Validation of autism spectrum quotient adult version in an Australian sample

The Autism Spectrum Quotient is used to assess autistic spectrum traits in intellectually competent adults in both the general population and the autism spectrum community. While the autism spectrum Quotient has been validated in several different cultures, to date no study has assessed the psychometrics of the Autism Spectrum Quotient on an Australian population. The purpose of this study was to assess the psychometrics of the autism spectrum Quotient in an Australian sample of both typically developing individuals (n = 128) and individuals with autism spectrum disorder (n = 104). The results revealed that the internal consistency and the test-retest reliability were satisfactory; individuals with autism spectrum disorder scored higher on total Autism Spectrum Quotient score and its subscales than typically developing individuals; however, gender differences were not apparent on total score. Possible cultural differences may explain some of the psychometric variations found. The results of this analysis revealed that the Autism Spectrum Quotient was a reliable instrument for investigating variation in autistic symptomology in both typically developing and Autism Spectrum Disorders populations within an Australian population.<br /

Learning with Generative Artificial Intelligence Within a Network of Co-Regulation

The emergence of generative artificial intelligence (AI) has created legitimate concerns surrounding academic integrity and the ease with which such technologies might lead to cheating in assessment, in particular. However, fixating solely on potential misconduct is overshadowing a more profound, transformative interaction between learners and machines. This commentary article delves into the relationship between students and AI, aiming to highlight the need for revised pedagogical strategies in the AI age. We argue that the much-discussed approaches that prioritise AI literacy or augmented critical thinking might be inadequate. Instead, we contend that a more holistic approach emphasising self-regulated learning (SRL) and co-regulation of learning is needed. SRL promotes autonomy, adaptability, and a deeper understanding, qualities indispensable for navigating the intricacies of AI-enhanced learning environments. Furthermore, we introduce the notion of a network of co-regulation, which underscores the intertwined learning processes between humans and machines. By positioning the self at the core of this network, we emphasise the indispensable role of individual agency in steering productive human-AI educational interactions. Our contention is that by fostering SRL and understanding co-regulated dynamics, educators can better equip learners for an interconnected AI-driven world

Implementing summative assessment with a formative flavour: a case study in a large class

Teaching a large class can present real challenges in design, management, and standardization of assessment practices. One of the main dilemmas for university teachers is how to implement effective formative assessment practices, with accompanied high quality feedback consistently over time with large classroom groups. This article reports on how elements of formative practices can be implemented as part of summative assessment in very large undergraduate cohorts (n = 1500 in one semester), studying in different modes (on- and off-campus), with multiple markers, and under common cost and time constraints. Design features implemented include the use of exemplars, rubrics and audio feedback. The article draws on the reflections of the leading teacher, and discusses that for summative assessment to benefit learners, it should contain formative assessment elements. The teaching practices utilised in the case study provide some means to resolve the tensions between formative assessment and summative assessment that may be more generally applicable

Do pain-related beliefs influence adherence to multidisciplinary rehabilitation? A systematic review

OBJECTIVES: To understand how pain-related cognitions predict and influence treatment retention and adherence during and after a multidisciplinary rehabilitation program. METHODS: Electronic databases including Medline, CINAHL, PsycINFO, Academic Search Complete, and Scopus were used to search three combinations of keywords: chronic pain, beliefs, and treatment adherence. RESULTS: The search strategy yielded 591 results, with an additional 12 studies identified through reference screening. 81 full-text papers were assessed for eligibility and 10 papers met the inclusion and exclusion criteria for this review. The pain-related beliefs that have been measured in relation to treatment adherence include: pain-specific self-efficacy, perceived disability, catastrophizing, control beliefs, fear-avoidance beliefs, perceived benefits and barriers, as well as other less commonly measured beliefs. The most common pain-related belief investigated in relation to treatment adherence was pain-related self-efficacy. Findings for the pain-related beliefs investigated among the studies were mixed. Collectively, all of the aforementioned pain-related beliefs, excluding control beliefs, were found to influence treatment adherence behaviours. DISCUSSION: The findings suggest that treatment adherence is determined by a combination of pain-related beliefs either supporting or inhibiting chronic pain patients\u27 ability to adhere to treatment recommendations over time. In the studies reviewed, self-efficacy appears to be the most commonly researched predictor of treatment adherence, its effects also influencing other pain-related beliefs. More refined and standardised methodologies, consistent descriptions of pain-related beliefs and methods of measurement will improve our understanding of adherence behaviours

Using formative assessment to influence self- and co-regulated learning: the role of evaluative judgement

Recently, the concept of evaluative judgement has gained attention as a pedagogical approach to classroom formative assessment practices. Evaluative judgement is the capacity to be able to judge the work of oneself and that of others, which implies developing knowledge about one’s own assessment capability. A focus on evaluative judgement helps us to better understand what is the influence of assessment practices in the regulation of learning. In this paper, we link evaluative judgement to two self-regulated learning models (Zimmerman and Winne) and present a model on the effects on co-regulation of learning. The models help us to understand how students can be self-regulated through developing their evaluative judgement. The co-regulation model visualises how the learner can become more strategic in this process through teacher and peer assessment in which assessment knowledge and regulation strategies are shared with the learner. The connections we make here are crucial to strengthening our understanding of the influence of assessment practices on students’ learnin

Module 3-2 - Feedback for learning (Contemporary Approaches to University Teaching)

Dr Jaclyn Broadbent from Deakin University discusses the use of video and audio technology to give high quality feedback. Video created for the Contemporary Approaches to University Teaching course which provides key introductory learning and teaching concepts and strategies for those who are in their first few years of university teaching

Academic success is about self-efficacy rather than frequency of use of the learning management system

Previous studies have investigated the association between the frequency of student learning management system (LMS) use (logins, discussion board use, resources used, etc.) and academic achievement. These studies indicate that low LMS use by students is likely to result in less academic success. However, these models fail to take into account self-beliefs that may also increase the explanatory value of learning analytics from the LMS. This study surveyed 310 students (M = 22.10 years, SD = 6.30 years) undertaking a first year health psychology subject. Results show the central role of self-efficacy in predicting student performance. Online activity was not predictive of performance, suggesting the primacy of psychological factors more so than online engagement in determining outcome. Of the motivational factors, amotivation was the single significant predictor of academic achievement. Proposed future research directions include the need to evaluate whether these results are sustained over time