EASEAI '22: "Findings from Teaching Entrepreneurship ..."
Findings from Teaching Entrepreneurship to Undergraduate Multidisciplinary Students: Case Study
Manuela Andreea Petrescu, Diana Laura Borza, and Dan Mircea Suciu
(Babeș-Bolyai University, Romania)
Innovation and entrepreneurship undeniably contribute to the progress of contemporary economies, and therefore an increasing number of universities are incorporating entrepreneurship-related courses into their curricula. The present study presents an innovative experiment for teaching an introductory entrepreneurship course, Fundamentals of Entrepreneurship, to students from different faculties, with different mindsets, needs, ideas, and hobbies. Following the establishment of the curriculum by the University, each lecture was delivered by a different invited speaker, highly skilled on the topic. The course aimed to encourage entrepreneurship, with a focus on digitisation, but also to foster the integration of people with different backgrounds and knowledge and to cultivate critical thinking. At the end of the course, we analyzed students’ opinions regarding the course format, the level of acquired knowledge and if they managed to find relevant information. We used quantitative and qualitative methods to interpret and analyse the collected data. The results correlated and reflected the same outcome: overall, the students appreciated the course format and the provided information.
@InProceedings{EASEAI22p25,
author = {Manuela Andreea Petrescu and Diana Laura Borza and Dan Mircea Suciu},
title = {Findings from Teaching Entrepreneurship to Undergraduate Multidisciplinary Students: Case Study},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {25--32},
doi = {10.1145/3548660.3561333},
year = {2022},
}
Publisher's Version
EASEAI '22: "Evidence-Based Practices: ..."
Evidence-Based Practices: Broadening Participation and Improving Learning in CS (Invited Talk)
Maureen Doyle, Alina Campan, and Meghan Schmidt
(Northern Kentucky University, USA)
In the US and other countries, women and people of color have been underrepresented in computing majors for more than twenty years. Given this trend and research showing that diverse teams are more successful, in 2017, Northern Kentucky University's Department of Computer Science began implementing multiple evidence-based practices to address these concerns. New programs and practices were selected based on demonstrated improvements in student success and increased diversity of majors. The changes fell into two broad categories: (1) Curriculum/Program and (2) Student Support. New initiatives included new introductory interactive textbooks and platforms, implementation of a peer teaching assistant program, and added requirements for a freshmen seminar. In addition, the department was awarded an NCWIT Extension Services grant to support faculty training in inclusive teaching and classroom pedagogy. The success of these programs is measured by the increase seen in majors and minors, as well as improvements in student retention and a more diverse set of computing majors. We will discuss a subset of the programs, their implementation, retention and demographic enrollment results, and future work.
@InProceedings{EASEAI22p1,
author = {Maureen Doyle and Alina Campan and Meghan Schmidt},
title = {Evidence-Based Practices: Broadening Participation and Improving Learning in CS (Invited Talk)},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {1--1},
doi = {10.1145/3548660.3570208},
year = {2022},
}
Publisher's Version
EASEAI '22: "A Pedagogical Approach in ..."
A Pedagogical Approach in Interleaving Software Quality Concerns at an Artificial Intelligence Course
Laura Diana Cernau, Laura Silvia Dioşan, and Camelia Șerban
(Babeș-Bolyai University, Romania)
The software engineering industry is an everchanging domain requiring professionals to have a good knowledge base and adaptability skills.Artificial Intelligence (AI) has achieved substantial success in enhancing program analysis techniques and applications, including bug prediction. It is a promising direction by applying advanced Machine Learning techniques into suitable software engineering tasks.
