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Research Article

Interactive Learning System for Learning Calculus

[version 1; peer review: 2 approved with reservations]
PUBLISHED 14 Mar 2022
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This article is included in the Research Synergy Foundation gateway.

Abstract

Background: IT tools assist in creating a more participative and independent learning environment. They have brought a new perspective to collaborative learning where students do not just sit in a chair and swallow lecture content but instead participate in creating and sharing knowledge. However, interactivity promoted through the implementation of technology is limited in many cases.   
Purpose: This research develops an interactive application for learning calculus that promotes human-system interaction via augmented reality (AR) and human-human interaction through chat functions. The study examines the effect of both interactivities on learning experience and how that learning experience affects the performance of learning.  
Methods: The research adopted a quasi-experimental study design and pre-post test data analysis to evaluate the effect of interactivities on learning experience and consequently the effect of learning experience on learning performance. The subjects were exposed to the developed application for learning the calculus chapter ‘Revolution of Solids” in a controlled environment. The study validated its research framework through partial least squares path modelling and tested three hypotheses via pre-and post-test evaluation.  
Conclusions: The results found that both interactivities affect learning experience positively; human-human interactivity has a higher impact than human-system interactivity. It was also found that learning performance as part of the learning experience increased from pre-test to post-test.

Keywords

Interactive learning system, augmented reality, learning experience, learning performance

Introduction

In traditional classroom environments, there exists a problem with student participation.1 It is also stated that the traditional method is inefficient as it is mostly spoon-feeding, and students’ analytical processes are absent due to a lack of peer interaction.2 Although some studies have addressed the issues of student interaction with the system there is still a lack of teacher-student or student-student interaction in interactive learning systems.13 Many systems have promoted a single type of interactivity.410

Calculus is one of the core subjects in computer science studies. Malaysian students pursuing diplomas and degrees in Information Technology take calculus subjects that include differentiation, and integration. Application of integration is one of the chapters in Calculus that requires spatial visualization and the 2D medium of the traditional classroom does not provide a proper solution.1113

In understanding the revolution of solids around an axis, visualization of the solid in-question is very important. Spatially related 3D images cannot be drawn on a 2D board or projected over a computer. These hinder students in visualizing the solid and result in difficulties in conceptualizing the concept. Although researchers have implemented AR to assist in visualization, they are mainly focused on human-system interaction, either through haptic or non-haptic interaction.1417 Besides, agility is another issue that some systems could not address as they are desktop-based systems.1719 As such, developing an interactive AR application that facilitates both human-human and human-system interactivities and assists in conceptualizing and understanding math better is needed.

The use of interactive technology in pedagogy has become very common in the last decade. Interactive technology has made learning more personalized and kept students engaged through various applications.20 Abykanova et al. also suggested that interactive technology can make learning more active, and intensive for students where students can communicate easily with each-others and also with the teacher.20 Game-based learning, multimedia lectures and labs, and electronic study guides are some examples of the most common interactive technologies in the pedagogy sector. Recently implementation of Augmented Reality (AR) in pedagogy has been adopted by many researchers where the emphasis has mainly been kept on 3D visualization. Many have implemented AR in geometry for school students14,19,21 while some others implemented it for calculus.12,13 In both cases, the authors have focused on the immersive learning experience through AR that allows students to comprehend and conceptualize learning content.

Literature review

Implementation of interactive learning systems in class to engage students in learning has become a common focus in the pedagogical arena. A system that promotes interpersonal interaction and provides a sense of others’ presence can be considered as an interactive system.4

Interactivity

In an attempt to increase class interactivity different types of technologies have been implemented in class. These can be categorized as synchronous and asynchronous mediums of interaction.22 Synchronous is used as a medium of interaction during class, especially when participating in in-class discussions, whereas asynchronous is for facilitating learning remotely, which may happen after the class. Examples of synchronous learning tools include interactive whiteboards2 and virtual-reality-based systems for learning language.4 An example of an interactive learning system for asynchronous learning is an AR-based interactive book.5 On the other hand, interactivity can also be categorized as human-system and human-human, as technologies are allowing both in a learning environment.3 Human-human interactivity is further divided into teacher-student and student-student interactivity. Table 1 shows promoted interactivities by different studies. These studies focused primarily on human-system interactivity while a few provided human-human interactivities.

