Search results for "Educational data mining"
showing 4 items of 4 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…
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.
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…