Though recent technological advances have enabled note-taking through different modalities (e.g., keyboard, digital ink, voice), there is still a lack of understanding of the effect of the modality choice on learning. In this paper, we compared two note-taking input modalities—keyboard and voice—to study their effects on participants’ understanding of learning content. We conducted a study with 60 participants in which they were asked to take notes using voice or keyboard on two independent digital text passages while also making a judgment about their performance on an upcoming test. We built mixed-effects models to examine the effect of the note-taking modality on learners’ text comprehension, the content of notes and their meta-comprehension judgement. Our findings suggest that taking notes using voice leads to a higher conceptual understanding of the text when compared to typing the notes. We also found that using voice triggers generative processes that result in learners taking more elaborate and comprehensive notes. The findings of the study imply that note-taking tools designed for digital learning environments could incorporate voice as an input modality to promote effective note-taking and higher conceptual understanding of the text.

Video has become an essential medium for learning. However, there are challenges when using traditional methods to measure how learners attend to lecture videos in video learning analytics, such as difficulty in capturing learners’ attention at a fine-grained level. Therefore, in this paper, we propose a gaze-based metric—“with-me-ness direction” that can measure how learners’ gaze-direction changes when they listen to the instructor’s dialogues in a video-lecture. We analyze the gaze data of 45 participants as they watched a video lecture and measured both the sequences of with-me-ness direction and proportion of time a participant spent looking in each direction throughout the lecture at different levels. We found that although the majority of the time participants followed the instructor’s dialogues, their behaviour of looking-ahead, looking-behind or looking-outside differed by their prior knowledge. These findings open the possibility of using eye-tracking to measure learners’ video-watching attention patterns and examine factors that can influence their attention, thereby helping instructors to design effective learning materials.

The value of students developing the capacity to make accurate judgements about the quality of their work and that of others has been widely recognised in higher education literature. However, despite this recognition, little attention has been paid to the development of tools and strategies with the potential both to foster evaluative judgement and to support empirical research into its growth. This paper provides a demonstration of how educational technologies may be used to fill this gap. In particular, we introduce the adaptive learning system RiPPLE and describe how it aims to (1) develop evaluative judgement in large-class settings through suggested strategies from the literature such as the use of rubrics, exemplars and peer review and (2) enable large empirical studies at low cost to determine the effect-size of such strategies. A case study demonstrating how RiPPLE has been used to achieve these goals in a specific context is presented.

Video is becoming a dominant medium for the delivery of educational material. Despite the widespread use of video for learning, there is still a lack of understanding about how best to help people learn in this medium. This study demonstrates the use of thermal camera as compared to traditional self-reported methods for assessing learners' cognitive load while watching video lectures of different styles. We evaluated our approach in a study with 78 university students viewing two variants of short video lectures on two different topics. To incorporate subjective measures, the students reported on mental effort, interest, prior knowledge, confidence, and challenge. Moreover, through a physical slider device, the students could continuously report on their perceived level of difficulty. Lastly, we used thermal sensor as an additional indicator of students' level of difficulty and associated cognitive load. This was achieved through, continuous real-time monitoring of students by using a thermal imaging camera. This study aims to address the following: firstly, to analyze if video styles differ in terms of the associated cognitive load. Secondly, to assess the effects of cognitive load on learning outcomes; could an increase in the cognitive load be associated with poorer learning outcomes? Third, to see if there is a match between students' perceived difficulty levels and a biological indicator. The results suggest that thermal imaging could be an effective tool to assess learners' cognitive load, and an increased cognitive load could lead to poorer performance. Moreover, in terms of the lecture styles, the animated video lectures appear to be a better tool than the text-only lectures (in the content areas tested here). The results of this study may guide future works on effective video designs, especially those that consider the cognitive load.

With the increased reach and impact of video lectures, it is crucial to understand how they are experienced. Whereas previous studies typically present questionnaires at the end of the lecture, they fail to capture students' experience in enough granularity. In this paper we propose recording the lecture difficulty in real-time with a physical slider, enabling continuous and fine-grained analysis of the learning experience. We evaluated our approach in a study with 100 participants viewing two variants of two short lectures. We demonstrate that our approach helps us paint a more complete picture of the learning experience. Our analysis has design implications for instructors, providing them with a method that helps them compare their expectations with students' beliefs about the lectures and to better understand the specific effects of different instructional design decisions.

As a human-centred educational practice and field of research, learning analytics must account for key stakeholders in teaching and learning. The focus of this paper is on the role of institutions to support teachers to incorporate learning analytics into their practice by understanding the confluence of internal and external factors that influence what they do. In this paper, we reconceptualise `teaching as design' for `analytics-enabled teaching as design' to shape this discussion to allow for the consideration of external factors, such as professional learning or ethical considerations of student data, as well as personal considerations, such as data literacy and teacher beliefs and identities. In order to address the real-world challenges of progressing teachers' efficacy and capacity toward analytics-enabled teaching as design, we have placed the teacher - as a cognitive, social, and emotional being - at the center. In so doing, we discuss potential directions towards research for practice in elucidating underpinning factors of teacher inquiry in the process of authentic design.

In order to further the field of learning analytics (LA), researchers and experts may need to look beyond themselves and their own perspectives and expertise to innovate LA platforms and interventions. We suggest that by co-creating with the users of LA, such as educators and students, researchers and experts can improve the usability, usefulness, and draw greater understanding from LA interventions. Within this article we discuss the current LA issues and barriers and how co-creation strategies can help address many of these challenges. We further outline the considerations, both pre- and during interventions, which support and foster a co-created strategy for learning analytics interventions.