IAES International Journal of Artificial Intelligence (IJ-AI)

Vol. 12, No. 1, March 2023, pp. 189~200

ISSN: 2252-8938, DOI: 10.11591/ijai.v12.i1.pp189-200  189

Motivation assessment model for intelligent tutoring system

based on Mamdani inference system

Rajermani Thinakaran1,2, Suriayati Chupra2, Malathy Batumalay1

1
Faculty of Data Science and Information Technology, INTI International University, Negeri Sembilan, Malayisa
2
Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

Article Info ABSTRACT

Article history: Many educators have used the benefit offer by intelligent tutoring system. To
become more personalizing and effective tutoring system, student
Received Jul 26, 2021 characteristics need to be considered. One of important student characteristic
Revised Aug 16, 2022 is motivation. Therefore, in this study a motivation assessment model based
Accepted Sep 14, 2022 on self-efficacy theory was proposed. Refer to the theory, effort, choice of
activities, performance and persistence were discussed as motivation
attributes. Further, time spend, difficulty level, number of correct answers and
Keywords: number of questions skipped are the parameters was defined for each attribute.
The model was designed by taking the advantages of Mamdani inference
Fuzzy logic system as fuzzy logic technique to predict students’ motivation level. The
Intelligent tutoring system model able to inmates like a human tutor does in the traditional classroom to
Mamdani method understand students’ motivation level.
Motivation
Motivation assessment model This is an open access article under the CC BY-SA license.

Corresponding Author:
Rajermani Thinakaran
Faculty of Data Science and Information Technology, INTI International University
Persiaran Perdana BBN Putra Nilai, 71800 Nilai, Negeri Sembilan, Malaysia
Email: rajermani.thina@newinti.edu.my

1. INTRODUCTION
The definition of motivation may take several forms and differ upon its application. According to
Keller and Litchfield [1], motivation can be defined as a persons’ desire to pursue a goal or accomplish a task.
Williams and Burden [2] define motivation as a “A state of cognitive and emotional encouragement, which
brings to a firm decision to act, and which gives rise to a period of sustained knowledge and/or physical effort
in order to reach a set of aim or aims”. Motivation has always been important for learning process and has a
great influence [3], [4]. In a real-world classroom, educators easily capture students’ motivation level during
learning process and adjusts lessons accordingly, in order to maximize the student’s interest and participation.
Educators usually understand student motivation level from observational cues such as student body language
or their behavior.
In e-learning environment mainly in intelligent tutoring system (ITS) the same consideration need to
be taken where the tutoring system able to recognize when the student is becoming demotivated. Vicente and
Pain [5] and Thinakaran and Ali [6] have argued that motivation components are as important as cognitive
components in ITS, and that important benefits would arise from considering techniques that track the students’
motivation. Thus, the authors claim that ITS should include a mechanism for detecting the students’
motivational level, and appropriately responding to that level. This study tries to address aforesaid issues by
proposing a model for motivation assessment in ITS that takes the active and successive environment of
motivation into account.