The main goal of this paper is to propose a pedagogical interdisciplinary approach that pave the path for developing an e-learning platform serving to check the quality of the source code that students wrote by means of Artificial Intelligence techniques. By putting into practice this proposal, we are planning to show the students how to combine concepts learned from two different courses. The first step of this approach would be part of the Advanced Programming Methods, a Software Engineering related course, where students learn about the importance of writing good quality code and use software metrics as a mean of software quality assessment. Then, the following steps will be integrated into the Artificial Intelligence course, where students learn about different Machine Learning algorithms and how to apply them to solve practical problems. Thus, as an applicability in this respect, students use the metric values calculated for their projects developed at Advanced Programming Methods course as lab assignments and also to train (at Artificial Intelligence class) a bug detection model able to estimate the quality of new codebases.
The proposed approach is helpful for both students and teachers. On one side, it helps the students understand the importance of writing clean, high-quality code. And on the other side, it helps teachers in their evaluation process by giving them time to focus on different aspects of homework than the code quality.
@InProceedings{EASEAI22p18,
author = {Laura Diana Cernau and Laura Silvia Dioşan and Camelia Șerban},
title = {A Pedagogical Approach in Interleaving Software Quality Concerns at an Artificial Intelligence Course},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {18--24},
doi = {10.1145/3548660.3561332},
year = {2022},
}
Publisher's Version
EASEAI '22: "Towards Automated Testing ..."
Towards Automated Testing for Simple Programming Exercises
Pierre Ortegat, Benoît Vanderose, and Xavier Devroey
(University of Namur, Belgium)
Automated feedback and grading platforms can require substantial effort when encoding new programming exercises for first-year students. Such exercises are usually simple but require defining several test cases to ensure their functional correctness. This paper describes our initial effort to leverage automated test case generation for simple programming exercises. We rely on grey-box fuzzing and random combinations of method calls to test the students' solutions and compare their execution to the results produced by a reference implementation. We implemented our approach in a prototype, called SimPyTest, openly available on GitHub. We discuss its usage and possible future extensions.
@InProceedings{EASEAI22p33,
author = {Pierre Ortegat and Benoît Vanderose and Xavier Devroey},
title = {Towards Automated Testing for Simple Programming Exercises},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {33--36},
doi = {10.1145/3548660.3561334},
year = {2022},
}
Publisher's Version
EASEAI '22: "A Pedagogical Approach in ..."
A Pedagogical Approach in Interleaving Software Quality Concerns at an Artificial Intelligence Course
Laura Diana Cernau, Laura Silvia Dioşan, and Camelia Șerban
(Babeș-Bolyai University, Romania)
The software engineering industry is an everchanging domain requiring professionals to have a good knowledge base and adaptability skills.Artificial Intelligence (AI) has achieved substantial success in enhancing program analysis techniques and applications, including bug prediction. It is a promising direction by applying advanced Machine Learning techniques into suitable software engineering tasks.
The main goal of this paper is to propose a pedagogical interdisciplinary approach that pave the path for developing an e-learning platform serving to check the quality of the source code that students wrote by means of Artificial Intelligence techniques. By putting into practice this proposal, we are planning to show the students how to combine concepts learned from two different courses. The first step of this approach would be part of the Advanced Programming Methods, a Software Engineering related course, where students learn about the importance of writing good quality code and use software metrics as a mean of software quality assessment. Then, the following steps will be integrated into the Artificial Intelligence course, where students learn about different Machine Learning algorithms and how to apply them to solve practical problems. Thus, as an applicability in this respect, students use the metric values calculated for their projects developed at Advanced Programming Methods course as lab assignments and also to train (at Artificial Intelligence class) a bug detection model able to estimate the quality of new codebases.
The proposed approach is helpful for both students and teachers. On one side, it helps the students understand the importance of writing clean, high-quality code. And on the other side, it helps teachers in their evaluation process by giving them time to focus on different aspects of homework than the code quality.
@InProceedings{EASEAI22p18,
author = {Laura Diana Cernau and Laura Silvia Dioşan and Camelia Șerban},
title = {A Pedagogical Approach in Interleaving Software Quality Concerns at an Artificial Intelligence Course},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {18--24},
doi = {10.1145/3548660.3561332},
year = {2022},
}
Publisher's Version
EASEAI '22: "Evidence-Based Practices: ..."
