A great deal has been written over the past few years about the characteristics of a new generation of students and the implications for teaching and learning. This generation, which has variously been referred to as the ‘Net Generation’, ‘Digital Natives’ and ‘Generation Y’ are claimed to be very different to their predecessors in their familiarity with technologies and the regularity with which they use them. Additionally, some commentators have claimed that their immersion in technology during their developmental years has changed the way that they learn and perhaps even the physiology of their brains. This paper reports on some preliminary results from a large cross-institutional study of the implications for University teaching of the characteristics of this generation of students. This paper focuses in particular on the results of a survey of the frequency with which 2588 first year students at the University of Melbourne, the University of Wollongong and Charles Sturt University, use 41 different applications of new technologies in their study and personal lives. The results indicate that there is greater diversity in frequency of use of technology than many commentators have suggested. Importantly, the use of collaborative and self-publishing ‘Web 2.0’ technologies that have often been associated with this generation is quite low. The results of this large survey suggest that to accept the claims of some of the commentators on the changes needed in universities to cater for this generation of students without undertaking further research is likely to be a substantial mistake.

Research indicates that effective learner-centred feedback requires learner agency, impact and sensemaking.While scholars are focusing on supporting agency and impact, limited research has addressed sensemaking.This is problematic, because if learners fail to understand feedback, impact is likely to be reduced.In response, this study examines (non) alignment between teacher intent and student sensemaking of authentic feedback comments.The sample included four teachers and eighteen students from two Australian universities.Data were collected via stimulated recall interviews and a feedback coding task.The results suggest that sensemaking of strength-based comments, critiques and actionable information was aided when the comments were clear and specific.On the other hand, sensemaking was limited when comments were designed to mitigate against negative affect, overloaded with multiple intentions, or overly brief.This study informs theory around learner-centred feedback design which, in turn, improves the likelihood that teacher comments will be interpreted accurately by learners.

Professional development programs that aim to enhance the use of educational technology in higher education have become a priority in many countries. However, educators’ pedagogical beliefs may present a barrier to the successful outcomes of these programs and are often overlooked. This paper presents a professional development approach designed to make explicit educators’ pedagogical beliefs in regards to educational technology. The outcomes of the study will provide insights into strategies to address educators’ beliefs about teaching, learning and students in general, as a launching pad for improvements in practice to occur.

The construct of student engagement has been useful in understanding students' motivation in digital learning environments where they are required to show increased autonomy and independence in learning. Increasing clarity around this construct has allowed researchers to more accurately describe the nature of student engagement and the context in which it is being investigated. At a task-level, psychological states of engagement have been shown to be beneficial for students' positive learning experience, and performance. Despite this, we still lack knowledge of how these engaged states unfold or sustain during a learning task. In this paper we report on a qualitative study that investigated undergraduate students' experiences of psychological states of engagement in a digital learning task. Findings revealed that the three dimensions of engagement - cognition, affect, and behaviour - changed in intensity, with students experiencing both times of engagement and of not being engaged through the course of a digital learning task.

Essay writing is a fundamental part of higher education. Students’ use of self-regulatory skills, such as time management and planning and writing strategies, while writing essays predicts better writing quality. Current characterisations of the relationship between self-regulation and essay writing are limited by the difficulty of assessing self-regulation in real-life essay writing contexts. This paper reports on a novel approach to examine students’ use of self-regulation strategies in a real-life setting, using learning analytics. Four case studies are presented to illustrate similarities and differences in students’ use of time management, planning and writing strategies. Participants managed their time in very different ways to complete the assignment. They were active over a different number of days, engaged in sessions of different durations, and at different times of the day. The participants used a variety of approaches to their writing: one participant started early and allowed editing time, another typed gradually over a number of days, and two participants waited until the due date to complete the essay, with varying amounts of editing. Findings from this research contribute to a novel detailed empirical evidence of different essay preparation behaviour in real-life settings. After further studies with a variety of essay types and student samples, there may be significant value in using the approached outlined in this paper as the basis of tools they provide students with advice and support in their essay preparation.

Due to recent conceptual shifts towards learner-centred feedback, there is a potential gap between research and practice. Indeed, few models or studies have sought to identify or evaluate which semantic messages, or feedback components, teachers should include in learner-centred feedback comments. Instead, teacher practices are likely to be primarily shaped by 'old paradigm' conceptualisations of feedback. In response, the current study develops a taxonomy of learner-centred feedback components based on a rapid systematic review of the literature. The face, content and construct validity of the taxonomy are then established through an empirical study with teachers and students at two Australian universities. The outcome of this study is a conceptual model featuring eight learner-centred feedback components. This model will help teachers design effective feedback processes and support learners to make sense of and use feedback information to improve their future work and learning strategies.

