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Highlight some LA/EDM studies in the introduction

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Charlotte Van Petegem 2024-02-15 11:52:05 +01:00
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@ -294,16 +294,30 @@ Learning analytics and educational data mining stand at the intersection of comp
They are made possible by the increased availability of data about students who are learning, due to the increasing move of education to digital platforms\nbsp{}[cite:@romeroDataMiningCourse2008].
They can also serve different actors in the educational landscape: they can help learners directly, help teachers to evaluate their own teaching, allow educational institutions to guide their decisions, or even allow governments to take on data-driven policies\nbsp{}[cite:@fergusonLearningAnalyticsDrivers2012].
Learning analytics and educational data mining are overlapping fields, but in general, learning analytics is seen as focusing on the educational challenge, while educational data mining is more focused on the technical challenge\nbsp{}[cite:@fergusonLearningAnalyticsDrivers2012].[fn::
The analytics focusing on governments/educational instutions is also called academic analytics.
The analytics focusing on governments or educational institutions is called academic analytics.
]
[cite/t:@chattiReferenceModelLearning2012] defined a reference model for learning analytics based on four dimensions:
#+ATTR_LATEX: :environment enumerate*
#+ATTR_LATEX: :options [label={\emph{\roman*)}}, itemjoin={{ }}, itemjoin*={{ }}]
- What data is gathered and used?
- Who is targeted by the analsis?
- Who is targeted by the analysis?
- Why is the data analysed?
- How is the data analysed?
This gives an idea to researchers what to focus on when conceptualizing, executing, and publishing their research.
An example of an Educational Data Mining study is\nbsp{}[cite/t:@daudPredictingStudentPerformance2017], where the students' background (including family income, family expenditures, gender, martial status, ...) is used to predict the student's learning outcome at the end of the semester.
Evaluating this study using the reference model by\nbsp{}[cite:@chattiReferenceModelLearning2012], we can see that the data used is very personal and hard to collect.
As mentioned in the study, the primary target audience of the study are policymakers.
The data is analysed to evaluate the influence of a student's background on their performance.
The data is analysed using a number of machine learning techniques.
Another example of the research in this field is a study by\nbsp{}[cite/t:@akcapinarUsingLearningAnalytics2019].
They focus on the concept of an early warning system, where student performance can be predicted early and appropriate action could be undertaken.
Their study uses data from a blended learning environment, where students can see the lesson's resources, participate in discussions, and write down their own thoughts about the lessons.
Here, the primary target audience is the student.
Although the related actions are not yet in scope of the study, the primary goal is to develop such an early warning system.
Again, a number of machine learning techniques are compared, to determine which one gives the best results.
** Structure of this dissertation
:PROPERTIES:
@ -2396,8 +2410,8 @@ The process of giving feedback on a programming assignment in Dodona is very sim
However, there exists a crucial distinction between traditional code reviews and those in an educational context: instructors often provide feedback on numerous solutions to the same assignment.
Given that students frequently commit similar errors, it logically follows that instructors repeatedly deliver the same feedback across multiple student submissions.
In response to this repetitive nature of feedback, Dodona has implemented a feature enabling instructors to save and later retrieve specific annotations.
This functionality facilitates the reuse of feedback by allowing teachers to search for previously saved annotations.
In response to this repetitive nature of feedback, Dodona has implemented a feature enabling instructors to save and later retrieve specific messages.
This functionality facilitates the reuse of feedback by allowing teachers to search for previously saved messages.
By using this functionality, we have generated data that we can use in this study: code submissions, where those submissions have been annotated on specific lines with messages that are shared over those submissions.
Note that there are two concepts here, whose distinction is important.
@ -2573,7 +2587,7 @@ subtree_matches(subtree, pattern):
Checking whether a pattern matches a subtree is an operation that needs to happen a lot of times.
For some messages, there are many patterns, and all patterns of all messages are checked.
One important optimization we added was therefore to only execute the algorithm in Listing\nbsp{}[[lst:feedbackmatchingpseudocode]] if the set of labels in the pattern is a subset of the labels in the pattern.
One important optimization we added was therefore to only execute the algorithm in Listing\nbsp{}[[lst:feedbackmatchingpseudocode]] if the set of labels in the pattern is a subset of the labels in the subtree.
**** Ranking the messages
:PROPERTIES: