitations and implications for practice are discussed. Educational Impact and Implications Statement : Autonomy-supportive instructions (e.g., inviting language, meaningful rationale) were found to dampen the impairing effects of too difficult math tasks on children's motivational, cognitive, and affective experiences. This is especially important for poor performers and children with mathematical learning disabilities, who find math to be harder overall. An autonomy-supportive environment and avoiding too hard learning material may stimulate children to accept new challenges, thereby possibly improving chances for later academic/job success

What learning analytics based prediction models tell us about feedback preferences of students

Learning analytics (LA) seeks to enhance learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators (Siemens & Long, 2011). This study examined the use of preferred feedback modes in students by using a dispositional learning analytics framework, combining learning disposition data with data extracted from digital systems. We analyzed the use of feedback of 1062 students taking an introductory mathematics and statistics course, enhanced with digital tools. Our findings indicated that compared with hints, fully worked-out solutions demonstrated a stronger effect on academic performance and acted as a better mediator between learning dispositions and academic performance. This study demonstrated how e-learners and their data can be effectively re-deployed to provide meaningful insights to both educators and learners

Robust Modeling of Epistemic Mental States

This work identifies and advances some research challenges in the analysis of facial features and their temporal dynamics with epistemic mental states in dyadic conversations. Epistemic states are: Agreement, Concentration, Thoughtful, Certain, and Interest. In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states. Non-linear relations are found to be more prevalent, while temporal features derived from original facial features have demonstrated a strong correlation with intensity changes. Then, we propose a novel prediction framework that takes facial features and their nonlinear relation scores as input and predict different epistemic states in videos. The prediction of epistemic states is boosted when the classification of emotion changing regions such as rising, falling, or steady-state are incorporated with the temporal features. The proposed predictive models can predict the epistemic states with significantly improved accuracy: correlation coefficient (CoERR) for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special Issue: Socio-Affective Technologie

Empowering Active Learning to Jointly Optimize System and User Demands

Existing approaches to active learning maximize the system performance by sampling unlabeled instances for annotation that yield the most efficient training. However, when active learning is integrated with an end-user application, this can lead to frustration for participating users, as they spend time labeling instances that they would not otherwise be interested in reading. In this paper, we propose a new active learning approach that jointly optimizes the seemingly counteracting objectives of the active learning system (training efficiently) and the user (receiving useful instances). We study our approach in an educational application, which particularly benefits from this technique as the system needs to rapidly learn to predict the appropriateness of an exercise to a particular user, while the users should receive only exercises that match their skills. We evaluate multiple learning strategies and user types with data from real users and find that our joint approach better satisfies both objectives when alternative methods lead to many unsuitable exercises for end users.Comment: To appear as a long paper in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020). Download our code and simulated user models at github: https://github.com/UKPLab/acl2020-empowering-active-learnin

Educating programmers: A reflection on barriers to deliberate practice

Copyright @ 2013 HEAProgramming is a craft which often demands that learners engage in a significantly high level of individual practice and experimentation in order to acquire basic competencies. However, practice behaviors can be undermined during the early stages of instruction. This is often the result of seemingly trivial misconceptions that, when left unchecked, create cognitive-affective barriers. These interact with learners' self-beliefs, potentially inducing affective states that inhibit practice. This paper questions how to design a learning environment that can address this issue. It is proposed that analytic and adaptable approaches, which could include soft scaffolding, ongoing detailed formative feedback and a focus on self-enhancement alongside skill development, can help overcome such barriers

Student profiling in a dispositional learning analytics application using formative assessment

How learning disposition data can help us translating learning feedback from a learning analytics application into actionable learning interventions, is the main focus of this empirical study. It extends previous work where the focus was on deriving timely prediction models in a data rich context, encompassing trace data from learning management systems, formative assessment data, e-tutorial trace data as well as learning dispositions. In this same educational context, the current study investigates how the application of cluster analysis based on e-tutorial trace data allows student profiling into different at-risk groups, and how these at-risk groups can be characterized with the help of learning disposition data. It is our conjecture that establishing a chain of antecedent-consequence relationships starting from learning disposition, through student activity in e-tutorials and formative assessment performance, to course performance, adds a crucial dimension to current learning analytics studies: that of profiling students with descriptors that easily lend themselves to the design of educational interventions

Do personality traits moderate relations between psychologically controlling parenting and problem behavior in adolescents?

This research examined whether and how adolescents' personality traits moderate associations between psychologically controlling parenting and problem behaviors. On the basis of self-determination theory, we also examined the mediating role of psychological need frustration in the effects of psychologically controlling parenting. A cross-sectional study in two samples (N = 423 and 292; M-age = 12.43 and 15.74 years) was conducted. While in Sample 1 both mothers and adolescents provided reports of parenting and problem behavior, Sample 2 relied on adolescent-reported parenting and mother-reported problem behavior. Psychologically controlling parenting was related to internalizing and externalizing problems in both samples. Little systematic evidence was obtained for the moderating role of personality, with the exception of a moderating effect of Agreeableness. In both samples, psychological control was unrelated to externalizing problems among adolescents high on Agreeableness. Analyses of Sample 2 showed that associations between psychological control and problem behavior were mediated by psychological need frustration. Adolescent personality plays a modest role as a moderator of associations between psychologically controlling parenting and problem behavior. Frustration of adolescents' basic and universal psychological needs can account for the undermining effects of psychologically controlling parenting. Directions for future research are discussed

A New Generation Gap? Some thoughts on the consequences of increasingly early ICT first contact

One possible consequence of ICT’s rapid rise will be a new ‘generation gap’ arising from differing perceptions of the learning technologies. The nature, causes and consequences of this gap are of interest to educational practitioners and policymakers. This paper uses data from an ongoing project together with a synopsis of research to describe the ICT-based generation gap that currently exists between students and their teachers and parents. It is argued that this gap may exist between students differing in age by as little as five years. Results from a related project exploring Networked Information and Communication Literacy Skills (NICLS), are used to introduce a discussion on the nature of any skills gap that must be addressed in the light of this generation gap