Search results for "educational data"
showing 9 items of 9 documents
Blended Learning als Spielfeld für Learning Analytics und Educational Data Mining
2020
Der Einsatz digitaler Lernformate im Blended Learning bietet demnach Chancen in mindestens zwei Bereichen. Zum einen konnen digitale Lernformate direkt die Lernprozesse von Studierenden gunstig beeinflussen, ihre Leistungen verbessern und zudem positive Effekte auf vielen weiteren Ebenen wie der Motivation oder des Selbstkonzeptes bewirken. Zum anderen generieren digitale Lernformate eine Fulle von Daten in vielfaltiger Gestalt. Studierende erzeugen bei der Arbeit mit digitalen Werkzeugen Nutzungsdaten, wie Verweildauern und Aktivitatsprofile, sie produzieren Leistungsdaten aus digitalen Aufgaben, sie hinterlassen Textbeitrage in Foren und Chats. All diese Daten konnen genutzt werden, um mi…
Deep learning for knowledge tracing in learning analytics: An overview
2021
Learning Analytics (LA) is a recent research branch that refers to methods for measuring, collecting, analyzing, and reporting learners’ data, in order to better understand and optimize the processes and the environments. Knowledge Tracing (KT) deals with the modeling of the evolution, during the time, of the students’ learning process. Particularly its aim is to predict students’ outcomes in order to avoid failures and to support both students and teachers. Recently, KT has been tackled by exploiting Deep Learning (DL) models and generating a new, ongoing, research line that is known as Deep Knowledge Tracing (DKT). This was made possible by the digitalization process that has simplified t…
Forecast of Study Success in the STEM Disciplines Based Solely on Academic Records
2020
We present an approach to the forecast of the study success in selected STEM disciplines (computer science, mathematics, physics, and meteorology), solely based on the academic record of a student so far, without access to demographic or socioeconomic data. The purpose of the analysis is to improve student counseling, which may be essential for finishing a study program in one of the above mentioned fields. Technically, we show the successful use of propositionalization on relational data from educational data mining, based on standard aggregates and basic LSTM-trained aggregates.
Analysing Student Performance using Sparse Data of Core Bachelor Courses
2015
Curricula for Computer Science (CS) degrees are characterized by the strong occupational orientation of the discipline. In the BSc degree structure, with clearly separate CS core studies, the learning skills for these and other required courses may vary a lot, which is shown in students' overall performance. To analyze this situation, we apply nonstandard educational data mining techniques on a preprocessed log file of the passed courses. The joint variation in the course grades is studied through correlation analysis while intrinsic groups of students are created and analyzed using a robust clustering technique. Since not all students attended all courses, there is a nonstructured sparsity…
Does taking additional Maths classes in high school affect academic outcomes?
2023
Several studies in the mathematical education literature show the effect of students’ high school skills in maths on their success at higher levels of education and work. In particular, the importance of maths course taking in US high schools is highlighted to be important for college enrolment and completion. The choice of taking additional maths courses or, as in Italy, of choosing a high-school curriculum with more maths, is not random: it depends on several substantial factors such as gender and socio-economic status. This selection bias implies that the differences in the academic outcomes might be traceable not only to mathematics ability and knowledge. In this paper, the aim is to es…
The Influence of Student Abilities and High School on Student Growth: A Case Study of Chinese National College Entrance Exam
2019
Enabled by available educational data and data mining techniques, educational data analysis has become a hot topic. Current researches mainly focus on the prediction of problems and performance rather than revealing the underlying causal relationships. Based on a unique exam data, we extracted the abilities of examinee from HSEE (High School Entrance Exam) based on the knowledge of educational experts, then we measured student growth from middle school to high school in total score and subject scores. We studied the impact of high school ranking and student abilities of HSEE on student growth by multiple linear regression model, in which high school ranking is divided into 5 levels, Level 1…
Automatic knowledge discovery from sparse and large-scale educational data : case Finland
2017
The Finnish educational system has received a lot of attention during the 21st century. Especially, the outstanding results in the first three cycles of the Programme for International Student Assessment (PISA) have made Finland’s education system internationally famous, and its unique characteristics have been under active research by various, predominantly educational, scholars since then. However, despite the availability of real but often sparse big data sets that would allow more evidence-based decision making, existing research to date has mostly concentrated on using classical qualitative and (univariate) quantitative methods. This thesis discusses, in general terms, knowledge discove…
Does taking additional Maths classes improve university performance?
2022
Several recent studies in educational literature showed how students’ skills in maths affect their success at higher levels of education. The aim of this paper is to evaluate the effect of taking additional maths class at high school on first-year performance of Italian university students. However, university performance and the choice of the high-school depend on several factors that make this evaluation challenging. Using information coming from three different sources, we carry out a multilevel propensity score procedure to estimate the average treatment effect between the applied sciences track and the traditional scientific one. After balancing for school- and student-level covariates…
Weighted Clustering of Sparse Educational Data
2015
Clustering as an unsupervised technique is predominantly used in unweighted settings. In this paper, we present an efficient version of a robust clustering algorithm for sparse educational data that takes the weights, aligning a sample with the corresponding population, into account. The algorithm is utilized to divide the Finnish student population of PISA 2012 (the latest data from the Programme for International Student Assessment) into groups, according to their attitudes and perceptions towards mathematics, for which one third of the data is missing. Furthermore, necessary modifications of three cluster indices to reveal an appropriate number of groups are proposed and demonstrated. pe…