=Paper=
{{Paper
|id=Vol-3037/paper4
|storemode=property
|title=Predictive Model for Assigning Exercises to Students in Spreadsheet Functions Using Artificial Neural Networks
|pdfUrl=https://ceur-ws.org/Vol-3037/paper4.pdf
|volume=Vol-3037
|authors=Edwar Saire-Peralta
}}
==Predictive Model for Assigning Exercises to Students in Spreadsheet Functions Using Artificial Neural Networks==
https://ceur-ws.org/Vol-3037/paper4.pdf
Predictive Model for Assigning Exercises to Students in
Spreadsheet Functions Using Artificial Neural Networks
Edwar Saire-Peralta 1
1
Universidad Nacional de San Agustín de Arequipa, Av. Independencia s/n, Arequipa, Perú
Abstract
The objective of the article is the development of a model that allows to predict the exercises
that the student can solve, and on the other hand the exercises that the student cannot solve in
the course of ®Microsoft Excel basic level with the topics of functions. For the development
of the process, artificial neural networks have been used. The model is fed with data such as
sex, age, academic grade, parents' level of education, type of school, previous grades of the
topics that the student obtains while advancing in the course. The research approach is
quantitative, experimental, applied and the population was represented by 85 students. The
result shows that the model achieves 72% probability of prediction in the assignment of
exercises to students. These exercises could not be solved were provided with an aid for their
resolution.
Keywords 1
Artificial neural networks, Supervised learning, Data mining, Cross validation
1. Introduction
The teaching-learning process is integral, according to [1] points out that, if the conditions of the
students are always different, such as the rhythms, ways of learning and starting points of each student,
then, what is learned and what is evaluated cannot be standardized, but must be differentiated according
to the individual characterization. [2] Indicates that student’s process information according to their
capacity, motivation, environment and the guidance provided by the teacher in their learning. Learning
rhythms are linked to academic performance, which is determined by personal, family, social and
educational factors [3, 4]. A learning session in the classroom is represented by several moments, one
of them represents the practice, which mostly aims to have students solve exercises regarding the topic
developed. It has been observed that many students have doubts and certain fears when interacting with
new learning topics; they are students who find it difficult to adapt to the pace of progress and
understanding imposed by the majority of students and even by the teacher. This reality is measurable
through the results of the evaluations. [5] Propose that the teacher should work at a safe level of demand,
which does not cause discouragement and low grades. According to [6] indicates that it is a mistake to
use the same contents, rhythms and evaluation to students, this is a problem because it can cause
frustrations and influences the relationship with other students. The situation described is very common
in classrooms, and many researches have used predictions to find the most suitable ways to know the
student based on certain data about them and give help.
2. Related work
CISETC 2021: International Congress on Educational and Technology in Sciences, November 16-18, 2021, Chiclayo, Peru
EMAIL: esaire@unsa.edu.pe (A.1)
ORCID: https://orcid.org/0000-0002-9526-0205 (A.1)
© 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
Related and pre-research work is characterized by the use of one or more classification algorithms.
Some research uses as input data those traces or interactions that students have with virtual platforms,
others use as inputs those data that are collected through instruments and that are designed at the time.
The literature has been reviewed and the opportunity to make predictions with data that arise from the
teaching-learning process is observed. In Table 1 we can see a summary of works related to the research.
Table 1
Summary of previous Works
Authors Title Contribution Opportunities for
improvement
[7] ICT for education: A system was built that The system was
adaptive system based enables the initial fed with data
on automatic learning recommendations of collected from
mechanisms for the educational content virtual courses,
appropriation of appropriate to the both from
technologies in high individual characteristics students and
school students. of students, administered educators, data
according to their analytics and
performance and the automatic learning
characteristics of the to make
territory. predictions and
initial
recommendations,
is limited by the
classroom
courses, since we
do not have the
necessary data.
[8] Model to predict A predictive model of The data collected
academic performance academic performance and used to make
based on neural was proposed using data the predictions
networks and learning provided by a virtual are from the
analytics interaction system with virtual system of
students, using learning courses they have,
analytics through however, there is
artificial neural networks, still an
patterns were found that opportunity for
were determinant in the improvement if
academic performance of personal, social
students. and other data of
interest to the
research are
included.
[9] Predicting academic Shows a range of To obtain the
performance by applying predictions to classify classification, the
data mining techniques (pass, fail) prospective data of the
students enrolled in a students enrolled
course. in the General
Data mining techniques Statistics course at
were used and results UNALM were
were compared using used; however,
logistic regression, the factors or
decision trees, neural predictor variables
networks and Bayesian were selected
networks. A prediction based on the data
effectiveness of 70% was they already had,
achieved. without taking
into account,
according to
research, that
there are very
influential
variables in
academic
performance,
which was not
taken into account
in their research.
