=Paper=
{{Paper
|id=Vol-2590/paper3
|storemode=property
|title=Personalized Impact on Students in MOOCs
|pdfUrl=https://ceur-ws.org/Vol-2590/paper3.pdf
|volume=Vol-2590
|authors=Liubov Lisitsyna,Svyatoslav Oreshin
|dblpUrl=https://dblp.org/rec/conf/micsecs/LisitsynaO19
}}
==Personalized Impact on Students in MOOCs==
https://ceur-ws.org/Vol-2590/paper3.pdf
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Personalized Impact on Students in MOOCs
Lubov Lisitsyna1, Oreshin Svyatoslav2
1 ITMO University, Kronvrkskiy pr. 49, Saint Petersburg, 197101, Russia
lisizina@mail.ifmo.ru
2 ITMO University, Kronvrkskiy pr. 49, Saint Petersburg, 197101, Russia
Aqice26@gmail.com
Abstract. Churn prediction is a common task for machine learning applications
in business. In this paper, we adapt this task to solve a massive open online
courses’ low efficiency problem which formulates as a very low ratio of
students who successfully finish a course. The presented approach is described
and tested using course "Methods and algorithms of the graph theory" held on
national platform of online education in Russia. This paper includes all the
necessary steps in building an intelligent system to predict students who are
active during the course, but not likely to finish it. The first part consists of
constructing the right sample for prediction, EDA and choosing the most
appropriate week of the course to make predictions on. The second part is about
choosing the right metric and building models. Also, approach with using
ensembles like stacking is proposed to increase the accuracy of predictions. As
a result, we overview the outcome of using this approach on real students and
discuss the results and further improvements. Our personalized impact showed
that the majority of students (70%) perceive such an impact positively and it
helps them to pass the hardest tasks considered online course.
Keywords: Machine learning · Data science · Massive Open Online Course ·
Educational analytics · Learning analytics
1 Introduction
The main problem of using Massive Open Online Courses (MOOC) is their low
performance (no more than 5%), which is estimated as the proportion of successfully
completing the course to the total number of students registered at the start of this
course. The low performance analysis of MOOC [1] revealed a number of reasons
related to the poor readiness of listeners for e-learning, with low motivation to
achieve higher learning outcomes. Different proposes [1–6] were published and
reviewed, monitoring situational awareness of the student when working with
electronic forms before learning.
In this paper, we adapt a churn prediction task to predict students’ churn in
MOOCs. Classical churn prediction task is about building a model which finds a list
of clients who are likely to break their contract. This task is also applied to predict
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution
4.0 International (CC BY 4.0)
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students’ churn in classical higher education [7]. If adapt this task to MOOCs, the
formulation is different. The main different is about the information that we have
about a student (client). In MOOCs, we have very limited data about a certain student,
thus we need to collect enough data to make predictions in the process of student’s
learning. We may also use meta information about student’s previous performance on
courses and his open information in profile if possible.
Firstly, we need to select the correct time period in the course, so we can use data
of the whole students’ activity in selected course before this point. There may be
several such points. Secondary, we need to choose the correct target for predictions.
We propose to use a binary target of a fact of successfully passing the final exam on
course. This target may differ because of different structures and difficulties on a
certain course. Further, we overview the whole process to solve selected problem
using machine learning approach and demonstrating its effectiveness on online course
"Methods and algorithms of graph theory" by IFMO University. Finally, we discuss
the experimental results and further goals of using proposed approach on real
students.
This article proposes a user-based approach to sampling statistical data recorded by
the e-learning system during the course to predict the performance of an online
course. This approach aims to increasing personalized monitoring of the e-learning
process and adaptation of a platform for a certain student.
1 Data mining and Exploratory data analysis
This section presents the process of collecting data from logs of activity in the
platform, aggregating this data by every student and choosing the correct time period
in the course to build predictions on. The process of data mining in MOOCs strongly
depends on the structure of a course. That is why we should start with analyzing a
certain course material to find the best approach and strategy to make predictions on.
