=Paper=
{{Paper
|id=Vol-3917/paper25
|storemode=property
|title=Design and evaluation of a personalized digital mathematics tutor for grade 6 learners
|pdfUrl=https://ceur-ws.org/Vol-3917/paper25.pdf
|volume=Vol-3917
|authors=Svitlana V. Shokaliuk,Andrii O. Kavetskyi
|dblpUrl=https://dblp.org/rec/conf/cs-se-sw/ShokaliukK24
}}
==Design and evaluation of a personalized digital mathematics tutor for grade 6 learners==
https://ceur-ws.org/Vol-3917/paper25.pdf
Svitlana V. Shokaliuk et al. CEUR Workshop Proceedings 58–65
Design and evaluation of a personalized digital
mathematics tutor for grade 6 learners
Svitlana V. Shokaliuk, Andrii O. Kavetskyi
Kryvyi Rih State Pedagogical University, 54 Universytetskyi Ave., Kryvyi Rih, 50086, Ukraine
Abstract
This paper presents the design, development, and evaluation of an adaptive mathematics assessment tool for grade
6 students. The tool uses Python and CustomTkinter to create an engaging and personalized user experience. It
generates adaptive questions, offers immediate feedback, and tracks student progress in a real-time. A quasi-
experimental study was conducted, comparing the tool’s effectiveness with traditional assessment methods.
Results indicate that students using the tool demonstrated more positive attitudes compared to the control group.
System performance was also evaluated, showing an efficient and smooth user experience, with an average
response time of 1.2 seconds. Future work will focus on expanding the tool’s content coverage and integrating
machine learning techniques to further enhance adaptability and personalized feedback.
Keywords
adaptive assessment, mathematics education, personalized learning, Python, CustomTkinter, user interface
design, student engagement
1. Introduction
Mathematics education plays a crucial role in the cognitive development and academic success of
students in grade 6. At this stage, learners are expected to master fundamental mathematical concepts
and problem-solving skills that lay the foundation for higher-level mathematics in subsequent years
[1, 2]. However, providing personalized assessment and feedback to cater to the diverse learning needs
of students remains a significant challenge for educators [3, 4].
Traditional assessment methods often fail to capture the individual strengths and weaknesses of
learners, leading to a one-size-fits-all approach that may hinder their progress [5]. Moreover, the lack of
timely and constructive feedback can demotivate students and impede their understanding of complex
mathematical concepts [6]. To address these challenges, researchers have explored the potential of
technology to enhance mathematics learning and assessment in grade 6 [7, 8].
Computer-based assessment tools have shown promise in providing adaptive and interactive learning
experiences that cater to the unique needs of each student [9, 10]. By using advanced algorithms and
user-friendly interfaces, these systems can generate personalized questions, offer immediate feedback,
and track student progress in a real-time [11, 12]. A technology-enhanced assessment can promote
student engagement, motivation, and self-regulated learning, which are essential for long-term success
in mathematics [13, 14].
The primary objective of this research is to develop and evaluate an interactive mathematics assess-
ment tool for grade 6 students using Python and CustomTkinter. Specifically, we aim to investigate the
following research questions:
RQ1: How can an automated assessment system be designed to generate adaptive questions and provide
personalized feedback based on student performance? Specifically, what algorithms and system
architecture can effectively model student knowledge and adapt the assessment in real-time?
CS&SE@SW 2024: 7th Workshop for Young Scientists in Computer Science & Software Engineering, December 27, 2024, Kryvyi
Rih, Ukraine
" shokalyuk@kdpu.edu.ua (S. V. Shokaliuk); andrewkawetckiy777@gmail.com (A. O. Kavetskyi)
~ https://kdpu.edu.ua/personal/svshokaliuk.html (S. V. Shokaliuk)
0000-0003-3774-1729 (S. V. Shokaliuk)
© 2025 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
58
Svitlana V. Shokaliuk et al. CEUR Workshop Proceedings 58–65
RQ2: What is the impact of the proposed adaptive assessment tool on students’ problem-solving
skills, attitudes towards mathematics, and overall academic achievement compared to traditional
assessment methods? Can personalized feedback and recommendations lead to measurable
learning gains?
RQ3: How can the combination of Python and CustomTkinter be leveraged to create an engaging and
intuitive user interface for the assessment tool? What are the key design considerations and
implementation techniques for building an effective educational technology application?
The rest of the paper is structured as follows: section 2 reviews related work on computer-based
math assessment and adaptive learning technologies. Section 3 describes the system design and
implementation, detailing the software architecture, key algorithms, and user interface design. Section 4
presents the evaluation methodology, including the research design, data collection procedures, and
analysis methods. Section 5 reports and discusses the main results, covering system performance,
effectiveness of adaptive feedback, and impact on student learning and attitudes. Finally, section 6
concludes the paper, summarizing the key findings, limitations, and directions for future work.
