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Student Sentiment Analysis Using Gamification
for Education Context
Lamiaa Mostafa
(&)
Business Information System Department, Arab Academy for Science
and Technology and Maritime Transport, Alexandria, Egypt
Lamiaa.mostafa31@aast.edu, Lamiaa.mostafa31@gmail.com
Abstract. Internet users are expressing their sentiments (opinions) online using
blogs and social media. Sentiment Analysis is a new technology that is used to
improve the quality of the institutions including Higher education institution
(HEI). Egypt educational institutions face a difficulties based on student moti-
vation and learning engagement. Gamification provides a great help for edu-
cational institutions to motivate student and increase their learning ability
however it depends on the teacher skills to use the Gamification tools. The paper
reviews work in sentiment analysis related to education field, Gamification in
learning. The paper will propose a Sentiment Analysis Classifier that will
analyze the sentiments of students while using Gamification tools in an edu-
cational course.
Keywords: Sentiment Analysis Gamification WordNet Learning
Slang Arabic
1 Introduction
Education systems depend on four key elements which are teacher, the student, the
university, and the curriculum. Student motivation affects the student performance in
the educational institution. Gamification is defined as “the use of game design elements
in non-game contexts [1]. Gamification can be used to solve actual problems in dif-
ferent fields.
Internet helped people to express their thoughts and feelings [2]. Blog post and
online forums allow users to write their reviews. The people are connecting with each
other with the help of the internet through the blog post, online conversation forums,
and many more online user-generated reviews which is also called Sentiments also
known as opinion mining [3] can be used in different ways: used in decision making
process of consumers, sentiments provide efficient low cost feedback channel, improve
the quality of companies products and services which will also enhance their repetition
[4]. Authors in [2] proposed a tool which decides the quality of text based on scientific
papers annotations.
The rest of this paper is organized as follows: Sentiment Analysis, Gamification in
the second and third Section, sentiment analysis classifier will be described in Sect. 4;
results will be analyzed in Sect. 5; Sect. 6include the paper conclusion and future
work.
©Springer Nature Switzerland AG 2020
A. E. Hassanien et al. (Eds.): AISI 2019, AISC 1058, pp. 329–339, 2020.
https://doi.org/10.1007/978-3-030-31129-2_30
2 Sentiment Analysis
Sentiments and opinions can be recognized in different ways. Overall sentiment which
is sentiment as a whole piece of text can be analyzed using classification tools. Clas-
sifiers divide sentiments into positive or negative sentiment [2]. Different researchers
classify overall sentiment analysis [5].
Another type of sentiment classification is Aspect-based sentiment analysis [6,7].
Aspect sentiment include two major tasks, the first is detect hidden semantic aspect
from given texts, the second is identify fine-grained sentiments [2].
Data has two perspectives: objective and subjective. Objective based on emotions
and personal feelings and subjective is based on facts. Sentiment analysis aim is to
analyze subjective perspective of data [8]. Text mining techniques used to analyze
sentiments of students [9]. Sentiment analysis recognize student emotions [10].
Researchers are working on sentiment analysis in different patterns: Twitter sentiment
analysis [11] and cross lingual portability [12] and others. The following subsections
will discuss Sentiment processing steps and previous research in sentiment analysis
used in learning.
2.1 Sentiment Processing
Authors in [13] divide the sentiment process into 4 stages: data acquisition, data
preparation, review analysis, and sentiment classification. Sentiment process consists of
many steps such as stop word removal in which irrelevant terms are removed such as
“the”or “and”)[14]. Remove html tags; remove numbering and punctuation conver-
sion to lower case and stemming words (removing suffixes and prefixes to identify the
stem [15]. Term-frequency-inverse document frequency (TF-IDF) should be used to
count the frequency of keywords on the document [14]. Clustering algorithms should
be used to define correlation between the terms and the topic of the document.
Classify sentiments depends on two techniques: machine learning divided into
supervised and unsupervised or lexicon-based divided into dictionary based and corpus
based [16]. Machine learning uses traditional mining algorithms such as Naive Bayes
(NB), Support Vector Machines (SVM) and Neural Networks (NN). Naïve Bayes is the
simplest and most used classifier [17], Support Vector Machine apply learning models
to analyze data. Neural Networks requires a large corpus with three classes: positive,
negative and neutral opinions to learn and classify new sentiment into one of the three
classes [18].