1.1 Course material overview
This research used statistical data accumulated on the national open education
platform of the Russian Federation during the online course "Methods and algorithms
of graph theory" (https://openedu.ru/course/ITMOUniversity/AGRAPH/) for the
period from 2016 to 2019. The experiment of personalized impact on students took
place in spring’s session in 2019. This online course [8-9] is conducted for 10 weeks
twice a year (at the beginning of the fall and spring semesters), contains 41 video
lectures with surveys and 11 interactive practical exercises. On the 10th week an
online exam is held. Table 1 presents practical exercises presented in the course. As
we can see, this course has many practical exercises and lasts 10 weeks of intensive
studying. We need to choose the correct time period to make predictions on. By the
time we make predictions, we should have enough information about student’s
performance and we still need to have enough time to impact on a student in such a
way to increase this student’s motivation thus, increase his or her chances to
successfully finish this course. In other words, selecting a correct time period the have
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a trade-off between timelessness of impact and having enough information about
student’s performance.
Table 1. Practical exercises of the course
Algorithm Typical graph problem Week number
Lee algorithm Search shortest route 2
Bellman-Ford algorithm Search route with minimal 2
weight
Roberts-Flores algorithm Search for Hamilton loops 3
Prim algorithm Search for minimum 4
spanning tree
Kruskal algorithm Search for minimum 4
spanning tree
Magu-Weismann algorithm Search for largest empty 5
subgraphs
Method based on Magu- Minimum vertex coloring of 6
Weisman algorithm graph
Greedy heuristic algorithm Minimum vertex coloring of 6
graph
Hungarian algorithm Search perfect matching in a 7
bipartite graph
Algorithm based on ISD method Detecting of isomorphism of 8
two graphs
Gamma-algorithm Graph planarization 9
To select the most appropriate time period to build predictions on, analysis of
practical exercises in the middle of the course was performed. Figure 1 presents the
distribution of an average time that a certain student needs to complete a practical
exercise. As we can see, Magu-Weismann algorithm is held on 5th week (middle
points of the course) and has a bimodal distribution, which can be an indicator that
this task is complicated for some number of students. Also, we can conclude that the
average time of solving a practical task is about 20 minutes.
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Figure 1. Average time (in minutes) taken to complete practical exercises
We have 10 weeks of course, so it is reasonable to take 5th week as a middle point of a
course as a time to make predictions. Also, we can see that the most complicated task
is held on 5th week. Completing this week strongly defines completing an overall
course. As a conclusion, we take 5th week as a time point of a course on which we
will make predictions about students’ performance on final exam. As was mentioned,
this time point may differ for a certain online course. Choosing this time point, we
recommend estimating the ratio of overall information that we can gain about a
certain student, complicatedness of problems before and after this point and fulness
about already learned material.
2 Task formalization and model fitting
In this section, we formulate churn prediction problem in MOOC in machine learning
terms and build a model for this binary classification task. After the model is tested
and compared, we propose an ensembling approach to increase overall models’
performance.
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2.1 Problem overview
We have a problem of binary classification of target that indicates a successful
passing the final exam of a course. Having a probability of successful passing, we
rank all the students by these probabilities. Then, we need to find out students that we
should apply additional impact on. These group of students should have be a group of
active students but who have a low chance to successfully finish a course. Thus, the
final probability should not be very low and very high. We suggest taking an expected
value of students who pass an exam we use it as a higher bound of probability of this
group. Then, we can take a various number of students depending on the resources
that we have to apply additional impact. If we have an automatized process of
applying additional impact, then we may take a big group. In our experiment we did a
personalized impact sending emails with analysis certain student’s problems and
giving him personalized advices depending on his or her case. It means that this type
of impact has a high cost of human’s time to analyze each student’s case. Also, we
can combine several types of impact with a different number of students and different
threshold of likelihood in passing final exam.
Evaluating classifiers for this task the ROC AUC metric [10] was chosen due to the
operation of the probabilities of the object belonging to the class with different
thresholds. Also, ROC AUC indicates the quality of ranking, which is the most
important subtask to make a correct choice of this group of students to make impact
on.
The formulation of the problem is a probabilistic binary classification with further
likelihood’s ranking.
2.2 Classifiers’ fitting and analyzing
To build a baseline for this classification problem, support vector machine [11],
logistic regression [12, 13], random forest [14] and gradient boosting on decision
trees (GBDT) [15, 16] were chosen and validated. To evaluate different classifiers,
nested cross-validation was used.
Table 2 present the results of cross-validation for these models of ROC-AUC value
and its std. As we can see from the table, GBDT has the best value of the chosen
metric. For GBDT algorithm, we used XGBoost and CatBoost implementations
which had comparable results of ROC AUC metric.
Table 2. Results of cross-validation for baseline models
Model ROC AUC
Logistic Regressor 0.8699
Support vector machine (rbf) 0.8763
Random Forest 0.9027
Gradient boosting on trees 0.9153
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But we have a hypothesis that we can improve out baseline due stacking [17, 18].