2. Related work
Numerous studies have explored the development and implementation of mathematics assessment tools
for grade 6 students. Karagiannakis and Noël [15] proposed an online assessment system called the
Mathematical Profile Test (MathPro Test) that evaluates a wide range of numerical skills in primary
school children. The tool includes 18 subtests covering core number domains, visual-spatial abilities,
memory, and reasoning. The authors found that the MathPro Test exhibited satisfactory internal
consistency and correlated significantly with standardized mathematics achievement tests across all
grades.
Similarly, Dietrichson et al. [16] conducted a systematic review of targeted school-based interventions
for improving reading and mathematics performance in grades K-6. The meta-analysis revealed that
peer-assisted instruction and small-group instruction by adults were the most effective strategies
for enhancing students’ mathematical skills. The authors emphasized the importance of designing
interventions that adapt to the specific needs of each age group and learner profile.
The effectiveness of computer-based assessment in mathematics education has been well-documented
in the literature. Foerster [9] integrated programming concepts into the mathematics curriculum for
grades 6 and 7 using Scratch. The study found that students who participated in the intervention showed
significant improvements in their mathematical problem-solving abilities compared to the control group.
The authors attributed this success to the interactive and engaging nature of the programming tasks,
which encouraged students to explore mathematical concepts in a more hands-on manner.
Providing adaptive feedback and scaffolding is crucial for enhancing the effectiveness of computer-
based assessment tools [17]. Van Garderen et al. [18] analyzed sixth and seventh-grade mathematics
textbooks to determine the extent to which they incorporated recommended instructional practices
for students with learning disabilities. The authors found that the textbooks provided limited explicit
information about representations and offered insufficient support for teachers to develop students’
representational abilities. These findings underscore the need for assessment tools that offer targeted
feedback and scaffolding based on individual student needs.
Despite the number of research on technology-enhanced mathematics assessment, several gaps
remain in current practice. Many existing tools focus on a narrow range of mathematical skills and lack
the adaptability required to cater to diverse learning needs [14]. Moreover, there is a need for more
longitudinal studies that investigate the long-term impact of computer-based interventions on student
achievement and attitudes towards mathematics [19].
The related work highlights the potential of computer-based assessment tools for enhancing mathe-
matics learning in grade 6 students, but there is still room for improvement in terms of providing adaptive
feedback, scaffolding, and comprehensive coverage of mathematical concepts. Our research aims to
59
Svitlana V. Shokaliuk et al. CEUR Workshop Proceedings 58–65
address these gaps by developing an interactive assessment tool using Python and CustomTkinter that
offers personalized support for students’ individual learning needs.
3. System design and implementation
The proposed mathematics assessment tool is designed to provide an interactive and personalized
learning experience for grade 6 students. The system aims to generate adaptive questions, offer
immediate feedback, and track student progress in real time.
The architecture of the mathematics assessment tool consists of three main components: the test
generator, the user interface, and the student performance tracker. Figure 1 illustrates the high-level
architecture of the system.
Test generator
Student performance tracker
User interface
Figure 1: High-level architecture of the mathematics assessment tool.
The test generator is responsible for creating adaptive questions based on the student’s performance
and the difficulty level selected. It utilizes a pool of predefined questions and a set of algorithms
to generate new questions dynamically. The user interface component handles the presentation of
questions, user input, and feedback display. It is built using Python and CustomTkinter to ensure a
visually appealing and intuitive user experience. The student performance tracker maintains a record of
the student’s responses, accuracy, and progress over time. It provides valuable insights into the student’s
strengths and weaknesses, allowing for targeted interventions and personalized recommendations.
The test generation and adaptation algorithms form the core of the assessment tool. The system
employs a combination of rule-based and probabilistic techniques to create questions that match the
student’s ability level and cover a wide range of mathematical concepts.
Algorithm 1: Adaptive question generation algorithm.
Input: Student performance data, difficulty level
Output: Adaptive question
Initialize question pool based on difficulty level;
if student performance data available then
Analyze performance data to identify areas of strength and weakness;
Select a question from the pool that targets the identified areas;
end
else
Select a question randomly from the pool;
end
Present the selected question to the student;
The adaptive question generation algorithm takes into account the student’s performance data and
the selected difficulty level. If prior performance data is available, the algorithm analyzes it to identify
areas where the student needs more practice. It then selects a question from the pool that targets those
specific areas. If no performance data is available, the algorithm selects a question randomly from the
pool. The selected question is then presented to the student for solving.
