Higher education faces increasing pressure from governments and employers to ensure graduates are equipped with the knowledge and capabilities required for the future workplace. While technical knowledge is assessed through grades, professional skills are complex and remain difficult to evaluate systematically. While curriculum mapping offers a potential solution, it is often applied in a simplistic, accreditation-driven manner that merely records the presence or absence of skills embedded in assessments. Such an approach overlooks the relative contribution of each skill to the assessments and, hence, cannot be used to estimate skill development. Other noted approaches have relied on the use of self-assessment reports or surveys, and as such are subjective and cannot provide longitudinal evidence of skill development. This study addresses these limitations by proposing a novel curriculum analytics method, weighted Performance Factor Analysis, to model skill scores using assessment grades and granular weighted skill-assessment mapping. Students’ development of seven professional skills were examined along with how they transition across an accounting degree program. The findings show distinct patterns and trajectories of skill development. Overall, the study makes a significant methodological contribution to measuring skills and offers insights into how graduates develop their professional skills across the curriculum.

The proliferation of Artificial Intelligence (AI) is fundamentally reshaping creative work and education. While research has demonstrated AI's potential to enhance creative outcomes, the processes underlying human-AI co-creation remain insufficiently explored. This study investigates the dynamics of human-AI co-creation in an undergraduate game design course by leveraging Learning Analytics and Epistemic Network Analysis. Specifically, it examines how students’ creative thinking types (divergent, convergent, and evaluative thinking) interact with their contribution types (creating new ideas, extending ideas, refining ideas, and transforming ideas) during co-creation with AI. Results reveal the collaborative role of different thinking types in the student-AI co-creative process. Divergent thinking primarily drives the generation of new ideas, while convergent and evaluative thinking are essential for refinement and quality control. These cognitive-behavioral relationships evolve across learning phases, shifting from open exploration to focused integration. Furthermore, high-achieving students strategically employ convergent thinking and engage in continuous refinement and transformation with AI, whereas lower-achieving students tend to remain at the exploratory stage and rely on initial AI outputs. Practically, the findings underscore the importance of supporting students in shifting between creative thinking modes, promoting critical evaluation, and offering phase-specific guidance to facilitate effective and meaningful co-creation with AI in educational settings.

Learning Analytics (LA) aims to provide university instructors with meaningful data and insights that can be used to improve courses. However, instructors are often met with challenges that arise when wanting to use LA to inform their educational design decisions. For instance, there may be a misalignment between instructors’ needs and the data and insights LA systems provide. Further research is required to understand instructors’ expectations of LA and how it can support the diversity of educational designs. This case study addresses this gap by investigating the role of LA in instructors’ educational decision-making processes. The study employs self-determination theory's constructs to examine instructors’ existing practices when using LA to support their decision-making. The study reveals that LA enables instructors to make data-informed iterative educational design decisions, supporting their need for competence and relatedness. The emotional aspect of LA is an important consideration that can easily lead to demotivation and avoidance of LA. Support is needed to address instructors’ psychological needs so instructors can fully utilise LA to make effective educational design decisions. The findings inform a framework for considering how instructors’ data-informed educational decision-making can be understood. The implications of our findings and opportunities for the future are discussed.

Ensuring constructive alignment between learning outcomes (LOs) and assessment design is crucial to effective learning design (LD). While previous research has explored the alignment of LOs with assessments, there is a lack of empirical studies on how assessment design influences LO mastery, particularly the relationship between formative and summative assessments. To address this gap, we conducted an empirical study within an undergraduate mathematics course. First, we evaluated the course's learning design to identify potential gaps in constructive alignment. Then, using a sample of 169 students, we analysed their assessment results to explore how LO mastery is demonstrated through formative and summative assessments. This study provides a novel learning analytics (LA) methodology by combining cognitive diagnostic models, epistemic network analysis, and social network analysis to examine LO mastery and interdependencies. Our findings reveal a strong connection between the mastery of LOs through formative and summative assessments, underscoring the importance of well-constructed LD. The practical implications suggest that LA can serve as a critical tool for quality assurance by guiding the revision of LOs and optimising LD to foster deeper student engagement and mastery of critical concepts. These insights offer actionable pathways for more targeted, student-centered teaching practices.

In response to the evolving demands of the contemporary workplace, higher education (HE) institutions are increasingly emphasising the development of transversal skills and graduate attributes (GAs). The development of GAs, such as effective communication, collaboration, and lifelong learning, are non-linear and follow distinct trajectories for individual learners. The ability to trace and measure the progression of GA remains a significant challenge. While previous studies have focused on empirical methods for measuring GAs in individual courses, a notable gap exists in understanding their longitudinal development within HE programs. To address this research gap, our study focuses on measuring and tracing the development of GAs in an Initial Teacher Education (ITE) undergraduate program at a large public university in Australia. By combining learning analytics (LA) with psychometric models, we analysed students’ assessment grades to measure learners’ GA development in each year of the ITE program. The resulting measurements enabled the identification of distinct profiles of GA attainment, as demonstrated by learners and their distinct pathways. The overall approach allows for a comprehensive representation of a learner's progress throughout the program of study. As such, the developed approach sets the grounds for more personalised learning support, program evaluation, and improvement of students’ GA attainment.

