Data Science Approach for Simulating Educational Data: Towards the Development of Teaching Outcome Model (TOM)

Ndukwe, Ifeanyi G.; Daniel, Ben Kei; Butson, Russell

doi:10.3390/bdcc2030024

Cited by 12 publications

(4 citation statements)

References 35 publications

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“…As a result of this growing use of technology, nowadays we can obtain data from almost everywhere. The increasing availability of educational data provides the educational researcher with numerous opportunities to use analytics to extract useful knowledge to enhance teaching and learning [ 20 ]. A game (educational or not) can generate vast amounts of interaction data, even in a short game-play session.…”

Section: Related Workmentioning

confidence: 99%

Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game

Gómez

Ruipérez‐Valiente

Andreo-Martínez

et al. 2021

Sensors

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Games have become one of the most popular activities across cultures and ages. There is ample evidence that supports the benefits of using games for learning and assessment. However, incorporating game activities as part of the curriculum in schools remains limited. Some of the barriers for broader adoption in classrooms is the lack of actionable assessment data, the fact that teachers often do not have a clear sense of how students are interacting with the game, and it is unclear if the gameplay is leading to productive learning. To address this gap, we seek to provide sequence and process mining metrics to teachers that are easily interpretable and actionable. More specifically, we build our work on top of Shadowspect, a three-dimensional geometry game that has been developed to measure geometry skills as well other cognitive and noncognitive skills. We use data from its implementation across schools in the U.S. to implement two sequence and process mining metrics in an interactive dashboard for teachers. The final objective is to facilitate that teachers can understand the sequence of actions and common errors of students using Shadowspect so they can better understand the process, make proper assessment, and conduct personalized interventions when appropriate.

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Section: Related Workmentioning

confidence: 99%

Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game

Gómez

Ruipérez‐Valiente

Andreo-Martínez

et al. 2021

Sensors

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show abstract

“…The widespread demand for educational data offers an educational researcher with a wide range of occasions to practice computational analysis of data to obtain valuable acquaintance to develop the education process. Educational computational analysis of data concentrates on gathering besides processing of student record and their education backgrounds, educating computational analysis of data deals with the survey of teaching environment strategy and the quality of coaching methods provided to students (Ndukwe et al, 2018).…”

Section: E-learningmentioning

confidence: 99%

Covid-19 and education system: impact of current pandemic on adaptive learning strategies in medical education system

Patil¹,

Naqvi²

2020

ijrps

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In the last few months, the globe has been overwhelmed by the pandemic coronavirus as the number of increasing documented cases, and death rates have continued to be reported. The disease is mild in most people usually aged people and those with the coexistence of multiple disorders alongside a primary disorder, pneumonia, acute respiratory distress syndrome (ARDS), and multiorgan dysfunction may develop. No special or specific drugs/medications are available. Neither vaccine has been developed, nor is any previous antiviral drug effective for treatment. Instead, medication is done based on the treatment of clinical features with appropriate drugs and the management of critical patients in intensive care units and isolation. The situation of India's COVID19 has improved decently than that of other countries already affected, thanks to precautionary measures. India, however, did not escape the pandemic. Coronavirus spread (COVID-19) has led most countries around the world to implement urgent lock-down strategies to reduce the blowout of the disease, results that stopped classroom teaching and educational institutions. Global response of health education to COVID-19 pandemic is different from each area, from closings of health institutions to distance learning tactics to compliance with area-specific actions, such as communal distancing to stop the blowout of disease. The unexpected transition from classroom teaching to online education is a challenge both for faculties and learners, and a great deal of preparation and effort is needed in a short time.

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“…The tasks in an EDM process that are responsible for analyzing data generated from interactions between students in learning environments can answer questions related to how to improve student learning [3]. Thus, it is possible to understand him and how he learns, in addition to other factors that influence learning.…”

Section: Introductionmentioning

confidence: 99%

Genetically Optimized Ensemble Classifiers for Multiclass Student Performance Prediction

2022

IJIES

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The knowledge obtained from data can be useful for the improvement of education systems, giving rise to a research space called Educational Data Mining (EDM). EDM covers the development of methods to explore information collected from educational environments, allowing to understand students more effectively and adequately, providing better educational benefits to them. Machine learning (ML) technologies are growing considerably in recent years. The field of data mining in education provides researchers and educators with metrics of success, failure, dropout, and more, allowing students to guess. The main reason for dropping out of school is not studying. Several researchers have proposed various educational data mining techniques to predict student performance and analyzed the techniques found in educational datasets. This paper proposes a student predictive model with the use of ensemble classifiers. Initially data is pre-processed and an analysis of the correlation between the entrance attributes was carried out to identify the existence of possible redundancies between them, resulting from a very high positive correlation. The filtered attribute is trained and tested with Boosting, Bagging and Random subspace classifiers. Further to improve the accuracy of predictive model genetic algorithm is applied on three classifiers. Genetic Algorithm is an approach used to find optimized solution to search problems and it intend to increase the probability of solving the problem. The process of optimization involves selection of the best option from the available set of options to achieve the desired goal. Selection is done such that the efficiency can be maximized and error can be minimized. An analysis of the correlation between the entrance attributes was carried out to identify the existence of possible redundancies between them, resulting from a very high positive correlation. There is significant improvement in classifier accuracy, when tested mathematic and Portuguese data i.e. 3 % and 11% respectively.

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Data Science Approach for Simulating Educational Data: Towards the Development of Teaching Outcome Model (TOM)

Cited by 12 publications

References 35 publications

Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game

Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game

Covid-19 and education system: impact of current pandemic on adaptive learning strategies in medical education system

Genetically Optimized Ensemble Classifiers for Multiclass Student Performance Prediction

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