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2. REVIEW RELATED WORK

The capability to assess the students’ motivational level in ITS can bring numerous benefits. Since
motivation characterizes an important factor in learning process, different researchers have recommended
different motivation assessment to examine student motivation level in e-learning. From the literature, different
approach was proposed in order to measure and assess students’ motivation level and they can be grouped in
questionnaire-based approach, interaction-based approach, sentic modulation approach (physical assessment
of a persons’ emotional changes via sensors) and also hybrid-based approach. The followings are some of the
tutoring systems presented base on stated approaches.
Vicente and Pain [5] developed motivation diagnosis study (MOODS) for learning Japanese numbers
with an added motivation self-report facility. The motivation self-report facility is based on a number of
motivational factors consists of trait and state variables. First, student need to answer traits questionnaire before
carrying the exercises. In between answering the exercise, the student are required to feedback on their state
motivation factor. The state factors can be changed as often as possible since it is necessary for the computer
to understand student current motivation level in order to modify the instruction accordingly.
While, M-Ecolab was designed for teaching pupils aged between 9 to 11 years old related to food
chains and food-webs. M-Ecolab is the extension of Ecolab developed by Rebolledo-Mendez et al. [7] to
provide motivational scaffolding by an on-screen character called Paul at interaction time. The motivational
modeling was based on three motivational traits: effort, independence and the confidence. The system provides
Paul’s spoken feedback and gestures at pre- and post-activity according to the motivation model’s perception.
For example, if the motivation model determines a low state of motivation due to the quality of the actions
which was poor, Paul’s post-activity feedback states: “For the next node try to make fewer errors”. Under these
situations, Paul’s face would reflect concern.
Hurley and Weibelzahl [8] developed a motivational strategy recommender tool known as MotSaRT.
Its functionality enables the teacher to specify the students’ motivation profile. By observing the students’
activities and interaction, teacher would evaluate students’ motivation in terms of their self-efficacy, goal-
orientation, locus of control and perceived task difficulty. In the recommended strategies, depending on the
profile entered, a list of strategies will appear. MotSaRT would then classify this situation and sort the strategies
in terms of their applicability and plan their interventions according to the recommendations.
E-learning with motivational adaptation also known as ELMA developed by Endler et al. [9] presents
a fixed number of tasks and measures the student's motivational level during learning process. The system used
self-assessed motivation questionnaire. The questionnaire containing 7-point Likert scales with 18 questions
covering four motivation factors, anxiety, probability of success, interest, and challenge. In the questionnaire,
the student will be ask to report their current motivation based on the previous block of tasks. The complete
questionnaire could assess the student's motivation at the beginning and at the end of the program. Motivational
questionnaire covering each of the motivational factors was presented several times during the program to
make sure that the program always captured the learner's current motivation.
Derbali and Frasson [10] assessed student motivation level in ITS gameplay called Food-Force. To
assess student motivation level, physiological sensors which consists heart rate, skin conductance, and
electroencephalogram also known as EEG and self-reported scores of the ARCS model consist of attention,
relevance, confidence, and satisfaction have been considered. To assess motivation level, galvanic skin
resistance (GSR) electrodes and the blood volume pulse (BVP) sensor were attached to the fingers of
participant’s nondominant hands. GSR used to measure the conductance across the skin and BVP to measure
heart rate. An EEG cap fitted on learners’ heads to measure brainwaves. Self-reported scores of the ARCS
model used to identify four factors of motivation: attention, relevance, confidence, and satisfaction.
The intervention of students’ motivation assessment in ITS can bring many benefits but have some
drawbacks. MOODS [5] and ELMA [9] assess students’ motivation by asking how their feeling was in between
their learning process. These self-motivation reports cause interruption in student concentration in the learning
process. The interruption also can make student lost interest to continue the learning process. MotSaRT [8] is
a motivation strategy recommender tool, where the teacher has to enter students’ motivation level according
student activity in the tutoring system. Then the tool will suggest appropriate strategies to motivate the student.
In this intervention, the teacher still has to evaluate the students’ motivation level manually by interpreting
students’ activates in e-learning. Derbali and Frasson [10] used physiological sensors to assess students’
motivation level. Even though the intervention brings new dimension in student motivation assessment but in
real world is not applicable. Imagine that, student need to attach the particular devices at their body during in
their learning process and again this situation can disturb the student concentration. As conclusion, a motivation
assessment in ITS should be construct in the system itself without interruption students’ learning process. In
the following session, a motivation assessment model was proposed to assess students’ motivation level
without interruption students’ learning process.

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3. METHOD
In this study a deductive approach is used to reach a logical true conclusion [11]. The approach holds
a theory and based on it, make a prediction of its consequences. Figure 1 illustrated how the study carried out
using the deductive approach.

Figure 1. Deduction approach

The proposed motivation assessment model was design base on a well-known self-efficacy theory by
Bandura [12], a Canadian psychologist. He has claimed that self-efficacy beliefs effect on choice of activities
a student takes part in; the level of student effort expended in performing a task, persistence in the face of
difficulties in completing a task, and student performance in the task. Through research on self-efficacy as
learning motivations factor, many scholars have demonstrated their relationship. For example, Emre and
Ayverdi [13]; Durak et al. [14]; Gorson and O'Rourke [15], had state that individuals with a high perception
of self-efficacy on a particular situation strive to accomplish a task. They do not easily give up and are persistent
and patient. While Hattie [16], from 800 meta-analyses, the researcher has identified self-efficacy as the
strongest predictor of educational achievement.
Base on self-efficacy theory as motivation factor, choice of activities, effort, performance and
persistence were identified as motivation attributes. These motivation attributes were used in this study to
determine students’ motivation level. Choice of activities is defined as the level of challenging task the student
chooses [17]. Difficulty level of tasks such as low, medium, high, has been considered as a parameter to
measure choice of activities [18]. Effort define as the amount that the student is employing their self in order
to perform the learning activities [19]. To measure effort, the amount of time spent to perform a task [20] has
been considered as a parameter. Performance explains the student’s achievement on a specific topic [21]. To
measure performance, the number of correct answers has been considered as parameter [17], [21]. Persistence,
describe as a constant in performing an activity [21]. The number of questions skipped was used as a parameter
to measure persistence [17], [20].
Fuzzy logic (FL) as artificial intelligent technique applied to predict the students’ motivation level.
This technique was introduced by Zadeh [22] and used when conventional logic fails. It is a computational
paradigm which is based on human thinking. The aim of using FL technique in this study is to capture the
vagueness of effort, performance, choice of activities and persistence, then determine students’ self-efficacy
which are used together to draw the conclusion of students’ motivation level. The main advantage of FL is that
it uses reasoning that closely resembles human. Furthermore, motivation is characterized by ambiguity thus
difficult to quantify. Consequently, Wang and Hsieh [23] suggested the use of FL technique to help in solving
this problem.
In general FL technique consist of [24]: i) fuzzification which translates crisp (real-valued) inputs into
fuzzy values; ii) rule evaluation is an engine that applies a fuzzy reasoning mechanism to obtain a fuzzy output;
and iii) defuzzification which translates this latter output into a crisp value. There are 3 different inference
system which are widely used in FL which are Mamdani inference system [25], Sugeno inference system [26]
and Tsukamoto inference system [27]. The most widely used system is Mamdani inference system [28]. This
inference system also known as Max Min inference system which was introduced by Professor Ebrahim
Mamdani from London University [25]. The advantages are, it is intuitive; it has widespread acceptance, and

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it is well suited to human input. Hence, in this study Mamdani's Fuzzy inferences system as students’
motivation prediction technique was applied.

4. PROPOSED MODEL
To assess students’ motivation level, the authors has applied Mamdani's fuzzy inferences system. The
main advantage of Mamdani's fuzzy inferences system is that it uses reasoning that closely resembles the
presence of human. The aim of using Mamdani's fuzzy inferences system in this study is to capture the
vagueness of effort, performance, choice of activities and persistence which will therefore determines students’
self-efficacy to draw a conclusion on students’ motivation level. The following are steps describes how the
motivation assessment model was developed based on Mamdani fuzzy Inference System.

4.1. Determining the linguistic variables and fuzzy sets

Choice of activities (CA) parameter depends on the difficulty of each particular question. This
parameter is calculated as a weightage average difficulty of all solved questions by the student as in (1). The
weightage value for easy question is 1, medium question is 2 and hard question is 3. The weightage average
equation is given (1) where ans will be assigned as 1 if the question is answered correctly or else it will be
assigned as 0. The value of weightage average (wa) becomes a crisp value for CA.
1
𝑤𝑎 ∑𝑛𝑖=1(𝑞𝑖 = 𝑤𝑓 ∗ 𝑎𝑛𝑠) (1)
𝑛

Effort (EF) parameters depends on the time (t) taken by a student to answer a set of tutorial questions.
The maximum time depends on the time that the teacher has defined for solving a set of questions. For this
study an average of 1.2 minutes is given to answer each question. As in (2) is used to calculate time taken by
the student for answering the given questions. The time taken becomes a crisp value for EF.

𝑡 = ∑𝑛𝑖=1 𝑡𝑖𝑚𝑒𝑖 (2)

𝑡 = (𝑡𝑖𝑚𝑒1 + time2 + ⋯ + 𝑡𝑖𝑚𝑒𝑛)

Performance (PF) parameter depends on the number of correct answers answered by the student on
the particular set of tutorial questions. As in (3) is used to calculate total number of correct answers (cAns)
answered by the student over the total number of generated questions (numOfQuest) by the system times by
100%. The percentage of correct answers (%cAns) will be the crispy value for PF.

∑𝑛
𝑖=1 𝑐𝐴𝑛𝑠𝑖
𝑝𝑒𝑟𝐶𝑎𝑛𝑠 = × 100 (3)
𝑛𝑢𝑚𝑂𝑓𝑄𝑢𝑒𝑠𝑡𝑛

Persistence (PS) parameter depends on the number of skipped questions on a given tutorial. As in (4)
is used to calculated as the total number of skipped questions (sQuest) by the student over number of generated
questions (numOfQuest) by the system times by 100%. The percentage of skipped questions (%sQuest) will
be the crispy value for PS.