Table 1. Interactivities of different studies.

NoStudyTeacher-StudentStudent-StudentInteractive system
11×
22
34××
410××
55××
66××
743×
89××
93
107××
1140×
128××

AR has become a trending technology in the pedagogical sector to provide students with an immersive experience. AR provides a real and virtual experience together to allow users real-time interaction.16 3D representation of a virtual object in a real environment can make learning more engaging as it provides a visual learning experience. This can be helpful to the majority of students as studies have found that there are more visual learners compared to auditory or kinesthetic learners.23 Besides, haptic interaction happens when users are allowed to interact with the 3D object through AR technology24 which also address kinesthetic learners as they learn through physical involvement. Table 2 depicts the interactivities promoted by different AR studies.

Table 2. AR interactivities of different studies.

NoStudyTeacher-StudentStudent-StudentInteractive book/systemMobility
116××
224××
314××
418×××
519×××
617×××
715××
841××

From the studies depicted in Table 2, five papers adopted mobility in their AR system except the three that were desktop-based. This table also shows that the systems solely focused on interaction through AR and not on implementing any human-human interactivity.

Learning Experience

The learning experience can be defined as the experience a learner goes through while learning content set by an institution.25 Including active engagement and collaboration as a part of it affects learning performances26,27 Furthermore, implementation of technology in class affects the learning experience positively.28

Methods

A pre-test and post-test quasi-experimental research design was adopted. The data was collected through a personally administered questionnaire survey. As the study was designed to be conducted using a pre-and post-test methodology, a longitudinal time horizon was adopted since the study was comparing students’ learning experience and performance before and after using the application.

Sampling method

Sample size calculation was done by following the 10 times rule, where the sample size is required to be 10 times larger than the maximum number of structural paths directed towards a latent variable.30 As such the minimum sample for this study is supposed to be larger than 20 as two paths are directed towards the latent variable. The sample size for this research was 59, but after eliminating missing data and straight-line answers the data was analyzed from 55 respondents. The respondents were students from a Malaysian university taking Calculus. At the end, the data was analyzed and hypotheses were accepted or rejected based on the p-values. Figure 1 illustrates the research design of this study.

24c705b3-9212-411d-8035-197af314469b_figure1.gif

Figure 1. Research design.

Research framework

This research has identified two types of interactivities, human-human and human-system. Figure 2 depicts the research framework of this study.

24c705b3-9212-411d-8035-197af314469b_figure2.gif

Figure 2. Research framework.

An interactive learning system based on AR was developed. Human-system and human-human interactivities were included as part of application functions. Human-system interaction was promoted by haptic interaction based on marker-based AR technology whereas human-human interactivity was implemented through a function of discussion platform. The framework was developed to find how these two interactivities affect the learning experience, the first dependent variable, and lastly how the learning experience incurred by the promoted interactivities affects students’ academic performance.

This study hypothesizes:

H1:

Human-system interaction is positively associated with students’ learning experience.

H2:

Human-human interaction is positively associated with students’ learning experience.

H3:

Improvement of learning experience improves the performance of learning

Research procedure

This research was conducted into three phases, in the first phase the students were asked to answer a pre-questionnaire and a quiz before using the application, in the second phase they used the application and explored AR, and lastly, in the third phase, they answered a post-test questionnaire.

Figure 3 shows students exploring the AR function of the application.

24c705b3-9212-411d-8035-197af314469b_figure3.gif

Figure 3. Implementation of AR system in class.

Survey design

The survey questionnaire was divided into segments of three. The first section (A) was demographic questions; the second section (B) evaluated the two independent variables and one dependent variable, and in the third section (C) dependent factor learning performance was evaluated through a quiz developed by the subject expert.

Section A comprises of six questions regarding participants’ general information that included gender, ethnicity, age and whether they have used any educational AR application before or not. All the questions in this section were self-designed.

