Examining Motivational Game Features for Students With Learning Disabilities or Attention Disorders

Students with learning disabilities (LD) or attention disorders (AD) often require supplementary or alternative instruction to achieve their learning goals. Computer-assisted intervention (CAI) has been explored as a promising method for fostering students' success by providing an engaging learning environment. The following chapter examines publications employing empirical studies of computerized games designed for students with LD or AD conducted between 2006-2016. The goal of this chapter is to give a brief overview and critique of the current research on incorporating computerized games into modern education for students with LD or AD, and to identify the key game features that successfully motivate and engage students.

Engaged Mathematics Learning as/for a Transformative Praxis

Engaged learning in mathematics is essential to students' success and has a significant role in creating a transformative path in mathematics education. Considering this, this editorial attempts to highlight the role of behavioural, cognitive, affective and agentic aspects of engaged learning. It is impossible to bring all aspects of this complex and multidimensional construct in this short editorial; however, we try to open a new avenue by bringing the issue of engaged learning into pedagogical practices of mathematics in the context of Nepal. The discourse opens up how disengaged learning creates a mathematical Othering and its detrimental effects on mathematics education. Moreover, this discourse binds with the major features of classroom engagement, conceptualize and the impact of engaged learning on students' success. The editorial ends with a brief overview of the issue.

Developing Jurisprudential Inquiry Based Learning Model of Teaching Fiqh at Madrasah Tsanawiyah

Learning is the most significant activity in education. Through learning activities, the goals of education are achieved. Students’ success in learning mostly depends on their mastery to learn independently and on how they manage their study. The research aims to develop learning model at Madrasah Tsanawiyah, to make the learning process triggers students to be more creative and study better. The method used in the research refers to the nature of Research and Development. The steps of the study are preliminary study, development, and product test. The study found that the development of a jurisprudential inquiry-based learning model for learning Fiqh helps students to improve their learning outcomes. The model has significantly proven effective to support students’ behavior change, thus their achievement increases.

Machine Learning Algorithm to Predict Student’s Performance: A Systematic Literature Review

One of the ultimate goals of the learning process is the success of student learning. Using data and students' achievement with machine learning to predict the success of student learning will be a crucial contribution to everyone involved in determining appropriate strategies to help students perform. The selected 11 research articles were chosen using the inclusion criteria from 2753 articles from the IEEE Access and Science Direct database that was dated within 2019-2021 and 285 articles that were research articles. This study found that the classification machine learning algorithm was most often used in predicting the success of students' learning. Four algorithms that were used most often to predict the success of students' learning are ANN, Naïve Bayes, Logistic Regression, SVM and Decision Tree. Meanwhile, the data used in these research articles predominantly classified students' success in learning into two or three categories which are pass/fail; or fail/pass/excellent.

More alike than different: Novel methods for measuring and modeling executive function development

Executive functions (EFs) are linked to positive outcomes across the lifespan. Yet, methodological challenges have prevented rigorous understanding of the precise ways EFs are organized in childhood and how they develop over time. We introduce novel methods to address these challenges for both measuring and modeling EFs using a large, accelerated longitudinal dataset from a diverse sample of students in middle childhood (approximately ages 8 to 14; N = 1,286). Adaptive assessments allowed us to equate EF challenge across ages and a data-driven, network analytic approach revealed the evolving diversity of EFs while accounting for their unity. Our results suggest EF organization stabilizes around age 10, but continues refining through at least age 14. This approach brings new precision to EFs’ development by removing interpretative ambiguities associated with previous methodologies. By improving EF measurement, the field can move towards improving EF training, to provide a strong foundation for students’ success.

Systematic Literature Review on Machine Learning and Student Performance Prediction: Critical Gaps and Possible Remedies

Improving the quality, developing and implementing systems that can provide advantages to students, and predicting students’ success during the term, at the end of the term, or in the future are some of the primary aims of education. Due to its unique ability to create relationships and obtain accurate results, artificial intelligence and machine learning are tools used in this field to achieve the expected goals. However, the diversity of studies and the differences in their content create confusion and reduce their ability to pioneer future studies. In this study, we performed a systematic literature review of student performance prediction studies in three different databases between 2010 and 2020. The results are presented as percentages by categorizing them as either model, dataset, validation, evaluation, or aims. The common points and differences in the studies are determined, and critical gaps and possible remedies are presented. The results and identified gaps could be eliminated with standardized evaluation and validation strategies. It is determined that student performance prediction studies should be more frequently focused on deep learning models in the future. Finally, the problems that can be solved using a global dataset created by a global education information consortium, as well as its advantages, are presented.

How The Choice of Academic Majors and Students' Future Achievements According to The Talent Path

This article described college majors' choices on future learning achievement according to students' talents. The researcher believes that selecting majors according to talent will determine students' success in the future. So, to prove this assumption, we proved it through a study of several related kinds of literature from several educational and higher education journal publications. The publications in question are, for example, ERIC, Google Book, Elsevier, Sagepub, and Taylor and France, which were published ten years ago. We designed this qualitative study with a phenomenological approach. We explored as much data as possible that addresses student choice significant variables and academic achievement when students enter the study period. The analysis model that we did is through data coding, evaluation, and in-depth interpretation to conclude to answer questions on the principle of validity. Based on the findings and discussion data, we could conclude that there is an influence between the choice of majors and college achievement because achievement will be obtained if the field of study follows students' interests and talents. Without interest and talent, it is challenging to achieve porosity following expectations.

More alike than different: Novel methods for measuring and modeling executive function development

Executive functions (EFs) are linked to positive outcomes across the lifespan. Yet, methodological challenges have prevented rigorous understanding of the precise ways EFs are organized in childhood and how they develop over time. We introduce novel methods to address these challenges for both measuring and modeling EFs using a large, accelerated longitudinal dataset from a diverse sample of students in middle childhood (approximately ages 8 to 14; N = 1,286). Adaptive assessments allowed us to equate EF challenge across ages and a data-driven, network analytic approach revealed the evolving diversity of EFs while accounting for their unity. Our results suggest EF organization stabilizes around age 10, but continues refining through at least age 14. This approach brings new precision to EFs’ development by removing interpretative ambiguities associated with previous methodologies. By improving EF measurement, the field can move towards improving EF training, to provide a strong foundation for students’ success.