In the literature, one can find many claims about how long a learning video should be, but only a few valid reasons and even less empirical evidence. It is argued that a video should be as short as possible according to the learners’ attention span. Short videos shall prevent the learner from becoming too passive. The Segmenting Principle postulates the division of longer passages into smaller, separate sections as an alternative to shortening. In this article, we present two studies. In the first study, we examined the video length and segmentations of the entire 895 German and a sample of 524 English channels from YouTube Education (83,558/154,370 videos). We clustered the videos by length into three groups and identified a series of videos by their titles. Short Videos with up to 14 or 22 min of playing time can be considered common practice. About 8 % of the videos with short lengths were part of a series of segmented videos. Videos of medium length were part of a series in 21 % and 14 % of the cases. We conclude that dividing comprehensive video-based learning resources into multiple segments is a common practice. In the second study, we investigate two design variants for structuring longer videos into segments: (i) video with an additional chapter overview, visible chapter boundaries, and navigation options for the segments, and (ii) sequence of segmented videos of suitable length. An online user study compared these two variants with non-segmented video players (N=22). Segmented videos resulted in higher learning gains than the non-segmented version of the same video. The participants perceived the segmented videos in conditions (i) and (ii) better structured. The question of video length is not crucial for learning outcomes as long as the video can be provided in meaningful segments within the video player.

Background: Self‐assessment serves to improve learning through timely feedback on one's solution and iterative refinement as a way to improve one's competence. However, the complexity of the self‐assessment process is widely recognized, as well as that students can benefit from it only if their assessment is accurate enough.Objectives: In order to gain more insight into the self‐assessment process we analysed students' behaviour, accuracy, and question‐related characteristics that influence the capability of self‐assessment in two studies.Methods: The initial study examined 131 undergraduate students using voluntary self‐assessment questions in an online course in a B.Sc. Computer Science program while a year later a replication study with the same research settings was applied to a different cohort of 264 undergraduate students with minor modifications to the question design, in the light of the original findings.Results and Conclusions: Results from both studies show that similar patterns could be observed for usage and of accuracy and score distribution for almost all questions. Item difficulty and comprehensiveness of the sample solution were identified as features of self‐assessment questions affecting student's self‐assessment capability. The replication study showed that task design can be modified to affect students' accuracy. Recommendations to make self‐assessment tasks effective and efficient for learning are provided.

The integration of advanced learning analytics and data-mining technology into higher education has brought various opportunities and challenges, particularly in enhancing students' self-regulated learning (SRL) skills. Analyzing developed features for SRL support, it has become evident that SRL support is not a binary concept but rather a continuum, ranging from limited to advanced levels of SRL support. This article introduces the rubric, designed to evaluate the degree of self-regulated learning support available within technology enhanced learning environments. Following rubric design best practices, we took a multifaceted methodological approach to ensure rubric validity and reliability: consulting Zimmerman's theoretical model, comparing technological features distilled from empirical studies that demonstrated significant effectiveness, consulting SRL experts, and iterative development and feedback. Across three phases of SRL the rubrics describe evaluation criteria and in detail define performance levels (Limited, Moderate and Advance). By employing the rubric, educators and researchers can 1) gain insights into the extent of implemented SRL approaches, 2) further develop SRL support of learning environments, and 3) better support students on their journey towards becoming self-regulated learners. Finally, the reliability analysis demonstrated a high degree of agreement among different raters evaluating the same course, indicating that the rubric is a reliable tool for obtaining relevant evaluations of SRL support in higher education. We conclude by discussing the significance of the rubric in promoting self-regulated learning within the current pedagogical and technological landscape.

Advanced learning technologies have become a focal point in recent educational research, holding the promise of enhancing students' self-regulated learning (SRL) by facilitating various processes of planning, monitoring, performing, and reflecting upon learning experiences. However, concerns have arisen regarding the efficacy and design of technologies, the spectrum of possibilities for SRL support, and too ambiguous claims associated with these technologies. To address these uncertainties and to provide a platform for generating the more empirical evidence, Self-Regulated Learning Support (SRL-S) rubric was developed to facilitate the assessment of SRL support in technology-enhanced learning environments. It is grounded in established educational theory and proven empirical research results. This article presents a study that extends the application of the rubric to establish its reliability and validity, filling a gap in prior research. First, content, criterion-related, and construct validation were performed through international and interdisciplinary experts’ reviews. Subsequently, inter-rater and intra-rater reliability were assessed using Intraclass Correlation Coefficients and Cohens Kappa tests. The outcomes of these analysis demonstrated that the SRL-S is a reliable and valid instrument for assessing the levels of SRL support within learning environments. Additional implications for further research to support self-regulated learning are discussed.

The recent surge in the use of learning analytics in education has led to the development of more adaptive and personalized learning environments (APLE). A key feature of APLE is its capability to support learning tailored to various student needs and goals. Although educational studies emphasize goal setting as essential for effective student learning and self-regulation, current empirical research on APLE lacks clarity on how different learning activities (such as text reading and interacting with various task types) vary among students with different learning goals and outcomes, as well as what the specific thresholds and values for these activities are. To address this gap and support further research in APLE, this study aimed to examine how students with different learning outcomes (mastering, passing, and non-passing the course) differ in their learning behaviors and cognitive engagement with course materials, as indicated by their digital trace data obtained from APLE. Conducted within a formal asynchronous distance higher education program, the study grouped students based on their final exam scores and analyzed their digital traces. The findings highlight which aspects of digital trace data correlate effectively with student performance and identify parameters of various indicators that can be useful for guiding students’ behaviors towards desired academic goals. Additionally, the study offers valuable insights by challenging conventional assumptions about the uniform efficacy of different learning tasks (quiz tasks, self-assessment tasks and expert corrected tasks) in assessing student learning progress and outcome. It prompts a discussion about the role of student self-assessment, suggesting that while it is crucial for the self-regulation and learning process, it may not be the best indicator for students’ goal attainment.