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Evaluative judgement is the capability to make decisions about the quality of work of oneself and others. In this paper, we propose that developing students' evaluative judgement should be a goal of higher education, to enable students to improve their work and to meet their future learning needs: a necessary capability of graduates. We explore evaluative judgement within a discourse of pedagogy rather than primarily within an assessment discourse, as a way of encompassing and integrating a range of pedagogical practices. We trace the origins and development of the term 'evaluative judgement' to form a concise definition then recommend refinements to existing higher education practices of self-assessment, peer assessment, feedback, rubrics, and use of exemplars to contribute to the development of evaluative judgement. Considering pedagogical practices in light of evaluative judgement may lead to fruitful methods of engendering the skills learners require both within and beyond higher education settings. (HRK / Abstract übernommen).

With the development of artificial intelligence (AI) applications, it has become critical for scholars, educators and practitioners to understand an individual’s perceived self-efficacy regarding the use of AI technologies/products. Understanding users’ subsequent behaviors toward the advancement of AI technology is also critical. Despite the growing focus on AI, a suitable scale for measuring AI self-efficacy (AISE) has yet to be developed. Current scales for measuring AISE (i.e., technology self-efficacy scales) are considered inapplicable because they neglect to evaluate perceptions of specific AI characteristics (e.g., AI-based configuration or anthropomorphic design). Given the limitations of existing self-evaluation and diagnostic instruments, the aim of this research is to investigate the construct of AISE, and develop and validate an AISE scale (AISES) for measuring an individual’s perceived self-efficacy in regard to the use of AI technologies/products, in accordance with established exploratory and confirmatory scale development procedures. Specifically, a literature review is employed to generate initial items. An exploratory factor analysis is then performed for item purification purposes. At this stage, potential elements of AISE are extracted. Subsequently, factor extraction and confirmatory factor analysis are used to verify the construct structure of AISE. An analysis of 314 responses indicates that the AISE construct contains four factors: assistance, anthropomorphic interaction, comfort with AI, and technological skills. The scale is comprised of 22 items, and is found to have good fit, reliability, convergent validity, discriminant validity, content validity, and criterion-related validity. Moreover, nomological validity is built by the positive correlation between the AISE construct and motivated learning behaviors. This paper is the pioneer in developing and validating a scale to measure AISE. The findings extend existing knowledge of AISE and can help scholars further develop AISE theories. Our findings will also help educators and practitioners assess individuals’ AISE and explore related behaviors.

This paper reviews current known issues in student self-assessment (SSA) and identifies five topics that need further research: (1) SSA typologies, (2) accuracy, (3) role of expertise, (4) SSA and teacher/curricular expectations, and (5) effects of SSA for different students. Five SSA typologies were identified showing that there are different conceptions on the SSA components but the field still uses SSA quite uniformly. A significant amount of research has been devoted to SSA accuracy, and there is a great deal we know about it. Factors that influence accuracy and implications for teaching are examined, with consideration that students' expertise on the task at hand might be an important prerequisite for accurate self-assessment. Additionally, the idea that SSA should also consider the students' expectations about their learning is reflected upon. Finally, we explored how SSA works for different types of students and the challenges of helping lower performers. This paper sheds light on SSA research needs to address the known unknowns in this field. (ZPID).

Developing the ability to self-assess is a crucial skill for students, as it impacts their academic performance and learning strategies, amongst other areas. Most existing research in this field has concentrated on the exploration of the students’ capacity to accurately assign a score to their work that closely mirrors an expert’s evaluation, typically a teacher’s. Though this process is commonly referred to as self-assessment, a more precise term would be self-assessment scoring accuracy. Our aim is to review what is the average accuracy and what moderators might influence this accuracy. Following PRISMA recommendations, we reviewed 160 articles, including data from 29,352 participants. We analysed 9 factors as possible moderators: (1) assessment criteria; (2) use of rubric; (3) self-assessment experience; (4) feedback; (5) content knowledge; (6) incentive; (7) formative assessment; (8) field of knowledge; and (9) educational level. The results showed an overall effect of students’ overestimation (g = 0.206) with an average relationship of z = 0.472 between students’ estimation and the expert’s measure. The overestimation diminishes when students receive feedback, possess greater self-assessment experience and content knowledge, when the assessment does not have formative purposes, and in younger students (primary and secondary education). Importantly, the studies analysed exhibited significant heterogeneity and lacked crucial methodological information.

Given that massive open online learning courses (MOOCs) are considerably different from traditional classrooms in terms of roles and responsibilities of instructors and learners, successful learners are required to be self-directed in MOOC learning environments. One of the most popular self-directed learning (SDL) models proposed by Garrison (Adult Education Quarterly 48(1):18–33, https://doi.org/10.1177/074171369704800103 , 1997 ) includes three components: motivation, self-monitoring, and self-management. This model was originally discussed from traditional online and face-to-face learning environment. Thus, the present study investigated the relationship among motivation, self-monitoring, and self-management in MOOCs by surveying 322 MOOC learners. Using structural equation modeling, this study found that motivation directly affected self-monitoring and indirectly influenced self-management through self-monitoring. In addition, self-monitoring positively influenced self-management. Therefore, promoting student self-monitoring skills and motivating students is critical. Additional research is needed on the ways to facilitate and support self-monitoring of MOOC learners. Future research could examine the influence of the three elements of SDL on learning achievement and engagement. In addition, further exploration of learner behaviors in MOOCs could provide insights on facilitating learners’ SDL.