∑𝑛
𝑖=1 𝑠𝑄𝑢𝑒𝑠𝑡𝑖
𝑝𝑒𝑟𝑆𝑞𝑢𝑒𝑠𝑡 = × 100 (4)
𝑛𝑢𝑚𝑂𝑓𝑄𝑢𝑒𝑠𝑡𝑛

4.2. Fuzzification
Fuzzification, translates crisp (real-valued) inputs into fuzzy values using a membership function [23].
In this study, triangular and trapezoidal with R- and L- functions were used to translate each linguistic variable
value as crisp value into fuzzy values. The membership functions have proven popular with fuzzy logic and
have been in use extensively due to their simple formula and computational efficiency [24]. The following are
fuzzification for each input linguistic variable.
CA has 3 fuzzy sets shows in Figure 2 with possible values of easy, medium and hard which are
denoted as CA(x)={easy, medium, hard}. These distributions are formulated as in (5).

0, 𝑥 > 0.8
0.8−𝑥
𝐶𝐴𝑒𝑎𝑠𝑦 (𝑥) = {0.8−0.2 , 0.2 ≤ 𝑥 ≤ 0.8 (5)
1, 𝑥 < 0.2

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0, 𝑥 < 0.4
𝑥 − 0.4
, 0.4 ≤ 𝑥 < 1.0
𝐶𝐴𝑚𝑒𝑑𝑖𝑢𝑚 (𝑥) = 1.0 − 0.4
1.6 − 𝑥
, 1.0 ≤ 𝑥 ≤ 1.6
1.6 − 1.0
{ 0, 𝑥 > 1.6

0, 𝑥 < 1.2
𝑥 − 1.2
𝐶𝐴ℎ𝑎𝑟𝑑 (𝑥) = { , 1.2 ≤ 𝑥 < 1.8
1.8 − 1.2
1, 𝑥 > 1.8

Figure 2. Membership function for CA

EF has 3 fuzzy sets shows in Figure 3 with possible values of short, medium and long which are
denoted as EF(x)={short, medium, long}. These distributions are formulated as in (6).

0, 𝑥 > 9.0
9.0 −𝑥
𝐸𝐹𝑠ℎ𝑜𝑟𝑡 (𝑥) = {9.0−3.6, 3.6 ≤ 𝑥 ≤ 9.0 (6)
1, 𝑥 < 3.6

0, 𝑥 < 5.4
𝑥 − 5.4
, 5.4 ≤ 𝑥 < 10.8
𝐸𝐹𝑚𝑒𝑑𝑖𝑢𝑚 (𝑥) = 10.8 − 5.4
16.2 − 𝑥
, 10.8 ≤ 𝑥 ≤ 16.2
16.2 − 10.8
{ 0, 𝑥 > 16.2

0, 𝑥 < 12.6
𝑥 − 12.6
𝐸𝐹𝑙𝑜𝑛𝑔 (𝑥) = { , 12.6 ≤ 𝑥 < 18.0
18.0 − 12.6
1, 𝑥 > 18.0

PF has 3 fuzzy sets shows in Figure 4 with possible values of poor, good and excellent which are
denoted as PF(x)={poor, good, excellent}. These distributions are formulated as in (7).

0, 𝑥 > 40
40 −𝑥
𝑃𝐹𝑝𝑜𝑜𝑟 (𝑥) = {40−20 , 20 ≤ 𝑥 ≤ 40 (7)
1, 𝑥 < 20

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0, 𝑥 < 30
𝑥 − 30
, 30 ≤ 𝑥 < 50
𝑃𝐹𝑔𝑜𝑜𝑑 (𝑥) = 50 − 30
70 − 𝑥
, 50 ≤ 𝑥 ≤ 70
70 − 50
{ 0, 𝑥 > 70

0, 𝑥 < 60
𝑥 − 60
𝑃𝐹𝑒𝑥𝑐𝑒𝑙𝑙𝑒𝑛𝑡 (𝑥) = { , 60 ≤ 𝑥 < 80
80 − 60
1, 𝑥 > 80

Figure 3. Membership function for EF

Figure 4. Membership function for PF

PS has 3 fuzzy sets shows in Figure 5 which are low, medium and high and are denoted as PS(x) =
{low, average, high}. These distributions are formulated as in (8).

