Pub Date : 2019-10-01DOI: 10.1109/FIE43999.2019.9028600
Virginia Niculescu, C. Serban, A. Vescan
This Research to Practice Ful1 Paper presents a study regarding applying cyclic learning strategy with a special focus on object-oriented programming and states our findings, emphasizing both the advantages and disadvantages.The research considers as a use-case the teaching activity in the Faculty of Computer Science of Babeş-Bolyai University. The analysis takes into consideration several disciplines that compass a set of interconnected teaching objectives and aspects: (1) fundamental concepts and mechanisms (F) defined by object-orientated programming paradigm, (2) design principles, heuristics, and rules (D) that act as strategies implied in object-oriented design, and (3) functional and nonfunctional requirements related to software architecture (A).The study is directed by a statistical analysis of the grades obtained by the students at different courses that treat the (F, D, A) aspects, and also the results obtained at the Bachelor's final exam that evaluates the level of the acquired fundamental knowledge. Their evolution and correlation during a period of several years are analyzed, and together with an analysis of the degree of absorption of the students in the IT industry form the base of the study.
{"title":"Does Cyclic Learning have Positive Impact on Teaching Object-Oriented Programming?","authors":"Virginia Niculescu, C. Serban, A. Vescan","doi":"10.1109/FIE43999.2019.9028600","DOIUrl":"https://doi.org/10.1109/FIE43999.2019.9028600","url":null,"abstract":"This Research to Practice Ful1 Paper presents a study regarding applying cyclic learning strategy with a special focus on object-oriented programming and states our findings, emphasizing both the advantages and disadvantages.The research considers as a use-case the teaching activity in the Faculty of Computer Science of Babeş-Bolyai University. The analysis takes into consideration several disciplines that compass a set of interconnected teaching objectives and aspects: (1) fundamental concepts and mechanisms (F) defined by object-orientated programming paradigm, (2) design principles, heuristics, and rules (D) that act as strategies implied in object-oriented design, and (3) functional and nonfunctional requirements related to software architecture (A).The study is directed by a statistical analysis of the grades obtained by the students at different courses that treat the (F, D, A) aspects, and also the results obtained at the Bachelor's final exam that evaluates the level of the acquired fundamental knowledge. Their evolution and correlation during a period of several years are analyzed, and together with an analysis of the degree of absorption of the students in the IT industry form the base of the study.","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"41 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90472884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/FIE43999.2019.9028395
Michael Rugh, Ashley M. Williams, Yujin Lee, R. Capraro
Presented in this research category full paper is a study on the effectiveness of Texas Science, Technology, Engineering, and Mathematics (T-STEM) high schools on students’ mathematics achievement. The original T-STEM blueprints for funding and designating a school as T-STEM include specifications for raising grade 9 Algebra I End of Course assessment scores. As inclusive STEM high schools, T-STEM schools have been designed with the intention to better serve ethnic minorities, females, and low socio-economic status students by improving both academic achievement and attitude toward STEM fields. The purpose of this study was to examine the effect of attending T-STEM designated schools on students’ mathematics achievement after taking into account prior mathematics achievement, economic disadvantage, gender, and ethnicity. After controlling for these variables, T-STEM schools had no statistically significant effect on Algebra I End of Course assessment scores in the present study. We interpret the results as demonstrating that T-STEM schools are not sufficiently improving students’ mathematics achievement and are thus failing to achieve one of the critical standards set out by the original initiative to create T-STEM schools.
