14,729 research outputs found
MOOCs Meet Measurement Theory: A Topic-Modelling Approach
This paper adapts topic models to the psychometric testing of MOOC students
based on their online forum postings. Measurement theory from education and
psychology provides statistical models for quantifying a person's attainment of
intangible attributes such as attitudes, abilities or intelligence. Such models
infer latent skill levels by relating them to individuals' observed responses
on a series of items such as quiz questions. The set of items can be used to
measure a latent skill if individuals' responses on them conform to a Guttman
scale. Such well-scaled items differentiate between individuals and inferred
levels span the entire range from most basic to the advanced. In practice,
education researchers manually devise items (quiz questions) while optimising
well-scaled conformance. Due to the costly nature and expert requirements of
this process, psychometric testing has found limited use in everyday teaching.
We aim to develop usable measurement models for highly-instrumented MOOC
delivery platforms, by using participation in automatically-extracted online
forum topics as items. The challenge is to formalise the Guttman scale
educational constraint and incorporate it into topic models. To favour topics
that automatically conform to a Guttman scale, we introduce a novel
regularisation into non-negative matrix factorisation-based topic modelling. We
demonstrate the suitability of our approach with both quantitative experiments
on three Coursera MOOCs, and with a qualitative survey of topic
interpretability on two MOOCs by domain expert interviews.Comment: 12 pages, 9 figures; accepted into AAAI'201
Effects of Automated Interventions in Programming Assignments: Evidence from a Field Experiment
A typical problem in MOOCs is the missing opportunity for course conductors
to individually support students in overcoming their problems and
misconceptions. This paper presents the results of automatically intervening on
struggling students during programming exercises and offering peer feedback and
tailored bonus exercises. To improve learning success, we do not want to
abolish instructionally desired trial and error but reduce extensive struggle
and demotivation. Therefore, we developed adaptive automatic just-in-time
interventions to encourage students to ask for help if they require
considerably more than average working time to solve an exercise. Additionally,
we offered students bonus exercises tailored for their individual weaknesses.
The approach was evaluated within a live course with over 5,000 active students
via a survey and metrics gathered alongside. Results show that we can increase
the call outs for help by up to 66% and lower the dwelling time until issuing
action. Learnings from the experiments can further be used to pinpoint course
material to be improved and tailor content to be audience specific.Comment: 10 page
A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs
Massive Open Online Course (MOOC) systems have become
prevalent in recent years and draw more attention, a.o., due to the coronavirus
pandemic’s impact. However, there is a well-known higher chance
of dropout from MOOCs than from conventional off-line courses. Researchers
have implemented extensive methods to explore the reasons
behind learner attrition or lack of interest to apply timely interventions.
The recent success of neural networks has revolutionised extensive Learning
Analytics (LA) tasks. More recently, the associated deep learning
techniques are increasingly deployed to address the dropout prediction
problem. This survey gives a timely and succinct overview of deep learning
techniques for MOOCs’ learning analytics. We mainly analyse the
trends of feature processing and the model design in dropout prediction,
respectively. Moreover, the recent incremental improvements over
existing deep learning techniques and the commonly used public data
sets have been presented. Finally, the paper proposes three future research
directions in the field: knowledge graphs with learning analytics,
comprehensive social network analysis, composite behavioural analysis
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