Evidence-Based Practices: Broadening Participation and Improving Learning in CS (Invited Talk)
Maureen Doyle, Alina Campan, and Meghan Schmidt
(Northern Kentucky University, USA)
In the US and other countries, women and people of color have been underrepresented in computing majors for more than twenty years. Given this trend and research showing that diverse teams are more successful, in 2017, Northern Kentucky University's Department of Computer Science began implementing multiple evidence-based practices to address these concerns. New programs and practices were selected based on demonstrated improvements in student success and increased diversity of majors. The changes fell into two broad categories: (1) Curriculum/Program and (2) Student Support. New initiatives included new introductory interactive textbooks and platforms, implementation of a peer teaching assistant program, and added requirements for a freshmen seminar. In addition, the department was awarded an NCWIT Extension Services grant to support faculty training in inclusive teaching and classroom pedagogy. The success of these programs is measured by the increase seen in majors and minors, as well as improvements in student retention and a more diverse set of computing majors. We will discuss a subset of the programs, their implementation, retention and demographic enrollment results, and future work.
@InProceedings{EASEAI22p1,
author = {Maureen Doyle and Alina Campan and Meghan Schmidt},
title = {Evidence-Based Practices: Broadening Participation and Improving Learning in CS (Invited Talk)},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {1--1},
doi = {10.1145/3548660.3570208},
year = {2022},
}
Publisher's Version
EASEAI '22: "Student Misconceptions about ..."
Student Misconceptions about Finite State Machines: Identify Them in Order to Create a Concept Inventory
Julie Henry, Bruno Dumas, Andreea Vescan, and Alexandra Maria Pasca
(University of Namur, Belgium; Babeș-Bolyai University, Romania)
A concept inventory (CI) is a standardized assessment tool designed to evaluate a student's understanding of the fundamental concepts of a topic. To create a CI, it is necessary to accurately identify these concepts, but also to identify how poorly students understand them. The aim of this paper is to present an approach used to identify misconceptions related to the concept of Finite State Machine (FSM). In the learning process, identifying the students' misconceptions, i.e., when they appear and how to efficiently correct them, are important aspects of the best learning outcome. Rather than measuring understanding at a specific point in the learning timeline, the CI can be administered to students several times over the course of the learning period to measure how students' understanding of concepts changes. This preliminary study is composed of two main steps. In the first step, four misconceptions were identified about FSM based on multi-year observations and teacher experiences. From these misconceptions, seven statements about FSM are specified. In the second step, a Likert scale questionnaire (composed of seven statements) was administered five times to students according to a specific schedule, allowing to measure the evolution of FSM understanding. A pre-questionnaire is used to determine the students' misconceptions about the FSM concept, based on their learning (self-learning or from previous courses). This measure, which is the starting point of this preliminary study, makes it possible to highlight the changes in the students' positioning in relation to the statements provided and to link these changes to the teaching interventions. Thus, changes are clearly observable after the two theoretical classes, and stabilization is devoted after the delivery of the lab work.
@InProceedings{EASEAI22p2,
author = {Julie Henry and Bruno Dumas and Andreea Vescan and Alexandra Maria Pasca},
title = {Student Misconceptions about Finite State Machines: Identify Them in Order to Create a Concept Inventory},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {2--9},
doi = {10.1145/3548660.3561330},
year = {2022},
}
Publisher's Version
EASEAI '22: "Student Misconceptions about ..."
