The document presents findings from a study analyzing the relationship between students' creative skills and learning performance. Researchers conducted an online survey of 32 students to collect data on their self-assessed creativity abilities and learning performance during coursework. Several machine learning algorithms were used to build classification models relating creativity and performance. The models identified predictors like frequency of new ideas, working pace, and willingness to improve work. Results indicated creative students had better performance, and AdaBoost.M1 produced more accurate models than J48 and RandomTree algorithms. The study concludes there is a relationship between creativity and optimized learning.

1. Analysis of Relationship between Students’ Creative Skill
and Learning Performance
Malinka Ivanova and Tsvetelina Petrova
Technical University of Sofia, Sofia, Bulgaria
10th International Conference in Methodologies and Intelligent Systems
for Technology Enhanced Learning
7th-9th October, 2020
L'Aquila, Italy

2. Acknowledgements
The authors would like to thank the Research and
Development Sector at the Technical University of
Sofia for the financial support
ANALYSIS OF RELATIONSHIP BETWEEN STUDENTS’ CREATIVE
SKILL AND LEARNING PERFORMANCE
10th International Conference in Methodologies and Intelligent Systems
for Technology Enhanced Learning
7th-9th October, 2020
L'Aquila, Italy

3. ANALYSIS OF RELATIONSHIP BETWEEN STUDENTS’ CREATIVE SKILL AND LEARNING PERFORMANCE
Introduction
10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, 7th-9th October, 2020, L'Aquila, Italy
Creativity
- ability for generating of
original ideas or mixing
several ideas to create new
products
Students`
learning
performance
How they organize their
learning to reach the
learning goals
What learning activities
are taken for achieving
successful learning
outcomes

4. To present the findings related to the connection among
personal characteristics, creative ability and learning performance
of students during their course work preparation.
ANALYSIS OF RELATIONSHIP BETWEEN STUDENTS’ CREATIVE SKILL AND LEARNING PERFORMANCE
Objective
10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, 7th-9th October, 2020, L'Aquila, Italy

5. ANALYSIS OF RELATIONSHIP BETWEEN STUDENTS’ CREATIVE SKILL AND LEARNING PERFORMANCE
Classification algorithms for
construction the decision-
making models
Weka environment
Methodology
Online survey about
students` learning
performance at course
works preparation and
to self-evaluate their
creative abilities
C4.5 (named also J48) - for construction
pruned classification trees
AdaBoost.M1 - for creation of
boosting trees
RandomTree - for producing a set of trees as
each tree is constructed through a random
subset of variables
Collecting the data Processing the data
10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, 7th-9th October, 2020, L'Aquila, Italy

6. ANALYSIS OF RELATIONSHIP BETWEEN STUDENTS’ CREATIVE SKILL AND LEARNING PERFORMANCE
Methodology
32 students from Technical University of Sofia, College
of Energy and Electronics, were asked to share their
opinion in an online survey about their learning
performance at course works preparation and to self-
evaluate their creative abilities
for better understanding whether the
students are creative persons
related to personal data –
gender and ages
regarding the connection between
creativity at course work
preparation and learning
performance
Collecting the data. Participants
The online survey
Groups Ages Number of
students
1 21-25 9
2 26-30 6
3 31-35 3
4 36-40 9
5 41-45 4
6 46-50 1
72% of the surveyed
students are male and 28%
of them are female.
10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, 7th-9th October, 2020, L'Aquila, Italy

7. The first model - whether the students possess creative skill
ANALYSIS OF RELATIONSHIP BETWEEN STUDENTS’ CREATIVE SKILL AND LEARNING PERFORMANCE
Results
C4.5 (J48)
how often the idea is
realized in practice
whether the students like
to make experiments
RandomThree
frequency of new ideas
coming to students
how often the students
combine existing ideas to
create something new
whether the students like
to experiment
how often the new idea
is realized in practice
AdaBoost.M1
RandomTree
frequency of new ideas
coming to students
how often the students
combine existing ideas to
create something new
10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, 7th-9th October, 2020, L'Aquila, Italy

8. ANALYSIS OF RELATIONSHIP BETWEEN STUDENTS’ CREATIVE SKILL AND LEARNING PERFORMANCE
The second model is about the students’ learning performance during their
course work preparation
Results
Algorithm used Predictors for learning performance
C4.5 (J48) not identified
RandomTree • the students create course works according to their first idea
• the temp at course work preparation
• the students willing for course work improvement if they have
this possibility
AdaBoost.M1 the temp at course work preparation
10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, 7th-9th October, 2020, L'Aquila, Italy

9. ANALYSIS OF RELATIONSHIP BETWEEN STUDENTS’ CREATIVE SKILL AND LEARNING PERFORMANCE
Results
Third classification model - for identifying the relationship between creativity skill and
learning performance
IF the student’s learning performance is excellent
THEN he/she is a creative person.
IF the student’s learning performance is very
good AND working pace is average AND he/she is
willing to improve his/her course work if he/she
has this possibility THEN the student is a creative
person.
10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, 7th-9th October, 2020, L'Aquila, Italy

10. ANALYSIS OF RELATIONSHIP BETWEEN STUDENTS’ CREATIVE SKILL AND LEARNING PERFORMANCE
Results
Third classification model - for identifying the relationship between creativity skill and learning performance
the course works are created
according to students’ first ideaRandomTree
algorithm
the working pace at course work
preparation
the factors that influence on the
course work quality
how often the new ideas are
realized in practice
AdaBoost.M1
algorithm
the ability for a combination of
existing ideas to create
something new
10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, 7th-9th October, 2020, L'Aquila, Italy