Section B evaluates the variables of the research framework comprised of self-developed and adopted questions from multiple sources. Independent variable human-human interaction measurement questions were self-developed. Human-human interaction questions were formed based on the idea of peer-peer interaction and student-teacher interaction shown in Table 3.

Table 3. Questionnaire of human-human interactivity factor.

QuestionPre-testPost-test
1This lecture facilitates interaction with peersThis application facilitates interaction with peers
2This lecture facilitates interaction with teachersThis application facilitates interaction with teachers
3This lecture provides opportunities to discuss with peersThis application provides opportunities to discuss with peers
4This lecture provides opportunities to discuss with teacherThis application provides opportunities to discuss with teacher
5This lecture allows exchange of information with peersThis application allows exchange of information with peers
6This lecture allows exchange of information with teachersThis application allows exchange of information with teachers

For the second independent variable human-system interaction the questions were adapted from Sumadio and Rambli30 and are depicted in Table 4.

Table 4. Questionnaire of human-system interactivity factor.

QuestionAdopted paperThis paper
7Easy to useIt is easy to use
8Offers functions to solve tasks efficientlyIt gives opportunity to understand the lesson more easily through AR object
9Provides a good overview over its functionalityIt provides a good overview over its functionality
10Offers an opportunity to stop the task and continue at the same point later onIt offers an opportunity to stop the task and continue at the same point later on
11Easy to learn without somebody help or manualIt is easy to learn without somebody’s help or manual
12The familiarisation with gestures and manipulating virtual objects has been easyI find the augmented object realistic
13The three-dimensional virtual figures are clear and do not present definition difficultiesThe manipulation of augmented 3D solid was smooth
14Utilising materials (design notebook) and augmented reality technology has been easy and intuitiveIt is easy to have 360 degree view by rotating the solid via finger touch

The learning experience variable was evaluated based on the adopted questionnaire from31 shown in Table 5 and also from the self-developed questionnaire listed in Table 6.

Table 5. Questionnaire of learning experience factor.

NoAdopted paperCurrent study
15The quality of the augmented reality material helped to hold my attentionThe quality of the augmented reality material helped to hold my attention
16The variety of audio visual material helped keep my attention on the lessonThe variety of audio visual material helped to keep my attention on the lesson
17The content of this material is relevant to my interestsThe AR content of this material was relevant to my lesson
18The content of this material is relevant to my interestsThe audio visual material in this lesson is related to my lesson
19As I worked on this lesson, I was confident that I could learn the contentThe AR and audio visual contents in IARA enabled me to learn independently
20After working on this lesson for a while, I was confident that I would be able to pass a test on itAfter working on this lesson for a while, I was confident that I would be able to pass a test on it
21It was a pleasure to work on such a well-designed lessonI felt good to interact with AR object
22It was a pleasure to work on such a well-designed lessonI enjoyed the audio visual content so much that I would like to know more about this topic
23It was a pleasure to work on such a well-designed lessonI felt good after sharing information through discussion platform

Table 6. Self-developed questionnaire of learning experience factor.

NoSelf-developed
24Video of object formulation through revolution of solid has deepen my understanding
25AR object has facilitated in visualizing the solid better
26I am satisfied with the content and function of this application

Section C was developed by a calculus subject expert in evaluating learning performance via a quiz. The quiz included True/False and formative questions.

Results

This study conducted two types of analysis through Smart PLS 3.0 and SPSS 22 (IBM SPSS Statistics, RRID: SCR_019096) for conducting structural model assessment and pre-and post-test comparison of variables respectively. R is an open-source alternative software (R Project for Statistical Computing, RRID: SCR_001905) for Smart PLS.

Descriptive statistics

Demographic profile

This study collected 55 valid responses from the selected sample of 59 students from a Malaysian private university (Table 7). Out of 55 respondents, 46 were male (83.6%) while 9 were female constituting 16.4% of total respondents. The result is in line with other research findings as the gender ratio significantly skews towards males in the technology field.32,33 Among the three main ethnicity groups 43 students were Chinese (78.2%), followed by six Malay and six Indians constituting 10.9% each. As the sample was from pre-university students, the age range was from 18-22. The majority of students were 19 years old (60%) where 18 and 22 were the smallest age groups, constituting 3.6 % each.