Teaching students to think and act as scientists through inquiry is at the core of recent science education. Although self-regulated learning (SRL) is acknowledged as crucial to performing scientific inquiry, much is yet to be understood about the specifics of students’ interactions with the scientific process. In the current study, we conducted an exploratory investigation of the role of students’ SRL and related attitudes when completing an online scientific inquirybased task. A task with a Predict-Observe-Explain learning design was used to examine the role of students’ SRL and attitudes within specific phases of the scientific inquiry process. Participants were 233 students from an online undergraduate course. Four groups were identified with differing levels of SRL skills, challenge and confidence. We found that students with low SRL skills who also perceived a learning situation as challenging and had low confidence in their ability to learn, had difficulties designing effective experiments and correctly interpreting data. Implications and future studies are discussed.

Task difficulty (TD) reflects students' subjective judgement on the complexity of a task. We examine the TDs data of 236 undergraduate students in a simulation-based Predict-Observe-Explain (POE) environment using three different labels easy, medium and hard. Generally, the students who perceive the tasks to be easy or hard perform poorly at the transfer task than the students who perceive the tasks to be medium or moderately difficult. Sequences of students' TDs are analysed which consist of a set of several judgements, collected once for each task in a POE sequence. The analysis suggests that given a sequence of TDs, difficulty level hard followed by a hard may lead to poorer learning outcomes at the transfer task. By contrast, difficulty level medium followed by a medium may lead to better learning outcomes at the transfer task. In terms of the TD models, we identify student behaviours that can be reflective of their perceived difficulties. Generally, the students who report that the tasks are easy, adopt a trial-and-error behaviour where they spend lesser time and make more attempts on tasks. By comparison, the students who complete the tasks in a longer time by making more attempts are likely to report that the following task is hard. For the students who report medium TDs, mostly these students seem to reflect on tasks where they spend a long time and require fewer attempts for task completions. Additionally, these students provide longer texts for explaining their hypothesis reasoning. Understanding how student behaviours and TDs manifest over time and how they impact students' learning outcomes is useful, especially when designing for real-time educational interventions, where the difficulty of the tasks could be optimised for students. It can also help in designing and sequencing the tasks for the development of effective teaching strategies that can maximise students' learning.

With an increase in technology to mediate learning and a shift to more student-centred approaches, open-ended online assignment tasks are becoming more common in higher education. Open-ended tasks offer opportunities for students to develop their own interpretations of the requirements, and online technologies offer greater flexibility and afford new types of interactions with teachers and other students. This paper presents a study of students' task interpretation and self-set goals in the context of five open-ended online assignment tasks. The findings presented in this paper demonstrate the importance of a high-quality task understanding for goal setting and suggest practical implications for task design and support.

Current conceptualisations of feedback contend that it should be a learner-centred process. In practice, however, text-based feedback comments from teachers are a convenient and common source of feedback information, despite appearing to be contra-indicative of learner-centred models. This raises the question of how teachers can tackle the design of text-based learner-centred feedback, but at present there is limited empirical evidence available to answer this question. In response, we conducted a rapid review and qualitative synthesis of 95 peer-reviewed scholarly publications on feedback, and appraised the results through the lens of four diverse conceptualisations of learner-centred feedback. This process led to the development of a framework of 12 learner-centred design attributes for text-based feedback, which were organised around three layers of design: contexts, characteristics and components. Each of these design attributes are discussed and practical recommendations are synthesised from the literature. Areas in need of additional empirical research are also highlighted.

Massification is a reality facing universities around the world. While increased access to higher education has significant social and economic benefits, rapid growth in class sizes challenges institutions to maintain quality standards while teaching at scale, amidst ongoing cost pressure. This paper analyses this issue within the Australian higher education context. It employs the notion of the ?Iron Triangle? to examine the tensions between what appear to be mutually conflicting concepts of access, cost and quality. It also highlights key strategies that can be employed to potentially enhance quality without dramatically inflating costs.

One of the main challenges for online learners is knowing how to effectively manage their time. Highly autonomous settings, such as Massive Open Online Courses (MOOCs), put additional pressure on learners in this regard. However, little is known about how learners organise their time in terms of sessions or blocks of time across a MOOC. This study examined session behavioural data of 9272 learners in a MOOC and its relation to their engagement, grade and self-report data measuring aspects of self-regulated learning (SRL). From an exploratory temporal approach using clustering and group comparison tests, we examined how learners distributed sessions in relation to their length and frequency across the course (macro aspect), and which types of activities they prioritised within these sessions (micro aspect). We then investigated if these patterns of sessions were related to learners' level of engagement, achievement and use of self-regulated learning (SRL) skills. We found that successful learners had more frequent and longer sessions across the course, mixed up activities within sessions, and changed the focus of activities within sessions across the course. In addition, session distribution was found to be a meaningful proxy for learners’ use of SRL skills related to time management and effort regulation. That is, learners with higher levels of time management and effort regulation had longer and more sessions across the course. Based on the results, implications for supporting specific session behaviours to promote effective learning in MOOCs are discussed.