Student Misconceptions about Finite State Machines: Identify Them in Order to Create a Concept Inventory
Julie Henry, Bruno Dumas, Andreea Vescan, and Alexandra Maria Pasca
(University of Namur, Belgium; Babeș-Bolyai University, Romania)
A concept inventory (CI) is a standardized assessment tool designed to evaluate a student's understanding of the fundamental concepts of a topic. To create a CI, it is necessary to accurately identify these concepts, but also to identify how poorly students understand them. The aim of this paper is to present an approach used to identify misconceptions related to the concept of Finite State Machine (FSM). In the learning process, identifying the students' misconceptions, i.e., when they appear and how to efficiently correct them, are important aspects of the best learning outcome. Rather than measuring understanding at a specific point in the learning timeline, the CI can be administered to students several times over the course of the learning period to measure how students' understanding of concepts changes. This preliminary study is composed of two main steps. In the first step, four misconceptions were identified about FSM based on multi-year observations and teacher experiences. From these misconceptions, seven statements about FSM are specified. In the second step, a Likert scale questionnaire (composed of seven statements) was administered five times to students according to a specific schedule, allowing to measure the evolution of FSM understanding. A pre-questionnaire is used to determine the students' misconceptions about the FSM concept, based on their learning (self-learning or from previous courses). This measure, which is the starting point of this preliminary study, makes it possible to highlight the changes in the students' positioning in relation to the statements provided and to link these changes to the teaching interventions. Thus, changes are clearly observable after the two theoretical classes, and stabilization is devoted after the delivery of the lab work.
@InProceedings{EASEAI22p2,
author = {Julie Henry and Bruno Dumas and Andreea Vescan and Alexandra Maria Pasca},
title = {Student Misconceptions about Finite State Machines: Identify Them in Order to Create a Concept Inventory},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {2--9},
doi = {10.1145/3548660.3561330},
year = {2022},
}
Publisher's Version
EASEAI '22: "Mining Sorting Concept across ..."
Mining Sorting Concept across Curriculum Levels: A Cyclic Learning Based Approach
Mariana Maier, Camelia Șerban, and Andrei Moisin
(Babeș-Bolyai University, Romania)
Nowadays, when the changes that appear in programming paradigms and in software process development methodologies are extremely frequent, teaching Computer Science throughout all levels of study has become a demanding task. To all these are added changes caused by the dynamics of the society and the traits of the current learners and how they learn. A new era of education has just begun.
To cope with the challenges mentioned above, teachers from three levels of study had been invited to share their experience in teaching sorting algorithms, through questionnaires, in order to achieve learning efficiency for students. Thus, the paper proposes a framework for a unitary approach of teaching sorting algorithms. Its contribution is twofold: firstly, it frames a pedagogical approach defined as a conceptual framework in teaching sorting algorithms by mining and investigating contents and aspects taught at three curriculum levels, following the Revised Bloom’s Taxonomy. Secondly, a software tool is proposed - AlSort - based on gamification and storytelling as a learning strategy of sorting algorithms. The tool is implemented for the gymnasium and high school level and it is under development for the university. It covers the first three levels from Bloom’s Taxonomy.
@InProceedings{EASEAI22p10,
author = {Mariana Maier and Camelia Șerban and Andrei Moisin},
title = {Mining Sorting Concept across Curriculum Levels: A Cyclic Learning Based Approach},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {10--17},
doi = {10.1145/3548660.3561331},
year = {2022},
}
Publisher's Version
EASEAI '22: "Mining Sorting Concept across ..."
Mining Sorting Concept across Curriculum Levels: A Cyclic Learning Based Approach
Mariana Maier, Camelia Șerban, and Andrei Moisin
(Babeș-Bolyai University, Romania)
Nowadays, when the changes that appear in programming paradigms and in software process development methodologies are extremely frequent, teaching Computer Science throughout all levels of study has become a demanding task. To all these are added changes caused by the dynamics of the society and the traits of the current learners and how they learn. A new era of education has just begun.