11. ANALYSIS OF RELATIONSHIP BETWEEN STUDENTS’ CREATIVE SKILL AND LEARNING PERFORMANCE
Results Forth model - the influence of students’ personal characteristics on creativity
RandomTree algorithm
The root node is how often the students come up with new ideas that it splits to four nodes:
 creative people;
 realization on practice the emerged new ideas;
 ability to combine ideas to receive something new;
 like experimenting.
10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, 7th-9th October, 2020, L'Aquila, Italy

12. Algorithm Group Results/typical that
RandomTree 2 (26-30 years) very often the new ideas are coming and they are creative
persons and they often/very often combine ideas to obtain
something new
1 (21-25 years) very often the new ideas emerge and they are creative
persons
4 (36-40 years) often the new ideas emerge and they rarely combine ideas
to receive something new, but often realize ideas on practice
ANALYSIS OF RELATIONSHIP BETWEEN STUDENTS’ CREATIVE SKILL AND LEARNING PERFORMANCE
Forth model - the influence of students’ personal characteristics on creativityResults
Algorithm Group
C4.5 (J48) 2 (26-30 years) very often have new ideas
1 (21-25 years) often come new ideas and they often combine several ideas
to prepare something new
4 (36-40 years) often realize the ideas on practice
3 (31-35 years)
5 (41-45 years)
not so often emerged new ideas, but they like to make
experiments
AdaBoost.M1 3 (31-35 years)
5 (41-45 years)
not so often come new ideas
10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, 7th-9th October, 2020, L'Aquila, Italy

13. ANALYSIS OF RELATIONSHIP BETWEEN STUDENTS’ CREATIVE SKILL AND LEARNING PERFORMANCE
Fifth model - the relationship between students’
personal characteristics and learning
performance
Results
Algorithm
used
Predictors for learning performance
C4.5 (J48) the number of factors that influence on
course work quality
RandomTree working temp
AdaBoost.M1 the number of factors that influence on
the product quality
Algorithm Group Number of factors
influenced on
work quality
C4.5 (J48) 4 (36-40 years) 1 or 3
3 (31-35 years) 5 + temp of working
5 (41-45 years) 6
1 (21-25 years) 2 ÷ 5
2 (26-30 years) 2 ÷ 5
AdaBoost.M1 4 (36-40 years) 1
10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, 7th-9th October, 2020, L'Aquila, Italy

14. ANALYSIS OF RELATIONSHIP BETWEEN STUDENTS’ CREATIVE SKILL AND LEARNING PERFORMANCE
Sixth model - the relationship between students’ personal characteristics (age and
gender), creative skill and learning performance
Results
Algorithm
used
Main node
C4.5 (J48) the frequency for emerging new ideas
RandomTree whether the student is a creative person
AdaBoost.M1 the frequency for emerging new ideas
 the students’ age play somewhat role in creativity and
learning performance
 the gender does not have influence neither on the
creativity, nor on the learning performance
10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, 7th-9th October, 2020, L'Aquila, Italy

15. Weka KnowledgeFlow environment
Models performance comparison
ANALYSIS OF RELATIONSHIP BETWEEN STUDENTS’ CREATIVE SKILL AND LEARNING PERFORMANCE
Parameter J48 RandomTree AdaBoost.M1
Percentage_correct 37.5% 25% 46.875%
Kappa_statistic 0.0643 -0.1179 0.0701
True Positive Rate 0.375 0.250 0.469
False Positive Rate 0.318 0.390 0.382
Precision 0.402 0.260 0.251
Recall 0.375 0.250 0.469
F-Measure 0.382 0.255 0.327
MCC 0.069 -0.137 0.116
ROC Area 0.558 0.477 0.566
PRC Area 0.417 0.334 0.372
Results
10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, 7th-9th October, 2020, L'Aquila, Italy

16. The comparison of visualized ROC areas and PRC areas of the examined classifiers for
one class excellent of the attribute selfassessmnt
ANALYSIS OF RELATIONSHIP BETWEEN STUDENTS’ CREATIVE SKILL AND LEARNING PERFORMANCE
Results
ROC areas PRC areas
10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, 7th-9th October, 2020, L'Aquila, Italy

17. The present exploration proves the mutual relationship between
creative ability and learning performance:
• Creative persons are characterized with optimized learning
performance.
• The learning performance of students who self-described as
non-creative persons is just good.
Three machine learning algorithms are applied on “small data” for
creation several classification models and their performance
comparison shows that the AdaBoost.M1 parameters are better than
J48 and RandomTree classifiers.
ANALYSIS OF RELATIONSHIP BETWEEN STUDENTS’ CREATIVE SKILL AND LEARNING PERFORMANCE
Conclusions
10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, 7th-9th October, 2020, L'Aquila, Italy

18. Obtained predictors for explaining the mutual relationship between
creative ability and learning performance:
ANALYSIS OF RELATIONSHIP BETWEEN STUDENTS’ CREATIVE SKILL AND LEARNING PERFORMANCE
Conclusions
Creative
ability
Learning
performance
the final product is created according to
students’ first idea
the importance of product quality factors
the frequency of realization in practice the
new ideas
working temp and the ability for combination
existing ideas to create something new
10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, 7th-9th October, 2020, L'Aquila, Italy

19. ANALYSIS OF RELATIONSHIP BETWEEN STUDENTS’ CREATIVE SKILL AND LEARNING PERFORMANCE
10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, 7th-9th October, 2020, L'Aquila, Italy