Lexical based approach depends on dictionaries of terms such as WordNet. The
lexicon-based approach classifies a text according to the positive, negative and neutral
words and it does need training phase. Lexicon-based approach divided into dictionary-
based and corpus-based approach. Dictionary-based approach uses synonyms, anto-
nyms, and hierarchies found in lexical databases, Corpus-based approach find word
patterns occurrence to determine the polarity of a text [13]. After understanding the
processing of sentiment analysis, the paper will explore some of the researches that
implement the sentiment analysis method in leaning field.
330 L. Mostafa
2.2 Sentiment in Learning
Opinions written by online users have been studied using SA [19]. Opinions text
should be cleaned since it is full of spelling mistakes and implicit meaning. Online text
processing is a very important process before extracting sentiments [20]. Educational
Data Mining (EDM) task is to extract knowledge to enhance students’learning pro-
cesses [21]. Li [22] proposed a sentiment-enhanced learning model for the online
language learning, the experimental results show that sentiment learning is effective
tool online language learning. Permana et al. [23] proposed a sentiment model that
evaluates student satisfaction in learning using NB in classification which resulted in
enhanced of 16.49% over the existing system.
3 Gamification
The previous section discussed using sentiment analysis in learning, the aim of the
paper is to understand student sentiments on using Gamification in learning, and this
section will discuss Gamification concept and previous researchers that uses Gamifi-
cation in different sectors. Game design techniques help motivate people to finish the
required duties [24]. Games can be used in different services and increase the
involvement of people in non-game services [25]. The Gamification has 2 main ben-
efits: context role in game and the user qualities [26,27]. Gamification can be used in
different contexts tourism [28] and job recommendation in which a framework pro-
posed by [29] to understand the level of student in each working track and enhance his
learning path through Gamification tools. When person chooses to play a game over
responsibility, this is due to pressure and reduce stress [30]. Using Gamification in
higher learning assignment enhance the academic progress while reducing stress,
providing learning milestones and improving personal satisfaction [31]. It is important
to understand the previous researchers that use Gamification in learning so the next
subsection will discuss the previous work in Gamification.
Educational games “pretend to be games, as the fun factor is an additive, and not
the goal of those creations, however it is a specific learning outcome goal”[32].
Gamification in education aims to engage and motivate students [33], Gamification
develops the student’s skills such as collaboration, self-regulation and creativity. There
are many Gamification tools such as ClassDojo, Classcraft, Moodle plugin, Open
Badges [32] Games development has 2 main types: Game based learning and serious
games, game-based learning is using games to support learning however specific games
are games developed for the aim of teaching [1].
Different researchers use Gamification techniques in learning [34–36]. Authors in
[34] implemented a Gamification tool (Funprog) for teaching Programming funda-
mentals for 80 students of first-year students from University of Ecuador. The result of
the survey shows an acceptance level from the student and the teacher. Authors in [1]
provides a strategy to use e-learning platform and Gamification techniques in infor-
mation system courses.
Five interactivity story-based games where added by Arizona State University to
scientific curriculum to enhance student engagement [37]. Authors in [36] tested
Student Sentiment Analysis Using Gamification 331
Gamification on gender, age, and type of institution. They concluded that no differ-
ences in use of gamification by age, gender or type of institution. [35] applied Gam-
ification on software teaching in a specific class and the results had shown that
Gamification motivate students and enhance the learning tools used by the teacher. [38]
Focuses on the social gamification of e-learning and learner performance.
Authors in [25] proposed a Gamification model to be applied in Human Resource
Management course for Masters in Engineering. Authors in [39] designed a Gamifi-
cation tool that include levels based on storyline to enhance the teaching productivity
and learning path of the student concept for teaching in universities, they presented
Gamification for EPUB using Linked Data (GEL) is a framework combines Gamifi-
cation concepts and digital textbook they proposed Gamification Ontology (GO) that
share knowledge of gamified books between applications.
Briers in [40] applied gamified PMBOK for project management course targeting
junior project managers in Europe. PMBOK involves testing the following skills:
evaluating the reactions, cognitive learning, and the behavior changes.
Sobocinski in [32] proposed properties of Gamification tool used in education as
follows: narrative, points-badges, no grades, missions, small tasks, skills-badges,
individual and team tasks, boss fight, window of opportunity, optionality, schedule,
realoutcome. Narrative is to explain the course components, points and badges moti-
vate the students to reach different levels, grades are associated with difficulty so it is
replaced by points and badges, mission which is tasks enhance the student ability in
learning, small missions represent milestones, skills- badges reflect the development of
student level, team tasks create collaboration scenario between the student and his
colleagues, boss fight remove the fear of the final exams associated works in the
thinking of the students that is replaced by badges and points, window of opportunity
in which milestones and tests can be retaken, optionality of attendance gives the
students the freedom to stay home and learn, schedule flexibility enhance the student
ability to strength his skills, real outcomes represented in social experiment outside
class which give the student ability to present his progress to friends and family [32].