To cope with the challenges mentioned above, teachers from three levels of study had been invited to share their experience in teaching sorting algorithms, through questionnaires, in order to achieve learning efficiency for students. Thus, the paper proposes a framework for a unitary approach of teaching sorting algorithms. Its contribution is twofold: firstly, it frames a pedagogical approach defined as a conceptual framework in teaching sorting algorithms by mining and investigating contents and aspects taught at three curriculum levels, following the Revised Bloom’s Taxonomy. Secondly, a software tool is proposed - AlSort - based on gamification and storytelling as a learning strategy of sorting algorithms. The tool is implemented for the gymnasium and high school level and it is under development for the university. It covers the first three levels from Bloom’s Taxonomy.
@InProceedings{EASEAI22p10,
author = {Mariana Maier and Camelia Șerban and Andrei Moisin},
title = {Mining Sorting Concept across Curriculum Levels: A Cyclic Learning Based Approach},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {10--17},
doi = {10.1145/3548660.3561331},
year = {2022},
}
Publisher's Version
EASEAI '22: "Towards Automated Testing ..."
Towards Automated Testing for Simple Programming Exercises
Pierre Ortegat, Benoît Vanderose, and Xavier Devroey
(University of Namur, Belgium)
Automated feedback and grading platforms can require substantial effort when encoding new programming exercises for first-year students. Such exercises are usually simple but require defining several test cases to ensure their functional correctness. This paper describes our initial effort to leverage automated test case generation for simple programming exercises. We rely on grey-box fuzzing and random combinations of method calls to test the students' solutions and compare their execution to the results produced by a reference implementation. We implemented our approach in a prototype, called SimPyTest, openly available on GitHub. We discuss its usage and possible future extensions.
@InProceedings{EASEAI22p33,
author = {Pierre Ortegat and Benoît Vanderose and Xavier Devroey},
title = {Towards Automated Testing for Simple Programming Exercises},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {33--36},
doi = {10.1145/3548660.3561334},
year = {2022},
}
Publisher's Version
EASEAI '22: "Student Misconceptions about ..."
Student Misconceptions about Finite State Machines: Identify Them in Order to Create a Concept Inventory
Julie Henry, Bruno Dumas, Andreea Vescan, and Alexandra Maria Pasca
(University of Namur, Belgium; Babeș-Bolyai University, Romania)
A concept inventory (CI) is a standardized assessment tool designed to evaluate a student's understanding of the fundamental concepts of a topic. To create a CI, it is necessary to accurately identify these concepts, but also to identify how poorly students understand them. The aim of this paper is to present an approach used to identify misconceptions related to the concept of Finite State Machine (FSM). In the learning process, identifying the students' misconceptions, i.e., when they appear and how to efficiently correct them, are important aspects of the best learning outcome. Rather than measuring understanding at a specific point in the learning timeline, the CI can be administered to students several times over the course of the learning period to measure how students' understanding of concepts changes. This preliminary study is composed of two main steps. In the first step, four misconceptions were identified about FSM based on multi-year observations and teacher experiences. From these misconceptions, seven statements about FSM are specified. In the second step, a Likert scale questionnaire (composed of seven statements) was administered five times to students according to a specific schedule, allowing to measure the evolution of FSM understanding. A pre-questionnaire is used to determine the students' misconceptions about the FSM concept, based on their learning (self-learning or from previous courses). This measure, which is the starting point of this preliminary study, makes it possible to highlight the changes in the students' positioning in relation to the statements provided and to link these changes to the teaching interventions. Thus, changes are clearly observable after the two theoretical classes, and stabilization is devoted after the delivery of the lab work.
@InProceedings{EASEAI22p2,
author = {Julie Henry and Bruno Dumas and Andreea Vescan and Alexandra Maria Pasca},
title = {Student Misconceptions about Finite State Machines: Identify Them in Order to Create a Concept Inventory},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {2--9},
doi = {10.1145/3548660.3561330},
year = {2022},
}
Publisher's Version
EASEAI '22: "Findings from Teaching Entrepreneurship ..."