This study investigates the effects of using design thinking on students’ problem solving and creativity skills, applying a constructivist learning theory. A course where students use design thinking for analyzing real problems and proposing a solution, was evaluated. The study involved 910 novice university students from different disciplines who worked in teams throughout the semester. Data were collected at three times during the semester, i.e. at the beginning (t0), in the middle (t1) and at the end (t2), after solving a short case study. Each time the problem solving and creativity skill of each student was rated by three different actors, i.e. the students themselves (self-evaluation), one peer and one teacher (facilitator). The objective of this study is to investigate whether the problem solving skills and creativity skills improved along the course, as rated by the three actors. A repeated measures ANOVA was used for the data analysis of this within-subjects design. Results show a significant improvement on students’ problem solving and creativity skills, according to the three raters. Effect sizes were also calculated; in all cases the effect sizes from t0 to t1 were larger than t1 to t2. The multi-actor perspective of this study, the adoption of reliable and valid measures and the large sample size provide robust evidence that supports the implementation of design thinking in higher education curriculum for promoting key skills such as problem solving and creativity, demanded by labor markets. Finally, a discussion that puts forward an agenda for future research is presented.

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 coregulation 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’ learning.

Background As the information age wanes, enabling the prevalence of the artificial intelligence age; expectations, responsibilities, and job definitions need to be redefined for those who provide services in healthcare. This study examined the perceptions of future physicians on the possible influences of artificial intelligence on medicine, and to determine the needs that might be helpful for curriculum restructuring. Methods A cross-sectional multi-centre study was conducted among medical students country-wide, where 3018 medical students participated. The instrument of the study was an online survey that was designed and distributed via a web-based service. Results Most of the medical students perceived artificial intelligence as an assistive technology that could facilitate physicians’ access to information (85.8%) and patients to healthcare (76.7%), and reduce errors (70.5%). However, half of the participants were worried about the possible reduction in the services of physicians, which could lead to unemployment (44.9%). Furthermore, it was agreed that using artificial intelligence in medicine could devalue the medical profession (58.6%), damage trust (45.5%), and negatively affect patient-physician relationships (42.7%). Moreover, nearly half of the participants affirmed that they could protect their professional confidentiality when using artificial intelligence applications (44.7%); whereas, 16.1% argued that artificial intelligence in medicine might cause violations of professional confidentiality. Of all the participants, only 6.0% stated that they were competent enough to inform patients about the features and risks of artificial intelligence. They further expressed that their educational gaps regarding their need for “knowledge and skills related to artificial intelligence applications” (96.2%), “applications for reducing medical errors” (95.8%), and “training to prevent and solve ethical problems that might arise as a result of using artificial intelligence applications” (93.8%). Conclusions The participants expressed a need for an update on the medical curriculum, according to necessities in transforming healthcare driven by artificial intelligence. The update should revolve around equipping future physicians with the knowledge and skills to effectively use artificial intelligence applications and ensure that professional values and rights are protected.

Digital competence has gained a strong prominence in the educational context, being one of the key competencies that teachers must master in today's society. Although most models and frameworks focus on the pre-university level, there is a growing interest in knowing the state of digital competencies of university teachers, that is, the set of knowledge, skills and attitudes necessary for a teacher to make effective use of technologies. The aim of this research is to present a systematic review of the literature in the Web of Science and Scopus, to identify, analyze and classify the published articles between 2000 and 2021 on digital competences, and thus find and improve the research being done on digital skills and future avenues of teachers in the university context. The SciMAT software is used in the analysis. The initial search reveals more than 343 articles in English, of which 152 are duplicates and 135 are not related to the topic of study. After this filtering, 56 articles are obtained and analyzed in depth. The results reveal a predominance of research that focuses on analyzing teachers' self-assessment and reflection of their digital competencies. Teachers recognize that they have a low or medium–low digital competence, as well as the absence of certain competencies, especially those related to the evaluation of educational practice. Despite the multiple studies that address this issue, it is necessary to continue improving research in this area, deepening the assessment of teachers' digital competencies and design, on this basis, more practical and personalized training programs that respond to the needs of teachers in the digital era.

Students are the central agent in self-assessment; therefore, their perceptions are crucial for successful self-assessment. Despite the increasing number of empirical studies exploring how students perceive self-assessment, systematic reviews synthesising students’ perceptions of self-assessment and relating them to self-assessment implementation are scarce. This review covered 44 eligible studies and synthesised findings related to two key aspects of students’ perceptions of self-assessment: (1) usefulness of self-assessment; and (2) factors influencing their implementation of self-assessment. The results revealed inconclusive findings regarding students’ perceived usefulness of self-assessment. Although most studies reported a generally positive perception of self-assessment among students, some studies revealed students’ skepticism about its usefulness. Usefulness was influenced by specific individual factors (i.e., gender, age, and educational level) and instructional factors (i.e., external feedback, use of instruments, and self-assessment purpose). Additionally, implementation was influenced by specific individual factors (i.e., perceived usefulness, affective attitude, self-efficacy, important others, and psychological safety) and instructional factors (i.e., practice and training, external feedback, use of instruments, and environmental support). The findings of this review contribute to a better understanding of students’ perceptions of self-assessment and shed light on the design and implementation of meaningful self-assessment activities that cater to students’ learning needs.