{"title":"Comparing STEM Schools on Algebra Performance","authors":"Michael Rugh, Ashley M. Williams, Yujin Lee, R. Capraro","doi":"10.1109/FIE43999.2019.9028395","DOIUrl":"https://doi.org/10.1109/FIE43999.2019.9028395","url":null,"abstract":"Presented in this research category full paper is a study on the effectiveness of Texas Science, Technology, Engineering, and Mathematics (T-STEM) high schools on students’ mathematics achievement. The original T-STEM blueprints for funding and designating a school as T-STEM include specifications for raising grade 9 Algebra I End of Course assessment scores. As inclusive STEM high schools, T-STEM schools have been designed with the intention to better serve ethnic minorities, females, and low socio-economic status students by improving both academic achievement and attitude toward STEM fields. The purpose of this study was to examine the effect of attending T-STEM designated schools on students’ mathematics achievement after taking into account prior mathematics achievement, economic disadvantage, gender, and ethnicity. After controlling for these variables, T-STEM schools had no statistically significant effect on Algebra I End of Course assessment scores in the present study. We interpret the results as demonstrating that T-STEM schools are not sufficiently improving students’ mathematics achievement and are thus failing to achieve one of the critical standards set out by the original initiative to create T-STEM schools.","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"9 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89471717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/FIE43999.2019.9028461
Vaibhav Anu, G. Walia, Gary L. Bradshaw, Mohammad Alqudah
[Background]: This Research Category Full Paper presents the results of authors’ efforts to develop and evaluate learning materials for introducing Software Engineering (SE) students to the Cognitive Psychology concept of human errors (specifically to those human errors that occur during software development). During the last few years, the authors have developed, through a rigorous literature review and empirical investigation, human error intervention instrumentation and supporting training/teaching material. The intervention instrument consists of a corpus of human errors and a tool to support human error based software requirements inspections. The primary aim of developing this instrumentation and training material is to impart SE/CS students with the knowledge about the most frequently committed human errors during the software development process. [Goal and Method]: First, a study was conducted with Industry Practitioners with the goal of examining if the practitioners believed that human errors and human error training are useful and relevant to the software development process. Next, based on feedback from the practitioners, a study was conducted in an undergraduate Software Engineering course where students were trained using the human error instrument and were asked to perform error based requirements inspections. The high-level goal of this paper is to evaluate whether requirements inspections supported by human errors can be used to deliver knowledge about software engineering human errors as well as knowledge about requirements inspections (a key industry skill) to students. [Results]: Results showed that industry practitioners found the human error instrumentation and training useful. Based on their feedback, when the training was administered to students, it helped students understand those human errors that are the frequently committed during the software development process.
{"title":"Developing and Evaluating Learning Materials to Introduce Human Error Concepts in Software Engineering Courses: Results from Industry and Academia","authors":"Vaibhav Anu, G. Walia, Gary L. Bradshaw, Mohammad Alqudah","doi":"10.1109/FIE43999.2019.9028461","DOIUrl":"https://doi.org/10.1109/FIE43999.2019.9028461","url":null,"abstract":"[Background]: This Research Category Full Paper presents the results of authors’ efforts to develop and evaluate learning materials for introducing Software Engineering (SE) students to the Cognitive Psychology concept of human errors (specifically to those human errors that occur during software development). During the last few years, the authors have developed, through a rigorous literature review and empirical investigation, human error intervention instrumentation and supporting training/teaching material. The intervention instrument consists of a corpus of human errors and a tool to support human error based software requirements inspections. The primary aim of developing this instrumentation and training material is to impart SE/CS students with the knowledge about the most frequently committed human errors during the software development process. [Goal and Method]: First, a study was conducted with Industry Practitioners with the goal of examining if the practitioners believed that human errors and human error training are useful and relevant to the software development process. Next, based on feedback from the practitioners, a study was conducted in an undergraduate Software Engineering course where students were trained using the human error instrument and were asked to perform error based requirements inspections. The high-level goal of this paper is to evaluate whether requirements inspections supported by human errors can be used to deliver knowledge about software engineering human errors as well as knowledge about requirements inspections (a key industry skill) to students. [Results]: Results showed that industry practitioners found the human error instrumentation and training useful. Based on their feedback, when the training was administered to students, it helped students understand those human errors that are the frequently committed during the software development process.","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"50 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86740595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/FIE43999.2019.9028709
Catherine G. P. Berdanier
The purpose of this full research paper is to explore faculty perceptions on how departments and colleges can continue to support faculty through the associate professor level. Little research exists that discusses best practices or even possible formats for mentoring and faculty development programs at the associate professor level. This study begins to fill this gap for one institutional type, eliciting suggestions from faculty on desired characteristics and attributes of a mid-career (associate) faculty development program. To elicit feedback, five focus groups of between 5-9 tenured or tenure-track mechanical engineering faculty each were conducted to elicit information on what faculty at various levels knew about career development post tenure, and what their ideas of an “ideal” mid-career faculty development initiative would look like. Audio recordings and jottings were collected as data and analyzed through constant comparative methods and content analysis methods, using landscapes of practice theory to highlight areas where faculty noted the need for explicit professional development. Results indicate that perceptions on career advancement post-tenure differed between faculty at different levels, and indicate several potential structures, characteristics, and attributes that a successful potential midcareer faculty development model might have.