Findings from Teaching Entrepreneurship to Undergraduate Multidisciplinary Students: Case Study
Manuela Andreea Petrescu, Diana Laura Borza, and Dan Mircea Suciu
(Babeș-Bolyai University, Romania)
Innovation and entrepreneurship undeniably contribute to the progress of contemporary economies, and therefore an increasing number of universities are incorporating entrepreneurship-related courses into their curricula. The present study presents an innovative experiment for teaching an introductory entrepreneurship course, Fundamentals of Entrepreneurship, to students from different faculties, with different mindsets, needs, ideas, and hobbies. Following the establishment of the curriculum by the University, each lecture was delivered by a different invited speaker, highly skilled on the topic. The course aimed to encourage entrepreneurship, with a focus on digitisation, but also to foster the integration of people with different backgrounds and knowledge and to cultivate critical thinking. At the end of the course, we analyzed students’ opinions regarding the course format, the level of acquired knowledge and if they managed to find relevant information. We used quantitative and qualitative methods to interpret and analyse the collected data. The results correlated and reflected the same outcome: overall, the students appreciated the course format and the provided information.
@InProceedings{EASEAI22p25,
author = {Manuela Andreea Petrescu and Diana Laura Borza and Dan Mircea Suciu},
title = {Findings from Teaching Entrepreneurship to Undergraduate Multidisciplinary Students: Case Study},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {25--32},
doi = {10.1145/3548660.3561333},
year = {2022},
}
Publisher's Version
EASEAI '22: "Evidence-Based Practices: ..."
Evidence-Based Practices: Broadening Participation and Improving Learning in CS (Invited Talk)
Maureen Doyle, Alina Campan, and Meghan Schmidt
(Northern Kentucky University, USA)
In the US and other countries, women and people of color have been underrepresented in computing majors for more than twenty years. Given this trend and research showing that diverse teams are more successful, in 2017, Northern Kentucky University's Department of Computer Science began implementing multiple evidence-based practices to address these concerns. New programs and practices were selected based on demonstrated improvements in student success and increased diversity of majors. The changes fell into two broad categories: (1) Curriculum/Program and (2) Student Support. New initiatives included new introductory interactive textbooks and platforms, implementation of a peer teaching assistant program, and added requirements for a freshmen seminar. In addition, the department was awarded an NCWIT Extension Services grant to support faculty training in inclusive teaching and classroom pedagogy. The success of these programs is measured by the increase seen in majors and minors, as well as improvements in student retention and a more diverse set of computing majors. We will discuss a subset of the programs, their implementation, retention and demographic enrollment results, and future work.
@InProceedings{EASEAI22p1,
author = {Maureen Doyle and Alina Campan and Meghan Schmidt},
title = {Evidence-Based Practices: Broadening Participation and Improving Learning in CS (Invited Talk)},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {1--1},
doi = {10.1145/3548660.3570208},
year = {2022},
}
Publisher's Version
EASEAI '22: "A Pedagogical Approach in ..."
A Pedagogical Approach in Interleaving Software Quality Concerns at an Artificial Intelligence Course
Laura Diana Cernau, Laura Silvia Dioşan, and Camelia Șerban
(Babeș-Bolyai University, Romania)
The software engineering industry is an everchanging domain requiring professionals to have a good knowledge base and adaptability skills.Artificial Intelligence (AI) has achieved substantial success in enhancing program analysis techniques and applications, including bug prediction. It is a promising direction by applying advanced Machine Learning techniques into suitable software engineering tasks.
The main goal of this paper is to propose a pedagogical interdisciplinary approach that pave the path for developing an e-learning platform serving to check the quality of the source code that students wrote by means of Artificial Intelligence techniques. By putting into practice this proposal, we are planning to show the students how to combine concepts learned from two different courses. The first step of this approach would be part of the Advanced Programming Methods, a Software Engineering related course, where students learn about the importance of writing good quality code and use software metrics as a mean of software quality assessment. Then, the following steps will be integrated into the Artificial Intelligence course, where students learn about different Machine Learning algorithms and how to apply them to solve practical problems. Thus, as an applicability in this respect, students use the metric values calculated for their projects developed at Advanced Programming Methods course as lab assignments and also to train (at Artificial Intelligence class) a bug detection model able to estimate the quality of new codebases.