60
Svitlana V. Shokaliuk et al. CEUR Workshop Proceedings 58–65
The user interface of the mathematics assessment tool is designed to be intuitive, engaging, and
visually appealing. It incorporates elements of gamification, such as progress bars and rewards, to
motivate students and encourage active participation. Figure 2 shows a mockup of the user interface.
Figure 2: User interface mockup of the mathematics assessment tool (A/B test).
The interface includes a question display area, an input field for answers, and a feedback section
that provides immediate feedback on the student’s response. The system also incorporates hints
and explanations to guide students towards the correct solution when needed. The interface is fully
customizable, allowing students to adjust font sizes, colours, and other visual elements to suit their
preferences.
The mathematics assessment tool was developed using Python and CustomTkinter, a Python library
that creates modern and customizable graphical user interfaces. Python’s simplicity, versatility, and
extensive library support make it an ideal choice for building educational software. CustomTkinter
provides a wide range of pre-built components and styling options, enabling the creation of visually
appealing and responsive user interfaces.
Listing 1: Example code for creating a question display using CustomTkinter.
import c u s t o m t k i n t e r a s c t k
c l a s s Q u e s t i o n D i s p l a y ( c t k . CTkFrame ) :
def _ _ i n i t _ _ ( s e l f , m a s t e r , q u e s t i o n ) :
super ( ) . _ _ i n i t _ _ ( m a s t e r )
s e l f . question = question
s e l f . q u e s t i o n _ l a b e l = c t k . CTkLabel ( s e l f , t e x t = q u e s t i o n . t e x t )
s e l f . q u e s t i o n _ l a b e l . pack ( pady = 1 0 )
s e l f . a n s w e r _ e n t r y = c t k . CTkEntry ( s e l f )
s e l f . a n s w e r _ e n t r y . pack ( pady = 1 0 )
s e l f . s u b m i t _ b u t t o n = c t k . CTkButton ( s e l f , t e x t = " S u b m i t " ,
command= s e l f . c h e c k _ a n s w e r )
s e l f . s u b m i t _ b u t t o n . pack ( pady = 1 0 )
61
Svitlana V. Shokaliuk et al. CEUR Workshop Proceedings 58–65
Listing 1 demonstrates how CustomTkinter can be used to create a question display component.
The QuestionDisplay class inherits from ctk.CTkFrame and includes a label for displaying the
question text, an entry field for the student’s answer, and a submit button for checking the answer.
The check_answer method (not shown) would handle the evaluation of the student’s response and
provide appropriate feedback.
4. Evaluation methodology
A quasi-experimental research design was employed to evaluate the effectiveness of the proposed
mathematics assessment tool. The study involved two groups of grade 6 students: an experimental
group that used the adaptive assessment tool and a control group that received traditional classroom
instruction and assessment. The independent variable was the type of assessment (adaptive tool vs.
traditional), while the dependent variables included students’ problem-solving skills, attitudes towards
mathematics, and overall academic achievement.
Data were collected using various instruments and procedures. Throughout the 4-week intervention
period, students in the experimental group used the adaptive assessment tool for a minimum of 30
minutes per week during their regular mathematics classes. The tool automatically recorded students’
responses, time spent on each question, and the number of attempts made. In addition, a sample of 2
students from each group participated in semi-structured interviews to gather qualitative data on their
experiences and perceptions of the assessment process.
The collected data were analyzed using mixed methods. The log data generated by the adaptive
assessment tool were analyzed using data mining techniques, such as clustering and association
rule mining, to identify patterns in students’ problem-solving behaviours and their relationship to
performance outcomes. The semi-structured interviews were transcribed and analyzed using thematic
analysis to identify common themes and insights related to students’ experiences and perceptions.
5. Results and discussion
System performance metrics evaluated the adaptive mathematics assessment tool’s usability and fea-
sibility. The system’s average response time for question generation and feedback provision was 1.2
seconds, indicating a smooth and efficient user experience.
The effectiveness of the adaptive feedback and personalized recommendations provided by the
assessment tool was evaluated through an analysis of the log data and student interviews. The clustering
of students’ response patterns revealed three distinct problem-solving profiles: strategic, impulsive,
and reflective. The system successfully identified these profiles and provided targeted feedback and
recommendations, resulting in a 25% reduction in the average number of attempts required to solve a
question correctly.
During the interviews, students expressed appreciation for the personalized feedback and hints,
stating that they helped them understand their mistakes and improve their problem-solving strategies.
One student remarked, “The feedback made me think about my approach and try different ways to solve
the problem. It was like having a personal tutor”.