Learning motivations affect the student performance in class [41,42]. Authors in
[41] use sentiment analysis to improve the learning process in an e-learning environ-
ment. Sentiment analysis used to analyze the opinions of the students for better
understanding their opinion. After exploring the literature review in Sentiment Analysis
and Gamification, the Sentiment Analysis Classifier is designed and implemented.
4 Sentiment Analysis Classifier (SAR)
Sentiment Analysis classifier is used to analyze the students’opinion in using Gami-
fication tool in learning, sentiment analysis classifier passes by different stages
including text processing, feature selection and classification. The section is divided
into 4 subsections including Text Processing, Features selection, Machine Learning
Classifiers and Sentiment Dataset. Figure 1represents the Sentiment Analysis Classi-
fier steps.
332 L. Mostafa
4.1 Text Processing
The phase of text processing passes by two steps: the first step is the preprocessing step
and the second, is the feature selection or the representation of a document. The steps
are executed sequentially in which the output of the preprocessing step is the input of
the feature selection step [43].
Preprocessing is the step before the text is fed into a classifier. Preprocessing
includes many steps. Classifier preprocessing steps include parsing, removing stop
word and stemming [43]. Since the text is written in Arabic Chat Alphabet (ACA) also
known as (Slang Arabic),”is a writing system for Arabic in which English letters are
written instead of Arabic ones”[20], for example: “gama3aha feh haad ye3rf el
gamification deh eh”, the translation is “people, anyone knows about gamification?”.
ACA dictionary was created to be used in the Sentiment analysisClassifier. Student
Sentiment is reviewed by two experts to confirm the accuracy of the ACA dictionary.
Documents are preprocessed before classification. Preprocessing include stop word
removal and stemming. One of the stemmers that usually used in classification is Porter
Stemmer [14]. Parsing process converts the HTML file into stream of terms and remove
the HTML tags, stop word removal is the process of removing words that does not
affect the meaning of document sentences e.g. (and, a, or, the, on) and stemming is the
process of reducing a word to its stem or root form. Thus, stems represent the key terms
of a query or document rather than by the original words, e.g. “computation”might be
stemmed to “compute”[43].
Fig. 1. Sentiment analysis classifier steps.
Student Sentiment Analysis Using Gamification 333
Word Net
1
is a large lexical database of English. George A. Miller is the main
director. WordNet works as follows: it groups networks called sunsets, these sunsets
include nouns, verbs, adjectives and adverbs, and each sunsets aim is to express a
distinct concept. Synsets include conceptual-semantic and lexical relations. WordNet
can be downloaded freely. WorldNet’s structure which is the form of networks, makes
it a useful tool for text mining techniques as classification and natural language pro-
cessing. Different members develop WordNet as George A. Miller, Christiane Fell-
baum, Randee Tengi, and Helen Langone. WordNet is maintained at the Cognitive
Science Laboratory of Princeton University under the direction of psychology pro-
fessor George A. Miller. WordNet’s goal is to develop a system that can acquire
knowledge extracted from human thinking’s. One of these examples is the ability of a
human being in classifying different items into groups.
4.2 Classification Features
Any Webpage or document is represented using a combination of features. The basic
idea of feature selection algorithm is “Searching through all possible combination of
features in the data to find which subset of features work best for prediction. The
selection is done by reducing the number of features of the feature vectors, keeping the
most meaningful discriminating ones, and removing the irrelevant or redundant ones”
[45]. For the extraction of these features there are many known methods, including
Document Frequency (DF), Information Gain (IG) [43], Mutual Information
(MI) which is biased to rare terms opposite to IG [44]. Sentiment Analysis Classifier
uses DF technique and features are expanded using synonyms feature extracted from
WordNet.
4.3 Machine Learning Classifiers
After defining the features, sentiments can be classified. Statistical methods and
Machine Learning classifiers are usually used in sentiment classification including
Multivariate Regression Models, Bayes Probabilistic Approaches, Decision Trees,
Neural Networks, Support Vector Machine (SVM) [46], Symbolic Rule Learning,
Concept Vector Space Model (CVSM) [46], and Naïve Bayes (NB) is the most used
classifier in sentiment analysis [19]. Sentiment analysis classifier will use SVM and
NB.Knime
2
is used in preprocessing and keyword extraction and classification process.
