Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. This paper surveys the application of data mining ...to traditional educational systems, particular web-based courses, well-known learning content management systems, and adaptive and intelligent web-based educational systems. Each of these systems has different data source and objectives for knowledge discovering. After preprocessing the available data in each case, data mining techniques can be applied: statistics and visualization; clustering, classification and outlier detection; association rule mining and pattern mining; and text mining. The success of the plentiful work needs much more specialized work in order for educational data mining to become a mature area.
We review the comparative literature on the impact of national-level educational institutions on inequality in student achievement. We focus on two types of institutions that characterize the ...educational system of a country: the system of school-type differentiation (between-school tracking) and the level of standardization (e.g., with regard to central examinations and school autonomy). Two types of inequality are examined: inequality in terms of dispersion of student test scores and inequality of opportunity by social background and race/ethnicity. We conclude from this literature, which mostly uses PISA, TIMSS, and/or PIRLS data, that inequalities are magnified by national-level tracking institutions and that standardization decreases inequality. Methodological issues are discussed, and possible avenues for further research are suggested.
An influential literature in criminology has identified indirect "collateral consequences" of mass imprisonment. We extend this criminological perspective to the context of the U.S. education system, ...conceptualizing exclusionary discipline practices (i.e., out-of-school suspension) as a manifestation of intensified social control in schools. Similar to patterns of family and community decline associated with mass incarceration, we theorize that exclusionary discipline policies have indirect adverse effects on non-suspended students in punitive schools. Using a large hierarchical and longitudinal dataset consisting of student and school records, we examine the effect of suspension on reading and math achievement. Our findings suggest that higher levels of exclusionary discipline within schools over time generate collateral damage, negatively affecting the academic achievement of nonsuspended students in punitive contexts. This effect is strongest in schools with high levels of exclusionary discipline and schools with low levels of violence, although the adverse effect of exclusionary discipline is evident in even the most disorganized and hostile school environments. Our results level a strong argument against excessively punitive school policies and suggest the need for alternative means of establishing a disciplined environment through social integration.
This panel will focus on the emerging area of Learning Engineering. Learning Engineering is a transdisciplinary area focusing on the systematic application of evidence-based principles from science ...of learning disciplines to create effective learning experiences, addressing the challenges of learners. During the panel, examples of Learning Engineering will be presented of interest to anyone within human factors and ergonomics with interest in education, training, or usability/design science. The panel will represent experience from both academia and industry. The goal of this panel is to foster dialog between the IEEE Industry Connections Industry Consortium on Learning Engineering (ICICLE) and HFES members in the hope of increasing knowledge of Learning Engineering and creating ties between the two organizations.
The concept of cultural capital has proved invaluable in understanding educational systems in Western countries, and recent work seeks to extend those insights to the diverse educational systems of ...other geographic regions. Using data from the 2000 Programme for International Student Assessment, the authors explored cultural capital in South Korea by investigating the relationships among family socioeconomic status (SES), cultural capital, and children's academic achievement. South Korea was compared with Japan, France, and the United States to understand how institutional features of South Korean education shape the role of cultural capital in academic success. Results showed that family SES had a positive effect on both parental objectified cultural capital and children's embodied cultural capital in South Korea, consistent with evidence from the other countries. Moreover, parental objectified cultural capital had a positive effect on children's academic achievement in South Korea. In contrast to other countries, however, children's embodied cultural capital had a negative effect on academic achievement in South Korea, controlling for the other variables. The authors highlighted several institutional features of South Korean education, including a standardized curriculum, extreme focus on test preparation, and extensive shadow education, which may combine to suppress the effect of children's embodied cultural capital on academic achievement.
La pandemia causada por el COVID-19 ha visibilizado las precariedades y desigualdades a las que se enfrenta la educación en todo el mundo. El presente artículo tiene como finalidad analizar el ...impacto de la pandemia en el sistema educativo público de Honduras, uno de los países con mayor desigualdad a nivel mundial. Para cumplir con el objetivo, el trabajo se estructura en cuatro apartados. Primero, se describe la realidad de la sociedad hondureña y su sistema educativo. En un segundo apartado, se especifican las acciones propuestas por los organismos gubernamentales hacia los procesos formativos en tiempos de crisis. En tercer lugar, se detallan los desafíos que debe enfrentar el sistema educativo hondureño. Finalmente, se plantean algunas alternativas con el propósito de continuar en el proceso de enseñanza-aprendizaje.
•We assess the learning status of undergraduate and postgraduate students during the COVID-19 pandemic.•About 70% of learners reported that they were involved in e-learning during the ...lockdown.•Students have been facing several challenges related to the study during this crisis period.•Strategies are urgently needed to build a resilient education system that will ensure to develop the skill of the young minds.
To assess the impact of lockdown amidst COVID-19 on undergraduate and postgraduate learners of various colleges and universities of West Bengal. An online survey was conducted from 1 May to 8 May 2020 to collect the information. A structural questionnaire link using ‘Google form’ was sent to students’ through WhatsApp and E-mail. A total of 232 students provided complete information regarding the survey. The simple percentage distribution was used to assess the learning status of the study participants. During the lockdown period, around 70% of learners were involved in e-learning. Most of the learners were used android mobile for attending e-learning. Students have been facing various problems related to depression anxiety, poor internet connectivity, and unfavorable study environment at home. Students from remote areas and marginalized sections mainly face enormous challenges for the study during this pandemic. This study suggests targeted interventions to create a positive space for study among students from the vulnerable section of society. Strategies are urgently needed to build a resilient education system in the state that will ensure to develop the skill for employability and the productivity of the young minds.
Artificial Intelligence (AI) has penetrated the field of education. Trust has long been regarded as a driver for the acceptance of technology. Netnography and interviews were used to investigate ...trust in AI‐based educational systems from the perspective of users. We identified the factors influencing trust in AI‐based educational systems and categorized them as being related to technology, context and individual. Technology‐related factors encompass functionality, helpfulness, interpretability, dependability and interaction interface. Context‐related factors encompass benevolence of educational organizations, data management, teachers’ competencies, official norms and knowledge characteristics. Individual‐related factors encompass perception of the nature of learning, propensity to interact with teachers, perception of AI and autonomy orientation. The results from this paper will contribute to the literature on trust in technology and AI ethics in education.
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