Figure 2 and Figure 3 presents the similarity plot between Logistic regression and
GBDT classifiers on different folds on cross-validation. From plots, we can conclude
that the predictions of these classifiers are very different in each certain case, but
these classifiers have a high ROC AUC score.
For this purpose, we choose one linear model and one tree-based model. We chose
logistic regression as a linear model for stacking because SVM has constant
probabilities with Radial basis function kernel (RBF) [19], which is not appropriate
for ROC-AUC and stacking because of need of additional probability calibration,
which can be bad in a general case of ensembling models. Support vector machine
with a linear kernel is not able to operate with probabilities as other linear models
because it does not apply any mapping into probability space. GBDT model was
chosen as a tree-based model for further improvement because of the highest ROC
AUC value.
Figure 2. The similarity plot between GBTS and Logistic regression on 4 th fold of cross-validation
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Figure 3. The similarity plot between GBTS and Logistic regression on 5 th fold of cross-validation
Stacking was applied using another logistic regression model to build new predictions
on predictions of the initial models. We chose the 3 rd session of a course as a
validation set for meta classifier in ensembling. The results of cross-validation of
Logistic Regression, GBDT and meta model in this ensemble is presented in Table 3.
We conclude that stacking increased the results of each classifier. We will use
stacking as a final model for this problem
Table 3. Results of cross-validation for Logistic regression, GBDT and ensembling of these
models
Split Model ROC-AUC score
Logistic Regression 0.9255
1 Gradient Boosting on decision trees 0.9546
Stacking 0.9767
Logistic Regression 0.8702
2 Gradient Boosting on decision trees 0.9302
Stacking 0.9688
Logistic Regression 0.9116
3 Gradient Boosting on decision trees 0.9780
Stacking 0.9742
Logistic Regression 0.7659
4 Gradient Boosting on decision trees 0.9117
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Stacking 0.8876
Logistic Regression 0.8651
5 Gradient Boosting on decision trees 0.8925
Stacking 0.9160
Logistic Regression 0.8612 ± 0.0531
Gradient Boosting on decision trees 0.9189 ± 0.0427
Stacking 0.9304 ± 0.0459
After we fitted final model, we analyzed the results of feature importance of each
model in an ensemble and calculated the feature importance for the final model. The
results of feature importance for Logistic regression and Gradient Boosting are
presented in Table 4 and Table 5 correspondingly. We took 5 the most important
features for each base model in ensemble and calculated scalar product of these
importance with the importance of meta model in stacking. As a result, we get the
results of feature importance for the final model which is presented in Table 6. The
most important features for Logistic regression are overall mean number of attempts
in interactive tasks and mean number of attempts on a 4 th week. The most important
features for GBDT are overall activity of a student during and activity of a student on
the 2nd week. We can conclude that basically Logistic regression classifier pays
attention on features with a statistic related with attempts on interactive tasks and
GBDT classifier pays more attention on overall activity statistics. All these features
were calculated before the 5th week of the course.
The most important features for final model are the composition of feature
importance from Table 4 and Table 5 with meta model’s coefficients. As we can
conclude, the most important feature for the final model is activity of a student on the
2nd week of the course. Features as an overall number of attempts on a course and
overall activity are also important.
Table 4. Top 5 the most important features for Logistic regression
Feature Importance
Mean number of attempts on interactive tasks 20.16%
Number of attempts of the 4th interactive task 11.94%
Mean grade score 11.89%
Number of attempts of the 1st interactive task 6.32%
Number of attempts of the 6th interactive task 4.61%
Table 5. Top 5 the most important features for Gradient Boosting
Feature Importance
Overall activity 18.38%
Activity on the 2nd week 10.43%
Number of attempts of the 2nd interactive task 5.46%
Number of attempts of the 6th interactive task 5.28%
Mean number of attempts on interactive tasks 5.17%
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Table 6. Top 5 the most important features for the final model
Feature Importance
Activity on the 2nd week 8.25%
Number of attempts of interactive tasks 7.87%
Overall activity 7.64%
Activity on the 1st week 5.89%
Mean number of attempts of interactive tasks 5.72%
In this section we discussed the formulation of the problem in machine learning
terms, fitted and compared different classifiers and analyzed its feature importance.
Finally, we applied ensembling strategy to increase overall performance of Logistic
regression and Gradient boosting classifiers and analyzed its feature importance.