The proposed approach is helpful for both students and teachers. On one side, it helps the students understand the importance of writing clean, high-quality code. And on the other side, it helps teachers in their evaluation process by giving them time to focus on different aspects of homework than the code quality.
@InProceedings{EASEAI22p18,
author = {Laura Diana Cernau and Laura Silvia Dioşan and Camelia Șerban},
title = {A Pedagogical Approach in Interleaving Software Quality Concerns at an Artificial Intelligence Course},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {18--24},
doi = {10.1145/3548660.3561332},
year = {2022},
}
Publisher's Version
EASEAI '22: "Mining Sorting Concept across ..."
Mining Sorting Concept across Curriculum Levels: A Cyclic Learning Based Approach
Mariana Maier, Camelia Șerban, and Andrei Moisin
(Babeș-Bolyai University, Romania)
Nowadays, when the changes that appear in programming paradigms and in software process development methodologies are extremely frequent, teaching Computer Science throughout all levels of study has become a demanding task. To all these are added changes caused by the dynamics of the society and the traits of the current learners and how they learn. A new era of education has just begun.
To cope with the challenges mentioned above, teachers from three levels of study had been invited to share their experience in teaching sorting algorithms, through questionnaires, in order to achieve learning efficiency for students. Thus, the paper proposes a framework for a unitary approach of teaching sorting algorithms. Its contribution is twofold: firstly, it frames a pedagogical approach defined as a conceptual framework in teaching sorting algorithms by mining and investigating contents and aspects taught at three curriculum levels, following the Revised Bloom’s Taxonomy. Secondly, a software tool is proposed - AlSort - based on gamification and storytelling as a learning strategy of sorting algorithms. The tool is implemented for the gymnasium and high school level and it is under development for the university. It covers the first three levels from Bloom’s Taxonomy.
@InProceedings{EASEAI22p10,
author = {Mariana Maier and Camelia Șerban and Andrei Moisin},
title = {Mining Sorting Concept across Curriculum Levels: A Cyclic Learning Based Approach},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {10--17},
doi = {10.1145/3548660.3561331},
year = {2022},
}
Publisher's Version
EASEAI '22: "Implementing Microlearning ..."
Implementing Microlearning and Gamification Techniques in Teaching Software Project Management Concepts
Dan Mircea Suciu
(Babeș-Bolyai University, Romania)
The primary objective of this research is to examine the advantages of combining microlearning with specific gamification elements in an academic setting and to gain a better understanding of how microlearning and gamification aid computer science students in comprehending and expanding their knowledge of project management-related topics.
Implementing microlearning methods and gamification-specific aspects in two of our faculty's courses increased students' engagement, performance, and retention of knowledge, according to our research. In addition, students felt more driven to participate in online course discussions.
@InProceedings{EASEAI22p37,
author = {Dan Mircea Suciu},
title = {Implementing Microlearning and Gamification Techniques in Teaching Software Project Management Concepts},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {37--44},
doi = {10.1145/3548660.3561335},
year = {2022},
}
Publisher's Version
EASEAI '22: "Findings from Teaching Entrepreneurship ..."
Findings from Teaching Entrepreneurship to Undergraduate Multidisciplinary Students: Case Study
Manuela Andreea Petrescu, Diana Laura Borza, and Dan Mircea Suciu
(Babeș-Bolyai University, Romania)
Innovation and entrepreneurship undeniably contribute to the progress of contemporary economies, and therefore an increasing number of universities are incorporating entrepreneurship-related courses into their curricula. The present study presents an innovative experiment for teaching an introductory entrepreneurship course, Fundamentals of Entrepreneurship, to students from different faculties, with different mindsets, needs, ideas, and hobbies. Following the establishment of the curriculum by the University, each lecture was delivered by a different invited speaker, highly skilled on the topic. The course aimed to encourage entrepreneurship, with a focus on digitisation, but also to foster the integration of people with different backgrounds and knowledge and to cultivate critical thinking. At the end of the course, we analyzed students’ opinions regarding the course format, the level of acquired knowledge and if they managed to find relevant information. We used quantitative and qualitative methods to interpret and analyse the collected data. The results correlated and reflected the same outcome: overall, the students appreciated the course format and the provided information.