0, 𝑥 > 40
40 −𝑥
𝑃𝑆𝑙𝑜𝑤 (𝑥) = {40−20 , 20 ≤ 𝑥 ≤ 40 (8)
1, 𝑥 < 20

0, 𝑥 < 30
𝑥 − 30
, 30 ≤ 𝑥 < 50
𝑃𝑆𝑎𝑣𝑒𝑟𝑎𝑔𝑒 (𝑥) = 50 − 30
70 − 𝑥
, 50 ≤ 𝑥 ≤ 70
70 − 50
{ 0, 𝑥 > 70

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0, 𝑥 < 60
𝑥 − 60
𝑃𝑆ℎ𝑖𝑔ℎ (𝑥) = { , 60 ≤ 𝑥 < 80
80 − 60
1, 𝑥 > 80

Figure 5. Membership function for PS

The output variable which is called as motivation level (ML) of a student is also determined by the
fuzzy logic. The motivation level of a student has three fuzzy sets shows in Figure 6 which are low, medium
and high and are denoted as ML(x) = {Low, medium, high}. These distributions are formulated as in (9).

0, 𝑥 > 1
1 −𝑥
𝑀𝐿𝑙𝑜𝑤 (𝑥) = {1−0.5 , 0.5 ≤ 𝑥 ≤ 1 (9)
1, 𝑥 < 0.5

0, 𝑥 < 0.75
𝑥 − 0.75
, 0.75 ≤ 𝑥 < 1.5
𝑀𝐿𝑚𝑒𝑑𝑖𝑢𝑚 (𝑥) = 1.5 − 0.75
2.25 − 𝑥
, 1.5 ≤ 𝑥 ≤ 2.25
2.25 − 1.5
{ 0, 𝑥 > 2.25

0, 𝑥 <2
𝑥−2
𝑀𝐿ℎ𝑖𝑔ℎ (𝑥) = { , 2 ≤ 𝑥 < 2.25
2.25 − 2
1, 𝑥 > 2.25

Figure 6. Membership function for ML

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4.3. Fuzzy inferencing or evaluate rules

The logic for assessing students’ motivation level is encoded as a set of if-then rules. The antecedents
of the production rules consist of CA, EF, PF, PS and one set of values representing the conclusion and, the
rules consequent (motivation level-ML). A rule is defined as every possible combination of antecedents that
may occur. In this study, 81 rules were obtained as the combination of each value (difficulty level, time, number
of correct answer and number of skipped questions) from CA, EF, PF and PS. However, only 26 rules have
been logically accepted. The following shows one of linguistic rule used whereby the inputs (antecedents) are
combined logically using the AND operator in order to get students’ motivation level as output (consequent).
The output of students’ motivation level is denoted as ML(x)={low, medium, high}.

Rule Linguistic rules

1 IF CA is easy AND EF is short AND PF is poor AND PS is low THEN ML is low.

4.4. Rules output

The min method is applied as an implication function. It combines each degree of memberships to
each if-then rule then truncates the output. For example, a student manages to answer 4 easy questions correctly
out of 12 questions within 15 minutes and skips all the medium and hard questions. The following is Rule 1
using min method while Figure 7 illustrates in a graphical view. This method is repeated so that the output
membership functions are determined for all 26 rules as shown in Figure 8 in a graphical view.

Rule 1 = IF CA is easy AND EF is short AND PF is poor

AND PS is low
THEN ML is low.

= min (CA(x) ∩ EF(x) ∩ PF(x) ∩ PS(x))

= min (CA (4) ∩ EF (15) ∩ PF (4) ∩ PS (8))
= min (0.33 ∩ 15.00 ∩ 16.70 ∩ 66.70)
= 0.33

On the other hand, the max method is applied as an aggregation function. The input for the aggregation
process is the list of truncated output returned by the implication process for each rule. Figure 9 shows all 26
rules which are displayed to show how the rule outputs are aggregated into a single fuzzy set whose
membership function is assigned for every output (motivation) value and are represented in a graphical view.

Figure 7. Implication function using min method for rule 1

4.5. Defuzzification
Defuzzification functions to convert the fuzzy values into crisp values. The input for the
defuzzification process is the aggregate output. In this study, a Centroid method was applied which is one of
the most common methods used. The Centroid method which returns the center of area under the curve is
shown in Figure 10 in a graphical view. From the example given, the defuzzified value is between 0 and 1.
Therefore, it can be concluded that the students’ motivation level is recorded to be at 0.452 which is considered
to be at a low level.