Table 7. Demographic information.

CriterionFrequencyPercentage
GenderMale4683.6
Female916.4
RaceMalay610.9
Indian610.9
Chinese4378.2
Age (years)1823.6
193360.0
201221.8
21610.9
2223.6

Students’ experience of using similar types of applications in learning provides an indication of whether users are experienced in using this type of application or not. It is found that none of the students had used an AR application before.

Inferential statistics

The study adopted a PLS_SEM approach to maximize the variance of the defined framework’s endogenous construct.34 Structural model analysis was used to test the hypotheses along with its predictive accuracy and effect size. All the constructs were measured with five or more indicators. As the model is reflective, the constructs are reflective too. “Internal consistency reliability”, “convergent validity” and “Discriminant Validity” are the three criteria used to assess the constructs.

Internal consistent reliability

To evaluate the same source biasedness coefficient variation, Internal consistency reliability is used to investigate the reliability of indicators that measure a latent variable. From Table 8, each construct satisfied the criteria of composite reliability (CR) of ≥ 0.7.35 As such it can be concluded that the constructs met the internal consistency reliability criteria.

Table 8. Convergent validity and composite reliability.

ConstructItemLoadingCRAVE
Human-Human InteractionHHI10.6780.8860.564
HHI20.783
HHI30.771
HHI40.796
HHI50.724
HHI60.747
Human-System InteractionHSI10.6650.8860.526
HSI20.728
HSI30.8
HSI40.801
HSI50.683
HSI60.691
HSI70.697
Learning ExperienceLE110.740.8910.505
LE120.69
LE20.735
LE50.649
LE60.693
LE70.732
LE80.673
LE90.766

Convergent validity

Convergent validity is measured by the indicator of Average Variance Extracted (AVE) and factor loading.35 AVE indicates what percentage of the variance of a construct is defined by a marker.35 The acceptable value of AVE for each construct must be ≥ 0.5. From the AVE value depicted in Table 8, all constructs satisfied the convergent validity criteria. Factor loading of each indicator ≥ 0.6 is acceptable.36

Discriminant validity

Discriminant validity is measured by the Fornell and Larcker Criterion (FLC), cross-loading comparison and HTMT technique.37 Table 9 shows that all constructs satisfied FLC criteria, where the square roots of AVEs for the reflective constructs of HHI (0.751), HSI (0.726) and LE (0.711) are larger than the correlation for all other constructs diagonally.

Table 9. Discriminant validity-fornell and larcker criterion.

Human-Human InteractionHuman-System InteractionLearning Experience
Human-Human Interaction0.751
Human-System Interaction0.5650.726
Learning Experience0.7140.6160.711

From Table 10 of cross loading, all indicators load high on their own constructs compared to others. This confirms that the constructs are distinct from each other indicating discriminant validity as a result.

Table 10. Convergent validity cross loading.

Human-Human InteractionHuman-System InteractionLearning Experience
HHI10.6780.4120.422
HHI20.7830.4660.552
HHI30.7710.4370.504
HHI40.7960.4470.636
HHI50.7240.4740.492
HHI60.7470.3260.574
HSI10.2590.6650.214
HSI20.4940.7280.444
HSI30.4320.80.498
HSI40.3770.8010.33
HSI50.3430.6830.438
HSI60.4840.6910.547
HSI70.3760.6970.475
LE110.4310.5010.74
LE120.4230.4340.69
LE20.6080.3790.735
LE50.5140.4650.649
LE60.5490.4760.693
LE70.4630.5750.732
LE80.4140.260.673
LE90.6050.3750.766

The HTMT mechanism of assessing discriminant validity requires the value of HTMT to be lower than 0.85 for stringent criterion and 0.9 for conservative criterion.37 From Table 11, all the constructs satisfy the above criteria confirming discriminant validity thereof.

Table 11. Discriminant validity-HTMT.

Human-Human InteractionHuman-System InteractionLearning Experience
Human-Human Interaction
Human-System Interaction0.646
Learning Experience0.8170.672

From all the criteria of confirmatory factorial analysis, the research model is adequately fitting to be accepted. As such this designated measurement model with specified latent variables has been analyzed with SEM criteria.

