There is a growing interest in developing gamified learning solutions to address educational challenges. However, learning is highly influenced by the conditions in which it takes place (e.g., does gamified learning in a laboratory setting replicate the outcomes of gamified learning online at home?). Hence, it is crucial to understand the boundary conditions of different learning contexts to effectively implement gamified interventions that provide optimal learner support. This work contributes to such an understanding by assessing how general contextual aspects of three studies on gamified learning influence cognitive learning and motivational outcomes. Therefore, we re-examined the results of two earlier published online studies (Study 1: n=285; Study 2: n=61) and compared the results to a recently conducted laboratory study (Study 3: n=121), all of which employed the same associative learning task. Comparing results through a Bayesian lens, we find that motivational outcomes induced by gamification differ substantially between contexts. In contrast, cognitive learning outcomes seem comparatively robust across different contextual factors, with some indication of subtle influences in agreement with cognitive learning theories. Implications are discussed for future empirical research on learning, highlighting how a better understanding of boundary conditions of gamified learning interventions could open perspectives for context-aware educational interventions.

As the increase in university students’ mental health problems poses significant challenges to education, new research-based ways to support students’ well-being in higher education are urgently needed. Acceptance and Commitment Therapy (ACT) has been shown to be effective in enhancing different aspects of well-being in a variety of contexts. Although study burnout has been shown to have detrimental impacts on students’ well-being and studying, not much is known about the person-oriented features of burnout risk in relation to ACT-based intervention outcomes. The aim of this study was to compare the effects of an online ACT-based course on different aspects of university students’ well-being and study ability in latent groups of students that had different levels of study burnout and engagement at the beginning of the intervention. The results of latent profile analysis (LPA) showed that students (N=352) represented four profiles at the beginning of the course: indifferent (37.8%), engaged (29%), engaged-inefficacious (13.6%), and burned-out (19.6%). The results of mixed ANOVA with repeated measures showed that psychological flexibility, well-being, study engagement and organised studying increased, and study burnout risk decreased in the whole sample of course-participating students. The changes in students’ exhaustion, well-being, psychological flexibility, and organised studying did not differ between the four burnout and engagement profiles. The profiles differed in the changes of cynicism, inadequacy and engagement. The results of this study provide new knowledge of the person-oriented features of study burnout and indicate that ACT-based course interventions can be effective in enhancing different profiles representing university students’ well-being.

Technology integration is regarded as a crucial and complex endeavour to enhance students’ learning and prepare them to participate in a digital society. Although the research landscape on teachers’ technology integration is vivid and stimulating, an analytical model which synthesises different strands of research to model antecedents (i.e., teachers’ professional competences), processes and outcomes of technology integration in an integrated manner is missing. That said, previous research was often rather product-oriented and ignored potential effects on students’ learning processes and their achievement. To fill this gap, in this paper, we outline a preliminary model, the TPTI-model (teachers’ professional competence for technology integration), in which we deliberately link different research perspectives on teachers’ professional competences, professional vision and students’ learning (processes) to model technology integration during teaching. Based on the preliminary TPTI-model, we propose future research directions, which may allow to gain a better understanding of the teacher- and student-related conditions as well as processes of technology integration and their effects on students’ learning.

Abstract Mobile eye-tracking research has provided evidence both on teachers' visual attention in relation to their intentions and on teachers’ student-centred gaze patterns. However, the importance of a teacher’s eye-movements when giving instructions is unexplored. In this study we used mobile eye-tracking to investigate six teachers’ gaze patterns when they are giving task instructions for a geometry problem in four different phases of a mathematical problem-solving lesson. We analysed the teachers’ eye-tracking data, their verbal data, and classroom video recordings. Our paper brings forth a novel interpretative lens for teacher’s pedagogical intentions communicated by gaze during teacher-led moments such as when introducing new tasks, reorganizing the social structures of students for collaboration, and lesson wrap-ups. A change in the students’ task changes teachers’ gaze patterns, which may indicate a change in teacher’s pedagogical intention. We found that teachers gazed at students throughout the lesson, whereas teachers’ focus was at task-related targets during collaborative instruction-giving more than during the introductory and reflective task instructions. Hence, we suggest two previously not detected gaze types: contextualizing gaze for task readiness and collaborative gaze for task focus to contribute to the present discussion on teacher gaze

The emotions experienced by primary school students have both positive and negative effects on learning processes. Thus, to better understand learning processes, research should consider emotions during class. Standard survey-based methods, such as self-reports, are limited in terms of capturing the detailed trajectories of primary school children’s emotions, as their abilities of self-reporting are developing and still limited. Emotions can also be tracked by capturing emotional responses as they occur e.g. from physiological reaction measured with sensor wristbands. This technology generates an emotional responsestypology based on continuously captured physiological data, such as skin conductivity and skin temperature. However, such measurement methods need to be validated before being used. The present study thus attempted to validate this instrument with primary school students. We used the BM Sensor Wristband technology, as its emotional response typology is based on the categorical emotion and homeostasis approach. In our research, we focus on the emotional responses that can be distinguished by the BM Typology and that can influence learning processes. These emotional responses are: “joy”, “curiosity”, “attention”, “fear”, “anger” and “passivity”. Therefore, we induced emotional responses in primary school children through specifically developed audio-visual stimuli. Using logistic mixed effects modelling, we investigated the occurrence of opposing reactions. We observed that primary school children’s reactions to audio-visual stimuli could be differentiated. We conclude that primary school children’s emotional responses, such as “joy”, “curiosity”, “attention”, “fear”, “anger” and “passivity”, can be accurately measured by evaluating physiological data.