[10] Development of a Through the use of Data generated by
computerized evaluation artificial neural networks, the same project
system using neural an environment of have been used.
networks through R and attention to the needs of The results
Shiny each student was created obtained were
with the use of correct different levels of
materials through difficulty for the
exercises in their students in the
evaluation. This allows to exercises;
reduce the feeling of however, there is
dissatisfaction and to still room to
avoid in many cases the analyze other
abandonment of the determining
courses. factors and to take
into account the
levels reached by
students in the
previous topic,
since this process
is changing.
3. Problem
Students in educational centers are characterized by being unique, singular and belonging to
heterogeneous groups. In each learning session the teacher tries to improve his work with the students,
especially when developing the practical part, where the teacher usually leaves a set of exercises during
the class, which the whole group must solve in a certain time. [11] indicates that a school model where
teachers teach the same contents, with the same level of complexity and at the same speed, this school
is not attending to the differential needs of the students. It has been observed during the classes that
students, when solving the battery of exercises, need support, tutoring, help in some formulas, in their
application and syntax. The teacher is regularly confronted with two situations: first, when the student
asks the teacher for help or tutoring, time is always pressing, and second, many students need help, but
do not ask for it. Diversity refers to heterogeneous groups of students in the classroom. Students are
unique and different, through their learning styles, ways of thinking and speed of learning within their
limitations [2].
4. Proposal
To address the stated problem, a model has been implemented that allows predicting the assignment
of exercises to students on an individualized basis, providing textual help in the exercises that the
student cannot solve. In the proposal, a predictive model based on artificial neural networks will be
implemented. This algorithm has a set of interconnected elements, where its processing capacity is
stored in weight units, this thanks to the adaptation and learning of a set of patterns [12]. The proposed
Model has as input the personal and social factors and the academic performance qualifications of the
students, which will allow us to classify the students with data mining. With mining we can obtain
models that allow discovering patterns and trends regarding student information [13]. The outline of
the proposal is shown in Figure 1.
Data source : Inputs
Questionnary data Evaluation data
Construction of the model
Classification algorithms Artificial neural networks
Predictive model : Output
Exercises without help (can solve) Exercises with help (cannot solve)
Figure 1: Structure of the Model
4.1. Population
The students who participated in the research took the ®Microsoft Excel basic level course. The
topics that were developed in the course are mathematical operators, mathematical functions, and
statistical functions, among others. There is no filter to enroll students, anyone can take it. We work
with heterogeneous groups. In total we worked with 85 students as the population, which also represents
the sample.
4.2. Questionnaire data
An instrument based on the survey technique was constructed. For the elaboration of the
questionnaire instrument, the literature on those factors that influence students in the handling of
function subjects was reviewed, in addition to adding other factors contextualized to the problem being
addressed. Academic achievement, being multicausal, according to [14] can be grouped into social,
personal and institutional determinants. Table 2 shows the factors taken into account for the
questionnaire.
Table 2
Classification of attributes
Individual Academic Socioeconomic Institutional
Person with numerical Current occupation
skills How many hours a day
You were taught do you work
Age computer skills in Father's academic level
Sex school Mother's academic level Type of school
Marital status Experience with Excel Reason for studying
Academic degree Excel
Hours of study during
the day
The instrument was validated with a psychologist in Education and a professional in Educational
Sciences, the reliability of the instrument was calculated, applying cronbach's alpha, where the result
was 0.733, which represents a value of good in reliability. With the validity and reliability obtained, the
questionnaire was applied to 85 students who took the course.
4.3. Evaluation data
The data collection regarding evaluation represents data from 58 students. The evaluation grades is
an indicator that determines the academic performance of students, as stated by [15], where it indicates
that academic performance is the level of knowledge that a student has which is reflected in a numerical
value, where it measures the result of the teaching and learning process in which the student is the main
actor. For each of the 8 topics of the course, exercises were prepared. For each topic, 10 types of
exercises were designed. A total of 80 types of exercises were prepared.
5. Application of the Methodology
To develop the proposal, the KDD (Knowledge Discovery in Databases) data mining process was
followed, as described in [16]. The KDD process is a rare process that allows obtaining information
from the data, which is present in a hidden way, initially anonymous and very useful for users or
companies [17].
5.1. Integration and Collection Phase
The data sources were merged. The first data source was obtained by applying the questionnaire and
the second data source was formed by the evaluation data collection (it was recorded for each topic and
type of exercise whether the student could or could not solve the exercise). In the Figure 2, we can see
the evaluations recorded. The value of 1 indicates that a student was able to solve one type of exercise
and a value of 0 indicates that the student was not able to solve that type of exercise. As mentioned for
each topic, 10 types of exercises were designed with labels from letter A to J.