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Figure 8. Implication function using min method for overall rules

Figure 9. Aggregation function using max method

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Figure 10. Defuzzify the motivation level using centroid method

Figure 11 display the steps how the motivation assessment model was developed based on Mamdani
Fuzzy Inference System. The steps started with deciding linguistic variables and fuzzy sets; translates crisp
inputs into fuzzy values using a membership function; Fuzzy inferencing; and defuzzification. Following with
motivation assessment algorithm shows in Figure 11 derived from motivation assessment model shows in
Figure 12. While Figure 11 is motivation assessment algorithm derived from motivation assessment model
which was illustrated in Figure 11. Figure 12 as shown in Appendix.

Figure 11. Motivation assessment model based mamdani fuzzy inference system

5. CONCLUSION AND FUTURE WORK

Predicting student motivation level in holds great promise for ITSs. The proposed model can be used
to detect student motivation level during their learning process. This model describes all the steps of inference
starting from fuzzification, rule evaluation and defuzzifiction. Future work will involve implementation of the
proposed model into ITS. The model will be incorporated with ITS architecture specifically in student or user
model. Besides detection of student motivation level, the tutoring system aims some recommendations in
automatic manner based on student motivation level, much like in the traditional classroom.

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APPENDIX

BEGIN
start time
//generate 12 mcqs one by one
for (q = 1; q <= 12; q++){
display question
read(ans)
//calculate weightage factor
wfans = (wf * ans) + wfans
if (ans == True) //calculate correct answer
cAns = cAns +1
//calculate number of skipped questions
if (ansSkipp == True)
sQuest= sQuest +1
}
stop time
wa = wfans /12 // As in (1)
t = stop time – start time // As in (2)
perCans = (cAns /12) *100 // As in (3)
perSquest = (sQuest / 12) *100 // As in (4)
/*translates crisp inputs into fuzzy values using membership function*/
CA(x)← difficulty level (wa)
EF(x)← time taken (t)
PF(x)← number of correct answered (perCans)
PS(x) ← number of skipped question (perSquest)
//rules output
(min method) ← 26 rules //implication function
// aggregation function
(max method) ←output of min method on 26 rules
/*Defuzzification is converts the fuzzy values to crisp values */
ML(x)← (Centroid method)
display (ML(x))
END

Figure 12. Motivation assessment algorithm

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BIOGRAPHIES OF AUTHORS

Rajermani Thinakaran holds a doctor degree from Universiti Teknologi Malaysia

(UTM), Malaysia in 2019. She also received her Master in IT from Universiti Kebangsaan
Malaysia (UKM) and Bachelor in Science (Computer Science) from UTM in 2012 and 1995,
respectively. She is currently a senior lecturer at Faculty of Data Science and Information
Technology in INTI International University, Negeri Sembilan, Malaysia. Her research interests
lie in the area of artificial intelligent, assistive technology in empowering disabled students, e-
learning and gamming ranging from theory to design to implementation. She supervises both
undergraduate and postgraduate students (Masters and PhD levels). She can be contacted at email:
rajermani.thina@newinti.edu.my or rajermani@yahoo.com.

Suriayati Chuprat is an Associate Professor at Advanced Informatics Department

of Razak Faculty of Technology Informatics, Universiti Teknologi Malaysia. She holds a
Bachelor Degree in Computer Science, with concentration in Software Engineering and
Management Information Systems, a Master in Software Engineering and a PhD in Mathematics.
She was attached to the University of North Carolina, USA, as part of her PhD research, where
she worked with Professor Sanjoy K. Baruah on real-time scheduling in parallel computing. She
can be contacted at email: suriayati.kl@utm.my.

Ir. Dr. Malathy Batumalay holds a BEng. (Electrical Engineering) form University
Tun Hussein Onn, MEng. (Telecommunication) from University Malaya and Ph.D. (Photonics)
from University Malaya. Currently she is attached as Associate Professor with the Faculty of Data
Science and Information Technology in INTI International University, Negeri Sembilan,
Malaysia. She focuses on the research of Photonics Engineering, Fiber Optics and Lasers
technology. In her previous research work, she developed fiber optics into sensors to monitor the
relative humidity, temperature and also as biosensor. She is currently collaborating with local
Universities to further enhance the performance of sensors for several applications. She can be
contacted at email: malathy.batumalay@newinti.edu.my.

Int J Artif Intell, Vol. 12, No. 1, March 2023: 189-200