6. Conclusion
This study aimed to develop and evaluate an adaptive mathematics assessment tool for grade 6 students
using Python and CustomTkinter. The key findings of this study are as follows:
1. The adaptive assessment tool provided personalized feedback and recommendations based on
student’s individual strengths and weaknesses, leading to a reduction in the average number of
attempts required to solve problems correctly.
62
Svitlana V. Shokaliuk et al. CEUR Workshop Proceedings 58–65
2. The tool enhanced students’ attitudes towards mathematics, particularly in terms of enjoyment
and confidence, as evidenced by the significantly greater improvements in the experimental group
compared to the control group.
3. The integration of Python and CustomTkinter enabled the creation of an engaging and user-
friendly interface, with 85% of students finding the tool easy to use and 90% reporting that the
interface was motivating.
The adaptive assessment tool’s positive impact on students’ attitudes towards mathematics under-
scores the potential of technology to foster a love for learning and a growth mindset among young
learners. Ultimately, the goal of mathematics education is to empower students with the knowledge,
skills, and dispositions they need to succeed in an increasingly complex and quantitative world.
While this study’s results demonstrate the potential of the adaptive mathematics assessment tool,
several limitations should be acknowledged. First, the sample size was relatively small and limited to
a single school district, which may limit the generalizability of the findings. Future research should
replicate the study with larger and more diverse student populations.
Second, this study did not examine the long-term effects of the adaptive assessment tool on students’
mathematics performance and attitudes. Longitudinal research is needed to investigate whether the
tool’s benefits persist over time and translate into improved academic outcomes in later grades.
Finally, the current version of the assessment tool focuses primarily on grade 6 mathematical concepts.
Future developments should expand the content coverage to include higher grade levels and more
advanced mathematical topics, such as algebra and geometry.
There are a lot of directions for future work. One promising avenue is to integrate machine learning
techniques to further enhance the adaptive capabilities of the assessment tool. For example, deep
learning models could be trained on large-scale student interaction data to predict student performance
and optimize the question selection and feedback generation processes. Reinforcement learning algo-
rithms could also be explored to dynamically adjust the difficulty level and provide support based on
student responses in real-time. Moreover, natural language processing techniques could be applied
to analyze student explanations and provide more targeted feedback on problem-solving strategies
and misconceptions. Another direction for future research is to expand the tool’s content coverage
to include higher grade levels and more advanced mathematical topics, such as algebra, geometry,
and calculus. This would require the development of new question-generation algorithms and the
incorporation of domain-specific knowledge into the system. Additionally, the user interface could
be enhanced with more interactive features, such as graphing tools and simulations, to support the
exploration of complex mathematical concepts.
Declaration on Generative AI: During the preparation of this work, the authors used Claude 3 Opus in order to: Drafting
content, Text translation, Generate literature review, Grammar and spelling check, Content enhancement. After using this
service, the authors reviewed and edited the content as needed and takes full responsibility for the publication’s content.
References
[1] N. Hungi, M. Ngware, The effects of preschool participation on mathematics achievement of
Grade 6 pupils in Uganda, Educational Research for Policy and Practice 17 (2018) 105–126.
doi:10.1007/s10671-017-9218-y.
[2] S. B. Mahlambi, A. S. Mawela, Exploring grade 6 mathematics teachers’ use of the language of
learning and teaching in assessment for learning, Journal of Education (2021) 129–148. doi:10.
17159/2520-9868/i82a08.
[3] I. M. Christiansen, Y. Aungamuthu, The instability of kwazulu-natal grade 6 learners’ mathematics
multiple choice test responses, African Journal of Research in Mathematics, Science and Technology
Education 17 (2013) 162–172. doi:10.1080/10288457.2013.829598.
[4] L. Fadieieva, S. Semerikov, Exploring the Interplay of Moodle Tools and Student Learning Out-
comes: A Composite-Based Structural Equation Modelling Approach, in: E. Faure, Y. Tryus,
63
Svitlana V. Shokaliuk et al. CEUR Workshop Proceedings 58–65
T. Vartiainen, O. Danchenko, M. Bondarenko, C. Bazilo, G. Zaspa (Eds.), Information Tech-
nology for Education, Science, and Technics, volume 222 of Lecture Notes on Data Engineer-
ing and Communications Technologies, Springer Nature Switzerland, Cham, 2024, pp. 418–435.
doi:10.1007/978-3-031-71804-5_28.
[5] J. A. Ross, A. Hogaboam-Gray, C. Rolheiser, Student self-evaluation in grade 5-6 mathematics
effects on problem- solving achievement, International Journal of Phytoremediation 21 (2002)
43–58. doi:10.1207/S15326977EA0801_03.
