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Information Engineering with E-Learning Datasets
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Informationsutveckling inom E-Lärande (Swedish)
Abstract [en]

The rapid growth of the E-learning industry necessitates a streamlined process for identifying actionable information in the user databases maintained by E-learning companies. This paper applies several traditional mathematical and some machine learning techniques to one such dataset with the goal of identifying patterns in user proficiency that are not readily apparent from simply viewing the data. We also analyze the applicability of such methods to the dataset in question and datasets like it. We find that many of the methods can reveal useful insights into the dataset, even if some methods are limited by the database structure and even when the database has fundamental limits to the fraction of variance that can be explained. We also find that such methods are much more applicable when dataset records have clear times and student grades have fine resolution. We also suggest several changes to the way data is gathered and recorded in order to make mass-application of machine learning techniques feasible to more datasets.

Abstract [sv]

Snabb utveckling inom E-lärandesindustrin gör snabba och generaliserbara metoder för informationsutveckling med E-lärandesdatabaser nödvändiga. Detta arbete tillämpar olika traditionella maskininlärnings- och matematiska metoder i en sådan databas för att identifiera mönster i användarfärdighet som inte lätt kan upptäckas genom att läsa igenom databasen. Detta arbete analyserar även metodernas generaliserbarhet, särskilt var dem kan användas, deras nackdelar, och vad databaserna behöver uppfylla för att lätt kunna analyseras med metoderna. Vi finner att många av metoderna kan upplysa om strukturer och mönster i databasen även om metoderna begränsas i effektivitet och gene- raliserbarhet. Metoderna är också enklare att tillämpa när databasens artiklar associeras med tydliga tidpunkter och studenternas betyg har hög upplösning. Vi föreslår ändringar för datainsamlingstekniken som kan förenkla paralleli- serbara storskalig tillämpningar av maskininlärningsmetoder på många databaser samtidigt.

Place, publisher, year, edition, pages
2019. , p. 66
Series
TRITA-EECS-EX ; 2019:668
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-265008OAI: oai:DiVA.org:kth-265008DiVA, id: diva2:1376897
External cooperation
Sana Labs AB
Supervisors
Examiners
Available from: 2020-01-20 Created: 2019-12-10 Last updated: 2020-01-20Bibliographically approved

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CiteExportLink to record
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