Background: Science education should encompass enculturation in science which implies performing scientific practices such as use of data and modelling, in authentic contexts like the field. Purpose: This work aims to determine how preservice elementary teachers (PETs) use data obtained in the field, and how these data contribute to the process of…

This systematic literature review critically evaluates 14 empirical studies published over a 14 years span (2006-2019) to answer questions about the models and the effects of transformational school leadership on student academic achievement. The analysis of the related literature utilized vote counting and narrative synthesis to delineate the…

However, although existing models for evaluating the effectiveness of universities provide a large number of modeling solutions, it is difficult to objectively evaluate dynamic coefficients based on the differences in precision ideological and political work systems of different types of universities in the evaluation process of innovative paths…

Social sciences expose many cognitively complex, highly qualified, or fuzzy problems, whose resolution relies primarily on expert judgement rather than automated systems. One of such instances that we study in this work is a reflection analysis in the writings of student teachers. We share a hands-on experience on how these challenges can be…

This article provides a conceptual framework for considering both the theoretical and methodological factors that underpin the successful validation of a competency framework. Drawing on educational assessment literature, this article argues that a valid competency framework relates to an interpretive judgement of the credibility of the claims…

The last few years have witnessed an upsurge in the number of studies using Machine and Deep learning models to predict vital academic outcomes based on different kinds and sources of student-related data, with the goal of improving the learning process from all perspectives. This has led to the emergence of predictive modelling as a core practice…

Traditional statistical model evaluation typically relies on goodness-of-fit testing and quantifying model complexity by counting parameters. Both of these practices may result in overfitting and have thereby contributed to the generalizability crisis. The information-theoretic principle of minimum description length addresses both of these…

Educational data mining helps the educational institutions to perform effectively and efficiently by exploiting the data related to all its stakeholders. It can help the at-risk students, develop recommendation systems and alert the students at different levels. It is beneficial to the students, educators and authorities as a whole. Deep learning…

Interactive learning is a two-way learning method of learners independently by using computer and network technology. In the interactive relationships, interactive learning plays a role for learners to achieve the learning purpose, interactive learning has become an important effect of online learning, but it also has many problems that need to be…

A version of the classic rotationally resolved infrared (IR) spectrum of a diatomic molecule experiment has been developed using the POGIL framework to more fully engage students in the collection, modeling, analysis, and interpretation of the data. An analysis of the experimental protocol reveals that the POGIL approach actively engages students…

In the context of the rapid expansion of web-integrated technologies, there exists a need for a thorough comprehension of data privacy, emphasizing risk mitigation and the nuanced governance of private data within organizational and regulatory frameworks. Given the rise of privacy regulations such as the GDPR and CCPA, it has become important for…

Thinking about knowledge and knowing ("i.e.", epistemic cognition) is an important part of student learning and has implications for how they apply their knowledge in future courses, careers, and other aspects of their lives. Three classes of models have emerged from research on epistemic cognition: developmental models, dimensional…

Predictive analytics are increasingly pervasive in higher education. However, algorithmic bias has the potential to reinforce racial inequities in postsecondary success. We provide a comprehensive and translational investigation of algorithmic bias in two separate prediction models--one predicting course completion, the second predicting degree…

Machine learning-based modeling technology has recently become a powerful technique and tool for developing models for explaining, predicting, and describing system/human behaviors. In developing intelligent education systems or technologies, some research has focused on applying unique machine learning algorithms to build the ad-hoc student…

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…