Many test developers try to ensure the content validity of their tests by having external experts review the items in terms of relevance, difficulty, clarity, and so on. Although this approach is widely accepted, a closer look reveals there are several pitfalls that need to be avoided if experts’ advice is to be truly helpful. First, I offer a classification of tasks experts are given by test developers as reported on in the literature dealing with procedures of drawing on experts’ advice. Second, I review a sample of reports on test development (N = 72) to identify the common current procedures for selecting and consulting experts. Results indicate that often the choice of experts seems to be somewhat arbitrary, the questions posed to experts lack precision, and the methods used to evaluate experts' feedback are questionable. Given these findings I explore in more depth what prerequisites are necessary for their contributions to be useful in ensuring the content validity of tests. In conclusion, explicit guidelines on this matter need to be elaborated and standardised (above all, by the AERA, APA, and NCME-Standards).

Interest research offers different hypotheses about the association between interest and learning outcomes. The standard hypothesis proposes that interest predicts learning outcomes: people acquire new knowledge about a topic they find interesting. The affective by-product hypothesis assumes that learning predicts interest: by learning something, people develop an interest in this topic. Finally, the reciprocal hypothesis states that interest and learning covary. This longitudinal study aimed to test the predictive validity of these three hypotheses in the context of secondary school STEM education. The participants were 104 Finnish 7th grade students aged 12-14. Data were collected at three times during the school year through questionnaires and grade evaluations in mathematics and biology. A partial least squares (PLS) path modeling approach was used to determine the relationships between interest and course grades across the three measurement points: at the beginning of the autumn semester, at the beginning of the spring semester, and after the spring semester at the end of the school year. The results differed between the autumn and spring semesters: During the autumn semester, students’ interest predicted their grades, whereas during the spring semester, grades predicted their interest. These findings indicate that the relationships between students’ individual interest towards science and mathematics with learning vary. As a practical implication, more focus should be put on when and what type of performance feedback is given to students with differing interest profiles.

This special issue’s editors invited discussion of three broad questions. Slightly rephrased, they are: How well do self-report data represent theoretical constructs? How should analyses of data be conditioned by properties of self report data? In what ways do interpretations of self-report data shape interpretations of a study’s findings? To approach these issues, I first recap the kinds of self-report data gathered by researchers reporting in this special issue. With that background, I take up a fundamental question. What are self-report data? I foreshadow later critical analysis by listing facets I observe in operational definitions of self-report data: nature of the datum, topic, property, setting or context, response scale, and assumptions setting a stage for analyzing data. Discussion of these issues leads to a proposal that ameliorates some of them: Help respondents become better at self reporting.

This study investigated how four 10th-grade students with dyslexia processed and integrated information across web pages and representations when learning in a multiple source multimedia context. Eye movement data showed that participants’ processing of the materials varied with respect to their initial exploration of the web pages, their overall processing time, and the linearity of their processing patterns, with post-learning interviews indicating the deliberate, strategic considerations underlying each participant’s processing pattern. Eye movement data in terms of fixation duration and percentage of regressions also corroborated the findings of formal, diagnostic assessments. Finally, it was found that participants differed with respect to how much factual information they learned from working with the materials and how well they were able to integrate information across the web pages and representations, with results suggesting particular problems with learning factual information and, at the same time, constructing a coherent mental representation of the issue, as well as with drawing on textual information in the integration process. This study brings together two research areas that essentially have been kept apart in theory and research, that is, dyslexia and multimedia learning, and it provides unique information about the role of individual differences in multiple source multimedia contexts.

This paper describes our research approach in which we have focused on situational and contextual variations in motivation and emotion regulation to better understand its role, appearance and function in collaborative learning situations. We have used research designs that employ process-oriented measures combined with subjective interpretations to capture motivation and emotion regulation. Analysing on-line process data poses several challenges such as variation in the granularity of different data sources, problems that emerge due to the complexity of contextual and situational factors in ecologically-valid learning situations or, currently, challenges in the use of multiple data channels and their analyses. In this paper, we present three claims underlying our research, particularly the motivationand emotions and their regulation in learning. The claims are as follows: (1) motivation and emotion regulation is situation and context specific, (2) motivation and emotion regulation is influenced by multi-layered nature of motivationand(3) Motivation and emotion regulation is intertwined with other processes of learning and can be captured from their temporal manifestation. We present an example from our empirical study to discuss how these claims have led us to employ multiple process-oriented methods that include both subjective and objective data sources, including different combinations of situation-specific self-reports, video and physiological data. We then describe opportunities and challenges involved in the empirical studies.