Figure 2: Recorded evaluations
5.2. Selection, cleaning and transformation phase
The selection phase involved the use of all the data from the questionnaire with the totality of the
records collected. The cleaning phase was applied to the assessments with students who did not have
assessment scores. Finally, in the transformation phase all the questionnaire data were replaced by
numerical codes in order to process the data. The only field calculated was grade with a value from 0
to 20. The two data sources were integrated and linked. Finally, the data were normalized, since there
were blunt values, for better processing quality.
5.3. Data mining phase
The data mining technique applied to the proposed project is classification. The collected data were
separated, where 92% were assigned for training and 8% for model validation. The Artificial Neural
Networks algorithm with supervised learning Backpropagation was used. This supervised learning
algorithm is based on the repetition of the adjustment of the synaptic weights in the network, with the
aim of minimizing the difference in error between the expected and observed results, achieving the most
optimal [18]. To obtain the predictive model we used the free tool based on artificial Neural Networks,
which is called "Simbrain". In Figure 3 shows the network topology for the first subject.
Figure 3: Topology of the first subject
Layer 1 represents the input layer (15 attributes of the questionnaire), layer 2 refers to the hidden
layer with 20 neurons and finally layer 3 refers to the output layer with 10 answers. To train the model
for the second topic (mathematical functions), the topology must now have 16 inputs, which represents
the 15 student questionnaire data and the grade obtained for the academic performance of the previous
topic (first topic) and so on will increase the inputs for the other topics. There are numerous researches
such as those of [19, 20, and 21] have found evidence that the previous performance of their academic
performance could condition future results. Table 3 shows results for the first 5 topics.
Table 3
Final topology of the proposed model
Topic Input layer Hidden layer Output layer
Topic 01 15 20 10
Topic 02 16 30 10
Topic 03 17 20 10
Topic 04 18 20 10
Topic 05 19 20 10
5.4. Evaluation and Interpretation
Cross-validation was performed with 8% of the records that were initially separated. The results are
show in Table 4 for the first item. Recall that the value 1 represents that the student can solve the
exercise and the value 0 represents that the student cannot solve the exercise. The prediction on the set
of 5 students had a reliability of 72%.
Table 4
Model prediction for students
Student Model result Expected result Output layer
1 1111111111 1111011100 70%
2 1111111111 1111011101 80%
3 1110000100 1010000111 70%
4 1011011110 1111111101 60%
5 1111011110 1011011111 80%
5.5. Dissemination and Use
At the end of the evaluation, teachers as well as students were satisfied with the results, since an
assertiveness of 72% was achieved. The strength of the model is to identify those types of exercises
where students show difficulty, and it is in this space where the student will be supported.
6. Application and testing
Based on the predictive model obtained, its effectiveness was tested by selecting experimental
groups (group of students new to the course), to which the predictive model was applied for the first
two topics. In addition, control groups were selected (groups of students new to the course) where the
first two topics were also developed, but applying the traditional model. A total of 3 experimental
groups and 3 control groups were used for testing. In Table 5 we can see the averages obtained by the
experimental and control groups, where the average increase was from 13.4 to 17.2.
Table 5
Results of the groups
Group N° N°. topic Experimental Control
Group 01 Topic 01 16.3 13.8
Topic 02 17.4 12.0
Group 02 Topic 01 18.3 15.4
Topic 02 16.8 12.0
Group 03 Topic 01 17.9 15.2
Average 17.2 13.4
To give more reliability support to the obtained predictive model, the averages of the current results
of the predictive model were compared with the averages of students from previous years and months
(historical data of averages of 3 months of the previous year) and it was seen that the proposed model
also improves the averages from 13.3 to 16.4.
7. Discussion and conclusions
It is reflected and indicated that, in order to obtain efficient predictive models, it is necessary to work
not only with a greater amount of data, but also that these data must be of quality, must be selected by
experts in this discipline, data that other researches support. Many times institutions already have data
in their virtual systems [22, 23], but we must also measure the quantity of these data against the quality.
It has been shown that the proposed model can be successfully used to predict the types of exercises
that a student can solve and the types of exercises where he/she shows difficulties. The model was
exposed to a cross-validation, which had a prediction close to 72% with respect to the expected results.
An increase in their average from 13.49 to 17.29 in their evaluations was observed. This research not
only validated the model with 8% of the students, but also tested the model with new groups of students
in the institution. This model can be improved by working with more students and more that are related
to academic performance, since the learning would be more solid, in addition to adding the
characteristic that the exercises should be assigned gradually, i.e., classify the exercises by levels such
as basic, intermediate and advanced.
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