@InProceedings{EASEAI22p25,
author = {Manuela Andreea Petrescu and Diana Laura Borza and Dan Mircea Suciu},
title = {Findings from Teaching Entrepreneurship to Undergraduate Multidisciplinary Students: Case Study},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {25--32},
doi = {10.1145/3548660.3561333},
year = {2022},
}
Publisher's Version
EASEAI '22: "Towards Automated Testing ..."
Towards Automated Testing for Simple Programming Exercises
Pierre Ortegat, Benoît Vanderose, and Xavier Devroey
(University of Namur, Belgium)
Automated feedback and grading platforms can require substantial effort when encoding new programming exercises for first-year students. Such exercises are usually simple but require defining several test cases to ensure their functional correctness. This paper describes our initial effort to leverage automated test case generation for simple programming exercises. We rely on grey-box fuzzing and random combinations of method calls to test the students' solutions and compare their execution to the results produced by a reference implementation. We implemented our approach in a prototype, called SimPyTest, openly available on GitHub. We discuss its usage and possible future extensions.
@InProceedings{EASEAI22p33,
author = {Pierre Ortegat and Benoît Vanderose and Xavier Devroey},
title = {Towards Automated Testing for Simple Programming Exercises},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {33--36},
doi = {10.1145/3548660.3561334},
year = {2022},
}
Publisher's Version
EASEAI '22: "Student Misconceptions about ..."
Student Misconceptions about Finite State Machines: Identify Them in Order to Create a Concept Inventory
Julie Henry, Bruno Dumas, Andreea Vescan, and Alexandra Maria Pasca
(University of Namur, Belgium; Babeș-Bolyai University, Romania)
A concept inventory (CI) is a standardized assessment tool designed to evaluate a student's understanding of the fundamental concepts of a topic. To create a CI, it is necessary to accurately identify these concepts, but also to identify how poorly students understand them. The aim of this paper is to present an approach used to identify misconceptions related to the concept of Finite State Machine (FSM). In the learning process, identifying the students' misconceptions, i.e., when they appear and how to efficiently correct them, are important aspects of the best learning outcome. Rather than measuring understanding at a specific point in the learning timeline, the CI can be administered to students several times over the course of the learning period to measure how students' understanding of concepts changes. This preliminary study is composed of two main steps. In the first step, four misconceptions were identified about FSM based on multi-year observations and teacher experiences. From these misconceptions, seven statements about FSM are specified. In the second step, a Likert scale questionnaire (composed of seven statements) was administered five times to students according to a specific schedule, allowing to measure the evolution of FSM understanding. A pre-questionnaire is used to determine the students' misconceptions about the FSM concept, based on their learning (self-learning or from previous courses). This measure, which is the starting point of this preliminary study, makes it possible to highlight the changes in the students' positioning in relation to the statements provided and to link these changes to the teaching interventions. Thus, changes are clearly observable after the two theoretical classes, and stabilization is devoted after the delivery of the lab work.
@InProceedings{EASEAI22p2,
author = {Julie Henry and Bruno Dumas and Andreea Vescan and Alexandra Maria Pasca},
title = {Student Misconceptions about Finite State Machines: Identify Them in Order to Create a Concept Inventory},
booktitle = {Proc.\ EASEAI},
publisher = {ACM},
pages = {2--9},
doi = {10.1145/3548660.3561330},
year = {2022},
}
Publisher's Version