4.4 Sentiment Dataset
Sentiments were collected from 1000 students in Arab Academy for Science and Tech-
nology and Maritime Transport University (AAST) University in a business course.
700 students agreed to examine Gamification in learning while 300 students were not
interested. Before the process of using machine learning classifier, two datasets must be
1
http://wordnet.princeton.edu.
2
http://www.knime.com.
334 L. Mostafa
defined: the first dataset is agree sentiment (700) and the second dataset is disagree
sentiment (300). ACA dictionary was created and mapped to every word in sentiments.
Two professors in College of linguistics in the AAST revised the dictionary to ensure the
accuracy level. After mapping the words of ACA to equivalent English words, the text
processed by the regular text processing steps including stop word removal, stemming,
WordNet is used for the enriching the keywords selected that represent agree and disagree
set. Synonyms relationship was selected such as “agree = good idea = agree = happy”,
all there words represent an agree sentiment. Two machine learning classifiers were used:
SVM and NB, both are trained and tested in the 1000 sentiments with the percent 80% for
training and 20% for testing (Lewis and Catlett 1994).
After collecting the sentiments of the students, a written exam was executed on the
two groups: agree and disagree, to test the performance of the student when using
gamification and without using gamification and to confirm whether using gamification
enhance the learning curve of the students in the learning process or not. The following
section will discuss the results from using the proposed Sentiment Analysis Classifier
(Table 1).
5 Sentiment Analysis Classifier Results
Sentiment analysis classifier tests the machine learning classifiers based on 1000
sentiments, sentiments are classified into agree sentiment and disagree sentiment. Naive
Bayes, SVM and Decision Tree were used for the classification of the sentiments.
Sentiments are converted in to English language using ACA dictionary created by the
author and revised by two linguistic experts. Table 2shows the results of the sentiment
model.
Table 1. Sample for ACA dictionary
ACA English
Ana mesh fahem I cannot understand
3agbny awy fekrt el game I do like the game idea
Brnamg bared Application is not good
Mewafee gedan I do strongly agree
Table 2. Sentiment analysis classifier results
Classifier Accuracy Precision Recall
NB 83% 0.76 0.67
SVM 79% 0.61 0.83
Decision Tree 76% 0.51 0.8
Student Sentiment Analysis Using Gamification 335
The results of sentiment analysis classifier agreed with the conclusion of [1,34]
proving that using Gamification can enhance the efficiency of student learning.
Sentiment analysis techniques is a very good reflector of the student emotions
towards learning, this is equivalent to the conclusion of [21,22]. NB had shown the
highest accuracy results which is agreed with [23] who uses NB to evaluate the sen-
timent option of students in learning.
Gamification affects the performance of student in learning. The two groups were
tested to compare the performance when using Gamification and the results had shown
that performance is affected by Gamification in which the highest grade reached by the
Gamification group whose sentiment is agree reached 98 which is equivalent to A + in
the credit hour system. Table 3summarize the test results.
In conclusion, sentiment analysis classifier compared to the other previous
researchers focuses on ACA which is used by the Arabic internet users; sentiment
analysis classifier uses an ACA dictionary that can be used on the following researchers
as a base for converting ACA to English.
6 Conclusion and Future Work
In this paper we proposed a sentiment analysis classifier, the sentiment classifier data
set was 1000 student sentiments that are divided into 700 agree sentiment and 300
disagree sentiments. The sentiment analysis classifier passes by text processing, feature
selection and machine learning classification, three classifiers were used NB, SVM and
decision tree. The results had shown that the best classifier accuracy results is the NB,
also when executing a test on the 1000 students, the agree group of using Gamification
in learning shown a better results comparing to the disagree group, this proves that
Gamification will enhance the student performance in learning. The limitation of the
Sentiment Analysis classifier is listed as follows: Sentiment analysis classifier should
be tested in a larger dataset and should also be applied in different student’s categories
such as: different learning year, different type of courses and even different universities
and the author should consider using different Machine learning classifiers can be used
in classification such as Neural Networks and more semantic relationships can be
extracted from the sentiment not only the synonyms WordNet relationship. The author
will focus on future plans to expand the results of the sentiment analysis classifier to be
tested on more students in different majors.
Table 3. Summary of test results.
Traditional (Disagree) group Gamification (Agree) group
Total number of students 300 700
Minimum grade 47 78
Maximum grade 91 98
336 L. Mostafa
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