{"title":"Toward an Engineering Faculty Development Initiative for Associate Professors: Results from Focus Groups at an R1 Institution","authors":"Catherine G. P. Berdanier","doi":"10.1109/FIE43999.2019.9028709","DOIUrl":"https://doi.org/10.1109/FIE43999.2019.9028709","url":null,"abstract":"The purpose of this full research paper is to explore faculty perceptions on how departments and colleges can continue to support faculty through the associate professor level. Little research exists that discusses best practices or even possible formats for mentoring and faculty development programs at the associate professor level. This study begins to fill this gap for one institutional type, eliciting suggestions from faculty on desired characteristics and attributes of a mid-career (associate) faculty development program. To elicit feedback, five focus groups of between 5-9 tenured or tenure-track mechanical engineering faculty each were conducted to elicit information on what faculty at various levels knew about career development post tenure, and what their ideas of an “ideal” mid-career faculty development initiative would look like. Audio recordings and jottings were collected as data and analyzed through constant comparative methods and content analysis methods, using landscapes of practice theory to highlight areas where faculty noted the need for explicit professional development. Results indicate that perceptions on career advancement post-tenure differed between faculty at different levels, and indicate several potential structures, characteristics, and attributes that a successful potential midcareer faculty development model might have.","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"5 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87925533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/FIE43999.2019.9028354
Yasser El Geddawy, Fernando A. Mikic-Fonte, M. Nistal, M. Caeiro
This Research to Practice Work in Progress presents the first steps and ideas of a framework to address the problem of suggesting the most suitable recommendations for instructors (for teaching and assessing), by designing an intelligent multi-agent recommender system that uses data analysis methods. The paper addresses the whole framework, specifically focusing on the assessment part. The framework proposed takes into consideration the heterogeneous personalities and teaching/assessing styles of different instructors to personalize and customize their experience. It provides immediate and customize instructions and feedback to help instructors improve their educational tasks. The dataset contains data collected from the engagement of the instructor with the agents, to predict their teaching/assessing style from their behavior. The agent system has the ability to recommend methods and tools against a topic. It tries to build different instructors’ profiles, to generalize the most common practices toward an activity for future use. The agent system is like a personal assistant that helps teachers with finding information, and it gives them recommendations.
{"title":"Adaptive Multi-Agent Assisting Framework for a Personal Teaching Environment","authors":"Yasser El Geddawy, Fernando A. Mikic-Fonte, M. Nistal, M. Caeiro","doi":"10.1109/FIE43999.2019.9028354","DOIUrl":"https://doi.org/10.1109/FIE43999.2019.9028354","url":null,"abstract":"This Research to Practice Work in Progress presents the first steps and ideas of a framework to address the problem of suggesting the most suitable recommendations for instructors (for teaching and assessing), by designing an intelligent multi-agent recommender system that uses data analysis methods. The paper addresses the whole framework, specifically focusing on the assessment part. The framework proposed takes into consideration the heterogeneous personalities and teaching/assessing styles of different instructors to personalize and customize their experience. It provides immediate and customize instructions and feedback to help instructors improve their educational tasks. The dataset contains data collected from the engagement of the instructor with the agents, to predict their teaching/assessing style from their behavior. The agent system has the ability to recommend methods and tools against a topic. It tries to build different instructors’ profiles, to generalize the most common practices toward an activity for future use. The agent system is like a personal assistant that helps teachers with finding information, and it gives them recommendations.","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"21 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87951306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/FIE43999.2019.9028652
Spencer E. Offenberger, Geoffrey L. Herman, Peter A. H. Peterson, A. Sherman, Enis Golaszewski, Travis Scheponik, Linda Oliva
We analyze expert review and student performance data to evaluate the validity of the Cybersecurity Concept Inventory (CCI) for assessing student knowledge of core cybersecurity concepts after a first course on the topic. A panel of 12 experts in cybersecurity reviewed the CCI, and 142 students from six different institutions took the CCI as a pilot test. The panel reviewed each item of the CCI and the overwhelming majority rated every item as measuring appropriate cybersecurity knowledge. We administered the CCI to students taking a first cybersecurity course either online or proctored by the course instructor. We applied classical test theory to evaluate the quality of the CCI. This evaluation showed that the CCI is sufficiently reliable for measuring student knowledge of cybersecurity and that the CCI may be too difficult as a whole. We describe the results of the expert review and the pilot test and provide recommendations for the continued improvement of the CCI.