Now, we can use fitted models to predict a boundary group of students to make a
personalized impact on each group. In the next chapter we demonstrate the results of
our experiment of making a personalized impact on students in Spring of 2019 session
of the course.
3 Results of the experiment
To select a correct threshold value, we take the percentage of students who passed the
exam in previous sessions (5.6%) multiplied by the number of students in the current
session. Table 7 presents the results of ranking students in the test set on their
likelihood to complete the course, starting with the highest probability. After applying
calculated threshold, we get a list of students who need to have an additional impact
to increase the effectiveness of their learning (Table 8). The last column in the tables
shows whether the participant has actually passed the exam (1 for yes, 0 for no). The
resulting tables show that the model correctly ranks the students of the course
according to their likelihood to pass the exam in general: there are only 2 of students
who actually passed the final exam in the table with ranked students below selected
threshold. Table 8 was used to create a group of students to make personalized impact
on. We took 10 students for our experiment and made a personalized impact on this
group of students.
Table 7. Students with highest probability of examination
Student Probability of examination Examinated
Student 11 0.8661 1
Student 12 0.8616 1
Student 13 0.8542 1
Student 14 0.8221 0
Student 15 0.8217 0
Student 16 0.8162 1
Student 17 0.8038 1
Student 18 0.7765 0
Student 19 0.7719 1
10
Student 110 0.7666 0
Table 8. Students below the threshold of examinations
Student Probability of examination Examinated
Student 21 0.4741 0
Student 22 0.4453 0
Student 23 0.4352 0
Student 24 0.4232 0
Student 25 0.4216 1
Student 26 0.4163 0
Student 27 0.4015 0
Student 28 0.3793 1
Student 29 0.3771 0
Student 210 0.3348 1
After the boundary group of students was revealed, we made a personalized impact on
that group. Our impact was sending email with personalized analysis of each student
performance with hints and advices. This kind of impact is useful in this particular
course because of the following reasons:
1) Student feels personalized treatment from the author of the course, and it
increases his or her involvement in learning process.
2) Student can see his or her mistakes in solving practical exercises, so it increases
his or her chances on further success in solving his or her problematic task.
3) Student gets personalized advices about particular topics that he or she should
pay more attention at.
4) Student can give his or her feedback about the course and ask questions about
incomprehensible topics.
The methodology of making personalized impact may be different in each case. In
MOOCs there are many parts of quizzes and practical exercises that we can add
personalized feedback on if we correctly indicate the boundary group of students. It is
important to give personalized feedback that is suitable for each particular student
because in some cases it can decrease student’s motivation because of apparent
simplicity. In other case, it can be not relevant to waste additional resource to make
feedback on students who already gave up or didn’t plan to successfully finish a
course.
The results of using proposed personalized impact are presented in Figure 4. 70%
of students from the target group positively responded to our mailings. 50% of
students accepted our suggestions of helping, and 30% of students successfully passed
the hardest task of the course. Such results indicate that students perceive such an
impact positively and it helps students to pass the tasks that they feel problems with.
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Figure 4. The results of personalized impact on students in Spring’s 2019 session
5 Conclusion
In this research we indicated a problem of MOOCs’ low efficiency and proposed an
approach to solve this problem using machine learning algorithms. We used online
course “Methods and algorithms of graph theory” to show all the steps in building
such a solution. We compared different classifiers and proposed an approach to
increase overall quality using stacking. According to the results, the most significant
features were obtained for assessing the fact that the exam was passed by the students.
As a result of model’s prediction, a list of participants was received according to their
probabilities of passing a final exam. This approach can be used as to increase the
efficiency of learning of separated students and to improve course materials in
general. Also, this problem can be interpreted as a churn prediction problem. After the
final list of students is received, it can be used to make the course more personal for
this group of students. As an example, we suggest giving some hints and additional
bonuses for the student if he or she will continue learning or increasing deadlines.
Results of the final model analysis can be used for exploring aspects of the course that
are important for a separate group of students. Thus, this article proposes a general
approach for assessing and identifying MOOCs’ students during the course, on which
additional impact is required to improve the performance of e-learning using MOOC.
Using this approach in MOOCs can increase effectiveness of online courses and make
e-learning more self-organized and adaptive for a separate student. Finally, we present
the results of our experiment of using personalized impact on found boundary group
of students. This personalized impact was positively perceived by students and helped
them to solve the hardest practical problem of a course.
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