The term Learner Profile has proliferated over the years, and more recently, with the increased advocacy around personalising learning experiences. Learner profiles are at the center of personalised learning, and the characterisation of diversity in classrooms is made possible by profiling learners based on their strengths and weaknesses, backgrounds and other factors influencing learning. In this paper, we discuss three common approaches of profiling learners based on students’ cognitive knowledge, skills and competencies and behavioral patterns, all latter commonly used within Learning Analytics (LA). Although each approach has its strengths and merits, there are also several disadvantages that have impeded adoption at scale. We propose that the broader adoption of learner profiles can benefit from careful combination of the methods and practices of three primary approaches, allowing for scalable implementation of learner profiles across educational systems. In this regard, LA can leverage from other aligned domains to develop valid and rigorous measures of students' learning and propel learner profiles from education research to more mainstream educational practice. LA could provide the scope for monitoring and reporting beyond an individualised context and allow holistic evaluations of progress. There is promise in LA research to leverage the growing momentum surrounding learner profiles and make a substantial impact on the field's core aim - understanding and optimising learning as it occurs.

Industry supervisors play a pivotal role in ongoing learner support and guidance within a work-integrated learning context. Effective provisional feedback from industry supervisors in work-integrated learning environments is essential for increasing a team’s metacognitive awareness and ability to evaluate their performance. However, research that examines the usefulness and type of feedback from industry supervisors for teams remains limited. In this study, we investigate the quality of provisional feedback by comparing the teams’ helpfulness rating of the feedback from two types of industry supervisors (i.e., clients and mentors), based on the feedback type (task, process, regulatory and self-level oriented) using learning analytics. The results show that teams rated the perceived helpfulness scores of clients and mentors as very useful, with mentors providing slightly more helpful feedback. We also found that mentors provide more co-occurrences of feedback classifications than clients. The overall results show that teams perceive mentor feedback as more helpful than clients and that the mentor targets feedback that is more beneficial to the teams learning than the clients. Our findings can aid in developing guidelines that aim to validate and improve existing or new feedback quality frameworks by leveraging backward evaluation data.

Roles that learners assume during online discussions are an important aspect of educational experience. The roles can be assigned to learners and/or can spontaneously emerge through student-student interaction. While existing research proposed several approaches for analytics of emerging roles, there is limited research in analytic methods that can i) automatically detect emerging roles that can be interpreted in terms of higher-order constructs of collaboration; ii) analyse the extent to which students complied to scripted roles and how emerging roles compare to scripted ones; and iii) track progression of roles in social knowledge progression over time. To address these gaps in the literature, this paper propose a network-analytic approach that combines techniques of cluster analysis and epistemic network analysis. The method was validated in an empirical study discovered emerging roles that were found meaningful in terms of social and cognitive dimensions of the well-known model of communities of inquiry. The study also revealed similarities and differences between emerging and script roles played by learners and identified different progression trajectories in social knowledge construction between emerging and scripted roles. The proposed analytic approach and the study results have implications that can inform teaching practice and development techniques for collaboration analytics.

Collaboration is considered as one of the main drivers of learning and it has been broadly studied across numerous contexts, including Massive Open Online Courses (MOOCs). The research on MOOCs has risen exponentially during the last years and there have been a number of works focused on studying collaboration. However, these previous studies have been restricted to the analysis of collaboration based on the forum and social interactions, without taking into account other possibilities such as the synchronicity in the interactions with the platform. Therefore, in this work we performed a case study with the goal of implementing a data-driven approach to detect and characterize collaboration in MOOCs. We applied an algorithm to detect synchronicity links based on their submission times to quizzes as an indicator of collaboration, and applied it to data from two large Coursera MOOCs. We found three different profiles of user accounts, that were grouped in couples and larger communities exhibiting different types of associations between user accounts. The characterization of these user accounts suggested that some of them might represent genuine online learning collaborative associations, but that in other cases dishonest behaviors such as free-riding or multiple account cheating might be present. These findings call for additional research on the study of the kind of collaborations that can emerge in online settings.