Structural model assessment

Path analysis was performed to find the hypothesized relationship. The results for collinearity assessment and hypothesis are shown in Table 12.

Table 12. Collinearity assessment and hypothesis test.

Latent variablesVIFBeta ßt-valuep-valuef2
Human-Human Interaction -> Learning Experience ***1.470.5374.6290000.463
Human-System Interaction -> Learning Experience **1.470.3132.5880.0050.157

Both latent variables Human-Human Interaction (HHI) and Human-System Interaction (HSI) have positive effects on Learning Experience (LE). The variance inflation factor (VIF) results in Table 12 show that the lateral multicollinearity meets the criteria of being above the threshold of 0.2 and below the threshold of 5, implying collinearity was not an issue in the structural model.35

A study by35 suggested that for a one-tailed test “t-values” for a significant level of five per cent (α = 0.05) are required to be greater than 1.645. The result indicated that both exogenous constructs HHI and HSI have a “t-value” of >1.645 for a significant level of five per cent (α = 0.05). From the results, both latent variables have a positive relationship with LE, HHI being the stronger predictor than HSI.

Predictive accuracy, effect size and relevance

Predictive accuracy” is evaluated through the “coefficient of determination, R2”. R2 values imply the predictive power of exogenous constructs over endogenous ones. From the analysis construct LE’s R2 value is found as 0.576 which means exogenous constructs substantially explain 57.6% of LE’s variance as predictive power can be considered as substantial if it is greater than 0.26.38 Although35 has stated R2 value less than 0.67 is moderate.

To evaluate the effect size of exogenous constructs Cohen’s f2 value was obtained from the model analysis.38 stated that the f2 value of 0.35 is considered a substantial effect whereas 0.15 is considered moderate. From Table 12 it can be seen that the HHI construct has a substantial effect on LE (0.463) and HSI has a moderate effect (0.157).

In addition, the Q2 value of endogenous construct LE was found at 0.269 indicating moderate “predictive relevance”.35 Besides, as the Q2 value is larger than 0, it can be concluded that HHI and HIS exogenous constructs have “predictive relevance” for the endogenous construct LE.39

Pre-test post-test

For understanding the significance of pre-and post-test performance of student paired sample t-test was carried out and the result is shown in Table 13. From Table 13 it can be concluded that there is a significant relationship between the results of the pre-and post-tests as the P<0.05.29 The post-test mean implies that students’ performance results in the post-test are higher than the pre-test one signifying a positive improvement in the performance of learning.

Table 13. Pre and post test analysis of performance.

Paired samples test
Paired differencestdfSig. (2-tailed)
MeanStd. deviationStd. error mean95% confidence interval of the difference
LowerUpper
Pair 1Pre-test Post-test−1.436361.43712.19378−1.82487−1.04786−7.41254.000

Discussion

From the result of the hypotheses, two hypotheses, H1 and H2 were supported – the human-human factor has a larger impact than the human-system factor.

Human-human interaction through the application has a mean of 3.92 compared to human-human interaction without the application with a mean of 3.70. Human-human interaction affects the learning experience positively which is in line with results found by.1,2,3,40

Human system interactivity from AR has also affected learning experience positively in line with the related research.14,16,24,41 The learning experience mean score is 3.42 prior to application usage whereas later it turned to 3.68.

From the pre-and post-test results, the learning experience has increased the mean score of students’ performances. Learning experience, because of these interactivities, has affected performance positively similarly to results found in other studies.3,15

Conclusions

The role of interactivity in the learning experience has been established by many studies before, but implementing both human-human and human-system interactivities in an augmented reality application was overdue. This research had done exactly that and analyzed the effectiveness of the research framework by using PLS. From the results, it can be concluded that the model was fit to analyze the research framework with substantial predictive accuracy and a moderate effect size of the exogenous variable with a moderate relevance on endogenous variable LE. All three hypotheses are confirmed as P values for all three are at a satisfactory level. These results imply that human-human and human-system interactions positively affect learning experience and performance of learning as a result of an enhanced learning experience.