Essay tasks are a widely used form of assessment in higher education. Writing analytics can assist with challenges related to using essay tasks at scale and to identifying different issues in academic integrity. In this paper, we combined two techniques to investigate how students’ writing analytics varied across essay tasks with different cognitive load, considering both their typing behavior (i.e., writing process) and writing style (i.e., writing product). We also examined their relationship across these essay tasks. Findings showed that writing processes change across tasks with different cognitive load: when cognitive load increases, the interword intervals (indicator of planning and/or reviewing processes) increased, the burst length (indicator of translation processes) decreased, and the number of revisions per minute (indicator of reviewing processes) decreased. In contrast to the relation between the writing process and cognitive load, the relation between the writing product and cognitive load was found less clear. The results showed small and mixed effects of the tasks differing in cognitive load on the different writing product metrics. Hence, although the writing product follows from the writing process, the relation between cognitive load and the writing product and process appears to be less straightforward.

Online learning environments are now pervasive in higher education. While not exclusively the case, in these environments, there is often modest teacher presence, and students are provided with access to a range of learning, assessment, and support materials. This places pressure on their study skills, including self-regulation. In this context, students may access assessment material without being fully prepared. This may result in limited success and, in turn, raise a significant risk of disengagement. Therefore, if the prediction of students' assessment readiness was possible, it could be used to assist educators or online learning environments to postpone assessment tasks until students were deemed "ready". In this study, we employed a range of machine learning techniques with aggregated and sequential representations of students' behaviour in a Massive Open Online Course (MOOC), to predict their readiness for assessment tasks. Based on our results, it was possible to successfully predict students' readiness for assessment tasks, particularly if the sequential aspects of behaviour were represented in the model. Additionally, we used sequential pattern mining to investigate which sequences of behaviour differed between high or low level of performance in assessments. We found that a high level of performance had the most sequences related to viewing and reviewing the lecture materials, whereas a low level of performance had the most sequences related to successive failed submissions for an assessment. Based on the findings, implications for supporting specific behaviours to improve learning in online environments are discussed.

Task difficulty (TD) reflects students’ subjective judgement on the complexity of a task. We examine the task difficulty sequence data of 236 undergraduate students in a simulation-based Predict-Observe-Explain environment. The findings suggest that if students perceive the TDs as easy or hard, it may lead to poorer learning outcomes, while the medium or moderate TDs may result in better learning outcomes. In terms of TD transitions, difficulty level hard followed by a hard may lead to poorer learning outcomes. By contrast, difficulty level medium followed by a medium may lead to better learning outcomes. Understanding how task difficulties manifest over time and how they impact students’ learning outcomes is useful, especially when designing for real-time educational interventions, where the difficulty of the tasks could be optimised for students. It can also help in designing and sequencing the tasks for the development of effective teaching strategies that can maximize students’ learning.

Confusion is an important epistemic emotion because it can help students focus their attention and effort when solving complex learning tasks. However, unresolved confusion can be detrimental because it may result in students’ disengagement. This is especially concerning in simulation environments using discovery-based learning, which puts more of the onus for learning on the students. Thus, students with misconceptions may become confused. In this study, the possible moments of confusion in a simulation-based predict-observe-explain (POE) environment were investigated. Log-based interaction patterns of undergraduate students from a fully online course were analyzed. It was found that POE environments can offer a level of difficulty that potentially triggers some confusion, and a likely moment of students’ confusion was the observe task. It was also found that confidence in prior knowledge is an important factor that can contribute to students’ confusion. Students mostly struggled when they discovered a mismatch between the subjective and objective correctness of their responses. The effects of such a mismatch were more pronounced when confusion markers were analyzed than when students’ learning outcomes were observed. These findings may guide future works to bridge the knowledge gaps that lead to confusion in POE environments.

Emotions play a significant part in students' learning experiences within complex educational environments. However, the impact of emotional experiences on effective learning is not straightforward. For example, being confused during learning may be perceived as an adverse event. There is, however, considerable research evidence suggesting that confusion can also be a productive aspect of a student's learning processes. Despite this, research also suggests that when confusion is persistent it can become harmful, promoting learner frustration or boredom. Key challenges for the design of interactive digital learning environments (IDLEs) are to detect and assess students' emotions and to tailor the environment accordingly. In this paper we examine, from a review of the literature, the implications of learners' confusion that can occur in IDLEs. Strategies for managing students' confusion will then be discussed and examples of features enhancing learning in IDLEs will be suggested.

Keystroke logging and clickstream data, both emergent areas of study in the field of learning analytics, present promising alternative methods of detecting and preventing contract cheating. The current study examines whether analysis of keystroke and clickstream data can detect when a student is creating their own authentic writing or transcribing from another source. Participants were 62 university students (47 women, 15 men) who completed three writing tasks under experimental conditions: free writing, general transcription, and self-transcription. Analyses revealed that while completing the free-writing task, participants typed in shorter bursts with longer pauses and typed more slowly with more revisions compared to the two transcription tasks. Model-based clustering was able to accurately distinguish the free-writing task from the two transcription tasks based on patterns of bursts and writing speed. Overall, these results suggest that keystroke and clickstream analysis may be able to distinguish between a student writing an authentic piece of work and one transcribing a completed work. These findings signal significant implications for the detection of contract cheating.