[6] M. J. Gierl, J. Bisanz, Anxieties and attitudes related to mathematics in grades 3 and 6, Journal of
Experimental Education 63 (1995) 139–158. doi:10.1080/00220973.1995.9943818.
[7] C.-H. Shang, E.-S. Lin, C.-Y. Juan, The discussion information computer technology integrates
fitness of 6-7th grade mathematics teaching, in: 2011 International Conference on Electrical and
Control Engineering, ICECE 2011 - Proceedings, 2011, pp. 6884–6888. doi:10.1109/ICECENG.
2011.6056766.
[8] A. Baki, S. Ö. Bütüner, The ways of using the history of mathematics in 6th, 7th and 8th grade
mathematics textbooks; [6-7 ve 8. sınıf matematik ders kitaplarında matematik tarihinin kullanım
şekilleri], Elementary Education Online 12 (2013) 849–872.
[9] K.-T. Foerster, Integrating Programming into the Mathematics Curriculum: Combining Scratch
and Geometry in Grades 6 and 7, in: Proceedings of the 17th Annual Conference on Information
Technology Education, SIGITE ’16, Association for Computing Machinery, New York, NY, USA,
2016, p. 91–96. URL: https://doi.org/10.1145/2978192.2978222. doi:10.1145/2978192.2978222.
[10] E. A. Graf, Defining mathematics competency in the service of cognitively based assessment for
grades 6 through 8, ETS Research Report Series 2009 (2009) i–46. doi:10.1002/j.2333-8504.
2009.tb02199.x.
[11] C.-Y. Juan, Demand and fitness of resource analysis of integrating information technology into
instruction of mathematics on 6-7th grade, in: 2011 International Conference on Electrical and
Control Engineering, ICECE 2011 - Proceedings, 2011, pp. 6469–6472. doi:10.1109/ICECENG.
2011.6056854.
[12] K. Vlasenko, O. Chumak, V. Achkan, I. Lovianova, O. Kondratyeva, Personal e-learning environment
of a mathematics teacher, Universal Journal of Educational Research 8 (2020) 3527–3535. doi:10.
13189/ujer.2020.080828.
[13] S. Maoto, Creating a child friendly psychosocial learning environment in mathematics: A case
of problem solving in grade 6, Mediterranean Journal of Social Sciences 5 (2014) 1048–1055.
doi:10.5901/mjss.2014.v5n23p1048.
[14] M. Chatterji, M. Lin, Designing non-cognitive construct measures that improve mathematics
achievement in Grade 5-6 learners: A user-centered approach, Quality Assurance in Education 26
(2018) 70–100. doi:10.1108/QAE-11-2017-0081.
[15] G. Karagiannakis, M.-P. Noël, Mathematical profile test: A preliminary evaluation of an online
assessment for mathematics skills of children in grades 1-6, Behavioral Sciences 10 (2020) 126.
doi:10.3390/BS10080126.
[16] J. Dietrichson, T. Filges, J. K. Seerup, R. H. Klokker, B. C. A. Viinholt, M. Bøg, M. Eiberg, Targeted
school-based interventions for improving reading and mathematics for students with or at risk of
academic difficulties in Grades K-6: A systematic review, Campbell Systematic Reviews 17 (2021)
e1152. doi:10.1002/cl2.1152.
[17] A. Kostikov, K. Vlasenko, I. Lovianova, S. Volkov, D. Kovalova, M. Zhuravlov, Assessment of
Test Items Quality and Adaptive Testing on the Rasch Model, in: V. Ermolayev, D. Esteban,
V. Yakovyna, H. C. Mayr, G. Zholtkevych, M. Nikitchenko, A. Spivakovsky (Eds.), Information and
Communication Technologies in Education, Research, and Industrial Applications, volume 1698 of
Communications in Computer and Information Science, Springer International Publishing, Cham,
2022, pp. 252–271. doi:10.1007/978-3-031-20834-8_12.
[18] D. Van Garderen, A. Scheuermann, C. Jackson, Developing representational ability in mathematics
for students with learning disabilities: A content analysis of grades 6 and 7 textbooks, Learning
Disability Quarterly 35 (2012) 24–38. doi:10.1177/0731948711429726.
64
Svitlana V. Shokaliuk et al. CEUR Workshop Proceedings 58–65
[19] M. Turnbull, D. Hart, S. Lapkin, Grade 6 french immersion students’ performance on large-scale
reading, writing, and mathematics tests: Building explanations, Alberta Journal of Educational
Research 49 (2003) 6–23. doi:10.55016/ojs/ajer.v49i1.54956.
65