Predictive models on students' academic performance can be built by using historical data for modelling students' learning behaviour. Such models can be employed in educational settings to determine how new students will perform and in predicting whether these students should be classed as at-risk of failing a course. Stakeholders can use…

Massive open online courses offer a valuable platform for efficient and flexible learning. They can improve teaching and learning effectiveness by enabling the evaluation of learning behaviors and the collection of feedback from students. The knowledge map approach constitutes a suitable tool for evaluating and presenting students' learning…

Businesses and other organizations across the globe are becoming more and more data-driven, using a combination of descriptive, diagnostic, predictive and prescriptive analytics to gain a strategic advantage through understanding the past, what we hope to happen in the future, and the ability to accurately predict future outcomes. These forms of…

Individual participant data meta-analysis (IPDMA) projects obtain, check, harmonise and synthesise raw data from multiple studies. When undertaking the meta-analysis, researchers must decide between a two-stage or a one-stage approach. In a two-stage approach, the IPD are first analysed separately within each study to obtain aggregate data (e.g.,…

Feedback on how students progress through completing subgoals can improve students' learning and motivation in programming. Detecting subgoal completion is a challenging task, and most learning environments do so either with "expert-authored" models or with "data-driven" models. Both models have advantages that are…

It is meaningful to create a comprehensive score to extract information from mass continuous data when they measure the same latent concept. Therefore, this study adopts the logic of psychometrics to conduct scales on continuous data under the Rasch models. This study also explores the effect of different data discretization methods on scale…

Methods of network meta-analysis (NMA) can be classified as arm-based and contrast-based approaches. There are several arm-based approaches, and some of these have been criticized because they recover inter-study information and hence do not obey the principle of concurrent control. Here, we point out that recovery of inter-study information in…

Research in education and behavioral sciences often involves the use of latent variable models that are related to indicators, as well as related to covariates or outcomes. Such models are subject to interpretational confounding, which occurs when fitting the model with covariates or outcomes alters the results for the measurement model. This has…

This article reports on a study investigating how computational essays can be used to help students in higher education STEM take up disciplinary epistemic agency--cognitive control and responsibility over one's own learning within the scientific disciplines. Computational essays are a genre of scientific writing that combine live, executable…

Grounded within current reform recommendations and built upon Giere's views (1986, 1999) on model-based science, we propose an alternative approach to science education which we refer to as the "Evidence-Explanation (EE) Continuum." The approach addresses conceptual, epistemological, and social domains of knowledge, and places emphasis…

Machine learning (ML) models are fundamentally shaped by data, and building inclusive ML systems requires significant considerations around how to design representative datasets. Yet, few novice-oriented ML modeling tools are designed to foster hands-on learning of dataset design practices, including how to design for data diversity and inspect…

Technology-based, open-ended learning environments (OELEs) can capture detailed information of students' interactions as they work through a task or solve a problem embedded in the environment. This information, in the form of log data, has the potential to provide important insights about the practices adopted by students for scientific inquiry…

Encouraging teachers to reflect on their instructional practices and course design has been shown to be an effective means of improving instruction and student learning. However, the process of encouraging reflection is difficult; reflection requires quality data, thoughtful analysis, and contextualized interpretation. Because of this, research on…

Data-informed decision-making in teachers' practice, now recommended by different teacher inquiry models and policy documents, implies deep practice change for many teachers. However, not much is known about how teachers perceive the different steps that analytics-informed teacher inquiry entails. This paper presents the results of a study into…

This systematic review sheds light on the role of ontologies in predicting achievement among online learners, in order to promote their academic success. In particular, it looks at the available literature on predicting online learners' performance through ontological machine-learning techniques and, using a systematic approach, identifies the…