This paper presents a study that examined automated cross-language classification of online discussion messages for the levels of cognitive presence, a key construct from the widely used Community of Inquiry (CoI) model of online learning. Specifically, we examined the classification of 1,500 Portuguese language discussion messages using a classifier trained on a corpus of the 1,747 English language discussion messages. In the study, a random forest classifier was developed using a small set of 108 validated indicators of psychological processes, linguistic coherence, and online discussion structure. The classifier obtained 67% accuracy and Cohen's κ of 0.32, showing a moderate level of inter-rater agreement above chance and the general viability of the proposed approach. Most importantly, the findings suggest that certain aspects of cognitive presence construct are highly generalizable and transfer across different languages. Finally, the paper also presents a novel method for addressing class imbalance problem using a generic algorithm heuristic technique, which provided substantial improvements over the use of imbalanced dataset. Results and practical implications are further discussed.

Feedback is a major factor of student success within higher education learning. However, recent changes - such as increased class sizes and socio-economic diversity of the student population - challenged the provision of effective student feedback. Although the use of educational technology for personalised feedback to diverse students has gained traction, the feedback gap still exists: educators wonder which students respond to feedback and which do not. In this study, a set of trackable Call to Action (CTA) links was embedded in two sets of feedback messages focusing on students' time management, with the goal of (1) examining the association between feedback engagement and course success and (2), to predict students' reaction to provided feedback. We also conducted two focus groups to further examine students' perception of provided feedback messages. Our results revealed that early engagement with the feedback was associated with higher chances of succeeding in the course. Likewise, previous engagement with feedback was highly predictive of students' engagement in the future, and also that certain student sub-populations, (e.g., female students), were more likely to engage than others. Such insight enables instructors to ask "why" questions, improve feedback processes and narrow the feedback gap. Practical implications of our findings are further discussed.

This paper presents an approach to studying relationships between students' social presence and course topics from transcripts of asynchronous discussions in online learning environments. Specifically, the paper uses topic modeling and epistemic network analysis to investigate how students' social presence is expressed across different course topics. Finally, we show how this method can be adopted to examine how students' social presence changed due to an instructional intervention. The results of this study and its implications are further discussed.

Student engagement is often considered an overarching construct in educational research and practice. Though frequently employed in the learning analytics literature, engagement has been subjected to a variety of interpretations and there is little consensus regarding the very definition of the construct. This raises grave concerns with regards to construct validity: namely, do these varied metrics measure the same thing? To address such concerns, this paper proposes, quantifies, and validates a model of engagement which is both grounded in the theoretical literature and described by common metrics drawn from the field of learning analytics. To identify a latent variable structure in our data we used exploratory factor analysis and validated the derived model on a separate sub-sample of our data using confirmatory factor analysis. To analyze the associations between our latent variables and student outcomes, a structural equation model was fitted, and the validity of this model across different course settings was assessed using MIMIC modeling. Across different domains, the broad consistency of our model with the theoretical literature suggest a mechanism that may be used to inform both interventions and course design.

Reflective writing has been widely recognized as one of the most effective activities for fostering students' reflective and critical thinking. The analysis of students' reflective writings has been the focus of many research studies. However, to date this has been typically a very labor-intensive manual process involving content analysis of student writings. With recent advancements in the field of learning analytics, there have been several attempts to use text analytics to examine student reflective writings. This paper presents the results of a study examining the use of theoretically-sound linguistic indicators of different psychological processes for the development of an analytics system for assessment of reflective writing. More precisely, we developed a random-forest classification system using linguistic indicators provided by the LIWC and Coh-Metrix tools. We also examined what particular indicators are representative of the different types of student reflective writings.

In 2011, the phenomenon of MOOCs had swept the world of education and put online education in the focus of the public discourse around the world. Although researchers were excited with the vast amounts of MOOC data being collected, the benefits of this data did not stand to the expectations due to several challenges. The analyses of MOOC data are very time-consuming and labor-intensive, and require and require a highly advanced set of technical skills, often not available to the education researchers. Because of this MOOC data analyses are rarely done before the courses end, limiting the potential of data to impact the student learning outcomes and experience.

In this poster, we present the results of the study which examined the relationship between student differences in their use of the available technology and their perceived levels of cognitive presence within the MOOC context. The cognitive presence is a construct used to measure the level of practical inquiry in the Communities of Inquiry model. Our results revealed the existence of three clusters based on student technology use. The clusters significantly differed in terms of their levels of cognitive presence, most notably they differed on the levels of problem resolution.

There is an emerging trend in higher education for the adoption of massive open online courses (MOOCs). However, despite this interest in learning at scale, there has been limited work investigating how MOOC participants have changed over time. In this study, we explore the temporal changes in MOOC learners' language and discourse characteristics. In particular, we demonstrate that there is a clear trend within a course for language in discussion forums to be of both more on-topic and reflective of deep learning in subsequent offerings of a course. We measure this in two ways, and demonstrate this trend through several repeated analyses of different courses in different domains. While not all courses show an increase beyond statistical significance, the majority do, providing evidence that MOOC learner populations are changing as the educational phenomena matures.

