Causality relationship among attributes applied in an educational data set

Carvalho, Walisson Ferreira de; Zárate, Luis Enrique

doi:10.1145/3297280.3297406

Cited by 9 publications

(3 citation statements)

References 11 publications

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“…• Causal mining refers to seeking for causal relationship in data. In education, it has been used to identify the characteristics of students' behaviour that contribute to their learning outcomes, problems, grades, etc 68 . Classification and Regression are two widely used prediction methods that have been used for estimating student's performance and characteristics 3,75 .…”

Section: Common Methods In Edm and Lamentioning

confidence: 99%

Towards Mining Creative Thinking Patterns from Educational Data

Shabani¹

2022

Preprint

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Creativity, i.e., the process of generating and developing fresh and original ideas or products that are useful or effective, is a valuable skill in a variety of domains. Creativity is called an essential 21st-century skill that should be taught in schools. The use of educational technology to promote creativity is an active study field, as evidenced by several studies linking creativity in the classroom to beneficial learning outcomes. Despite the burgeoning body of research on adaptive technology for education, mining creative thinking patterns from educational data remains a challenging task. In this paper, to address this challenge, we put the first step towards formalizing educational knowledge by constructing a domain-specific Knowledge Base to identify essential concepts, facts, and assumptions in identifying creative patterns. We then introduce a pipeline to contextualize the raw educational data, such as assessments and class activities. Finally, we present a rule-based approach to learning from the Knowledge Base, and facilitate mining creative thinking patterns from contextualized data and knowledge. We evaluate our approach with real-world datasets and highlight how the proposed pipeline can help instructors understand creative thinking patterns from students' activities and assessment tasks.

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Section: Common Methods In Edm and Lamentioning

confidence: 99%

Towards Mining Creative Thinking Patterns from Educational Data

Shabani¹

2022

Preprint

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“…"There are several different types of educational settings, including traditional education, CBE, and BL (Figure 5). Each of them offers several data sources (Romero & Ventura, 2007) [24].…”

Section: Educational Area and Datamentioning

confidence: 99%

The Importance of Educational Data Mining and Learning Analytics for Improving Teaching and Learning: An Issue Brief

Lalhruaitluanga

2024

IJRITCC

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“The words educational data mining and learning analytics are frequently used interchangeably, despite their being an increase in their investigation and implementation. This may be as a result of the fact that both areas have similar conceptual components. One way to ensure precision, homogeneity, and consistency It aims to pinpoint themes that are similar to and different from one other in the two domains as they develop. This a topic modelling study of papers on educational data mining and learning analytics was carried out in the elucidate the two areas' respective themes. In particular, we used structural topic modelling to find the two domains' subjects from the abstracts. For instructional purposes, we use structural topic modelling on N 1 4192 articles. For both educational data and survey data, we infer five-topic models analytics for mining and learning. While there may be disciplinary variations in research, our findings show that beyond their various lineages, there is no evidence to indicate a clear separation between the two disciplines. the area of educational research on the uses of advanced statistical methods is trending toward convergence for improving teaching and learning, discover how to mine massive data streams for insights that may be put to use. Over the past five years, both areas have converged on a growing emphasis on student behaviour. This study topic has advanced greatly, and a variety of related words, including Academic Analytics, Institutional Analytics, Teaching Analytics, Data-Driven Education, Data-Driven Decision-Making in Education, Big Data in Education, and Educational Data Science, are now used in the paper. The main publications, significant turning points, cycle of knowledge discovery, primary educational settings, specialised tools, freely accessible datasets, widely used methodologies, primary goals, and anticipated trends in this field of study are reviewed to provide the state of the art at this time.

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“…Since the ACM Alan Turing prize was awarded to Judea Pearl in 2011 for his contribution to Machine Learning, the number of papers considering Causal Inference has significantly increased. The researches in causality evolves different fields of knowledges such as application of causality, (de Carvalho and Zarate, 2019); feature selection, (Yang et al , 2019) and (Tsamardinos et al , 2018), missing data (Shpitser and Pearl, 2015).…”

Section: Introductionmentioning

confidence: 99%

A new local causal learning algorithm applied in learning analytics

Carvalho

Zárate

2020

IJILT

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PurposeThe paper aims to present a new two stage local causal learning algorithm – HEISA. In the first stage, the algorithm discoveries the subset of features that better explains a target variable. During the second stage, computes the causal effect, using partial correlation, of each feature of the selected subset. Using this new algorithm, the study aims to identify the actions that lead a student succeed or failure in a course.Design/methodology/approachThe paper presents a brief review of main concepts used in this study: Causal Learning and Causal effects. The paper also discusses the results of applying the algorithm in education data set. Data used in this study was extracted from the log of actions of a Learning Management System, Moodle. These actions represent the behavior of 229 engineering students that take Algorithm and Data Structure course offered in a blended model.FindingsThe algorithm proposed in the paper identifies that features with weak relevance to a target may become relevant when computing the direct effect.Research limitations/implicationsThe algorithm needs to be improved to automatically discard attributes that are under a specific threshold of direct effect. Researchers are also encouraged to test the proposed propositions further.Practical implicationsThe algorithm presented in this paper can be used to identify the mostly relevant features given a classification task.Originality/valueThis paper computes the direct effect of a selected subset of features in a target variable to evaluate if a variable in this subset is really a cause of the target or if it is a spurious correlation.

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Causality relationship among attributes applied in an educational data set

Cited by 9 publications

References 11 publications

Towards Mining Creative Thinking Patterns from Educational Data

Towards Mining Creative Thinking Patterns from Educational Data

The Importance of Educational Data Mining and Learning Analytics for Improving Teaching and Learning: An Issue Brief

A new local causal learning algorithm applied in learning analytics

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