Competing interests

This article has obtained public disclosure approval from Multimedia University. The authors declare that there is no conflict of interest.

Ethical considerations and consent

All the procedures performed in this study involving human participants were in adherence to the ethical policies of the University as approved by the Technology Transfer Office of Multimedia University under ethical approval number: EA0702021.

Written consent was also obtained from all individual participants involved in the study. Personal data from individuals was promised to be kept confidential and strictly restricted for use in this study only.

Grant information

This research is funded and supported by Multimedia University under the MMU Graduate Research Assistant (GRA) Scheme with Grant Reference Number: MMUI/170099. Md Asifur Rahman is the appointed GRA for this grant.

Data availability

Zenodo: IARA LP Dataset. https://doi.org/10.5281/zenodo.574496042

This project contains the following underlying data:

Dataset IARA LP 01122021.xlsx (The file contains two sheets. The first one contains the indicators of three variables; Learning Experience, Human-Human Interaction and Human-System Interaction which were used for framework analysis. The second sheet includes the results of students' performance before and after using the learning system as pre-test and post-test).

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

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Rahman MA, Sook Ling L and Yin OS. Interactive Learning System for Learning Calculus [version 1; peer review: 2 approved with reservations]. F1000Research 2022, 11:307 (https://doi.org/10.12688/f1000research.73595.1)
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ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions
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Reviewer Report 12 Apr 2022
Affandy Affandy, Faculty of Computer Science, Dian Nuswantoro University, Semarang, Indonesia 
Approved with Reservations
VIEWS 16
This research explores the benefits of human interaction in interactive learning systems with visualization through augmented reality. The subject exposed is calculus on the topic "Revolution of Solids". The research compares the impact of human-system interaction support with human-human interaction ... Continue reading
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Affandy A. Reviewer Report For: Interactive Learning System for Learning Calculus [version 1; peer review: 2 approved with reservations]. F1000Research 2022, 11:307 (https://doi.org/10.5256/f1000research.77256.r127478)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
  • Author Response 13 Apr 2024
    Sook Ling Lew, Faculty of Information Science and Technology, Multimedia University, Ayer Keroh, 75450, Malaysia
    13 Apr 2024
    Author Response
    Comment 1: This research explores the benefits of human interaction in interactive learning systems with visualization through augmented reality. The subject exposed is calculus on the topic "Revolution of Solids". ... Continue reading
COMMENTS ON THIS REPORT
  • Author Response 13 Apr 2024
    Sook Ling Lew, Faculty of Information Science and Technology, Multimedia University, Ayer Keroh, 75450, Malaysia
    13 Apr 2024
    Author Response
    Comment 1: This research explores the benefits of human interaction in interactive learning systems with visualization through augmented reality. The subject exposed is calculus on the topic "Revolution of Solids". ... Continue reading
Views
28
Cite
Reviewer Report 28 Mar 2022
Yong Wee Sek, Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia, Melaka, Malaysia 
Approved with Reservations
VIEWS 28
In this paper, the authors introduced the interactive learning system for learning Calculus. There is a great deal of good work in this paper. However, it does require a significant overall revision to make the best of this and present ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Sek YW. Reviewer Report For: Interactive Learning System for Learning Calculus [version 1; peer review: 2 approved with reservations]. F1000Research 2022, 11:307 (https://doi.org/10.5256/f1000research.77256.r127477)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
  • Author Response 13 Apr 2024
    Sook Ling Lew, Faculty of Information Science and Technology, Multimedia University, Ayer Keroh, 75450, Malaysia
    13 Apr 2024
    Author Response
    Comment 1: In this paper, the authors introduced the interactive learning system for learning Calculus. There is a great deal of good work in this paper. However, it does require ... Continue reading
COMMENTS ON THIS REPORT
  • Author Response 13 Apr 2024
    Sook Ling Lew, Faculty of Information Science and Technology, Multimedia University, Ayer Keroh, 75450, Malaysia
    13 Apr 2024
    Author Response
    Comment 1: In this paper, the authors introduced the interactive learning system for learning Calculus. There is a great deal of good work in this paper. However, it does require ... Continue reading

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Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions
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