The goal of this tutorial is to share data mining tools and techniques used by computer scientists with educational social scientists. We broadly define educational social scientists as being made up of people with backgrounds in the learning sciences, cognitive psychology, and educational research. The learning analytics community is heavily populated with researchers of these backgrounds, and we believe those that find themselves at the intersection of research, theory, and practice have a particular interest in expanding their knowledge of datadriven tools and techniques.

As the field of learning analytics continues to mature, there is a corresponding evolution and sophistication of the associated analytical methods and techniques. In this regard social network analysis (SNA) has emerged as one of the cornerstones of learning analytics methodologies. However, despite the noted importance of social networks for facilitating the learning process, it remains unclear how and to what extent such network measures are associated with specific learning outcomes. Motivated by Simmel's theory of social interactions and building on the argument that social centrality does not always imply benefits, this study aimed to further contribute to the understanding of the association between students' social centrality and their academic performance. The study reveals that learning analytics research drawing on SNA should incorporate both - descriptive and statistical methods to provide a more comprehensive and holistic understanding of a students' network position. In so doing researchers can undertake more nuanced and contextually salient inferences about learning in network settings. Specifically, we show how differences in the factors framing students' interactions within two instances of a MOOC affect the association between the three social network centrality measures (i.e., degree, closeness, and betweenness) and the final course outcome.

In this paper, we present the results of an exploratory study that examined the problem of automating content analysis of student online discussion transcripts. We looked at the problem of coding discussion transcripts for the levels of cognitive presence, one of the three main constructs in the Community of Inquiry (CoI) model of distance education. Using Coh-Metrix and LIWC features, together with a set of custom features developed to capture discussion context, we developed a random forest classification system that achieved 70.3% classification accuracy and 0.63 Cohen's kappa, which is significantly higher than values reported in the previous studies. Besides improvement in classification accuracy, the developed system is also less sensitive to overfitting as it uses only 205 classification features, which is around 100 times less features than in similar systems based on bag-of-words features. We also provide an overview of the classification features most indicative of the different phases of cognitive presence that gives an additional insights into the nature of cognitive presence learning cycle. Overall, our results show great potential of the proposed approach, with an added benefit of providing further characterization of the cognitive presence coding scheme.

Massive Open Online Courses represent a fertile ground for examining student behavior. However, due to their openness MOOC attract a diverse body of students, for the most part, unknown to the course instructors. However, a certain number of students enroll in the same course multiple times, and there are records of their previous learning activities which might provide some useful information to course organizers before the start of the course. In this study, we examined how student behavior changes between subsequent course offerings. We identified profiles of returning students and also interesting changes in their behavior between two enrollments to the same course. Results and their implications are further discussed.

Connections established between learners via interactions are seen as fundamental for connectivist pedagogy. Connections can also be viewed as learning outcomes, i.e. learners' social capital accumulated through distributed learning environments. We applied linear mixed effects modeling to investigate whether the social capital accumulation interpreted through learners' centrality to course interaction networks, is influenced by the language learners use to express and communicate in two connectivist MOOCs. Interactions were distributed across the three social media, namely Twitter, blog and Facebook. Results showed that learners in a cMOOC connect easier with the individuals who use a more informal, narrative style, but still maintain a deeper cohesive structure to their communication.

Creating meaning from a wide variety of available information and being able to choose what to learn are highly relevant skills for learning in a connectivist setting. In this work, various approaches have been utilized to gain insights into learning processes occurring within a network of learners and understand the factors that shape learners' interests and the topics to which learners devote a significant attention. This study combines different methods to develop a scalable analytic approach for a comprehensive analysis of learners' discourse in a connectivist massive open online course (cMOOC). By linking techniques for semantic annotation and graph analysis with a qualitative analysis of learner-generated discourse, we examined how social media platforms (blogs, Twitter, and Facebook) and course recommendations influence content creation and topics discussed within a cMOOC. Our findings indicate that learners tend to focus on several prominent topics that emerge very quickly in the course. They maintain that focus, with some exceptions, throughout the course, regardless of readings suggested by the instructor. Moreover, the topics discussed across different social media differ, which can likely be attributed to the affordances of different media. Finally, our results indicate a relatively low level of cohesion in the topics discussed which might be an indicator of a diversity of the conceptual coverage discussed by the course participants.

All forms of learning take time. There is a large body of research suggesting that the amount of time spent on learning can improve the quality of learning, as represented by academic performance. The wide-spread adoption of learning technologies such as learning management systems (LMSs), has resulted in large amounts of data about student learning being readily accessible to educational researchers. One common use of this data is to measure time that students have spent on different learning tasks (i.e., time-on-task). Given that LMS systems typically only capture times when students executed various actions, time-on-task measures are estimated based on the recorded trace data. LMS trace data has been extensively used in many studies in the field of learning analytics, yet the problem of time-on-task estimation is rarely described in detail and the consequences that it entails are not fully examined.