{"title":"Initial Validation of the Cybersecurity Concept Inventory: Pilot Testing and Expert Review","authors":"Spencer E. Offenberger, Geoffrey L. Herman, Peter A. H. Peterson, A. Sherman, Enis Golaszewski, Travis Scheponik, Linda Oliva","doi":"10.1109/FIE43999.2019.9028652","DOIUrl":"https://doi.org/10.1109/FIE43999.2019.9028652","url":null,"abstract":"We analyze expert review and student performance data to evaluate the validity of the Cybersecurity Concept Inventory (CCI) for assessing student knowledge of core cybersecurity concepts after a first course on the topic. A panel of 12 experts in cybersecurity reviewed the CCI, and 142 students from six different institutions took the CCI as a pilot test. The panel reviewed each item of the CCI and the overwhelming majority rated every item as measuring appropriate cybersecurity knowledge. We administered the CCI to students taking a first cybersecurity course either online or proctored by the course instructor. We applied classical test theory to evaluate the quality of the CCI. This evaluation showed that the CCI is sufficiently reliable for measuring student knowledge of cybersecurity and that the CCI may be too difficult as a whole. We describe the results of the expert review and the pilot test and provide recommendations for the continued improvement of the CCI.","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"50 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85873766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/FIE43999.2019.9028555
Renata A. Revelo, Janet Omitoyin, M. Cardona, Rezvan Nazempour, H. Darabi
This work-in-progress research paper explores the way in which low-socioeconomic status (SES), first-year undergraduate engineering students develop their engineering identity. Identification with the field of engineering, or engineering identity development, is an ongoing process for students. While scholars have used retrospective studies to understand the developmental aspect of this process, a longitudinal study that follows students’ engineering identity development could provide an advantageous viewpoint. In this study, we investigate the engineering identity profiles of incoming low-SES, high-achieving engineering students. We interviewed 13 students using a protocol focused on understanding the students’ engineering identity profiles before entering engineering school. An integrated model of engineering identity development was used to frame the research and guide the analysis. Our preliminary results show existing pre-college identity-related patterns across students as well as initial ways of identifying with their major and engineering as a field. This work has contributions to research in the areas of engineering identity development as well as broadening understanding of engineering students who are both low-income and high-achieving. Our work has practical implications for academic and professional support programs for low-income engineering students and programs that aim to support engineering identity development.
{"title":"Engineering Identity Profiles of Low-SES, High-Achieving Incoming Engineering Students","authors":"Renata A. Revelo, Janet Omitoyin, M. Cardona, Rezvan Nazempour, H. Darabi","doi":"10.1109/FIE43999.2019.9028555","DOIUrl":"https://doi.org/10.1109/FIE43999.2019.9028555","url":null,"abstract":"This work-in-progress research paper explores the way in which low-socioeconomic status (SES), first-year undergraduate engineering students develop their engineering identity. Identification with the field of engineering, or engineering identity development, is an ongoing process for students. While scholars have used retrospective studies to understand the developmental aspect of this process, a longitudinal study that follows students’ engineering identity development could provide an advantageous viewpoint. In this study, we investigate the engineering identity profiles of incoming low-SES, high-achieving engineering students. We interviewed 13 students using a protocol focused on understanding the students’ engineering identity profiles before entering engineering school. An integrated model of engineering identity development was used to frame the research and guide the analysis. Our preliminary results show existing pre-college identity-related patterns across students as well as initial ways of identifying with their major and engineering as a field. This work has contributions to research in the areas of engineering identity development as well as broadening understanding of engineering students who are both low-income and high-achieving. Our work has practical implications for academic and professional support programs for low-income engineering students and programs that aim to support engineering identity development.","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"40 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86317960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/FIE43999.2019.9028444
Katherine M. Ehlert, Marisa K. Orr
The primary purpose of this Student Research Poster Paper is to discuss two grouping methodologies: cluste analysis and the Q-Methodology. Each of these statistical methodologies quantitatively group similar individuals but do so in two separate ways. In cluster analysis, individuals are grouped by optimizing proximity measures. For example, the single link clustering algorithm groups individuals together that have the smallest Euclidean distance from each other. In Q-Methodology, individuals are grouped by evaluating person-to-person correlations. For example, if two individuals have a correlation of 0.78, they are likely to be grouped together whereas individuals with a correlation of 0.09 are likely to be in different groups. In this paper, we outline multiple clustering approaches, the grouping mechanism in Q-Methodology, and discuss the differences between these two approaches when grouping participants. During this discussion, we will use an example from our own engineering education research to compare grouping results from the same data set. This paper contributes to the research field by describing and utilizing a relatively unknown methodology in engineering education. It will also add to our knowledge of cluster analysis techniques and compare those algorithms to another robust grouping method.
{"title":"Comparing Grouping Results Between Cluster Analysis and Q-Methodology","authors":"Katherine M. Ehlert, Marisa K. Orr","doi":"10.1109/FIE43999.2019.9028444","DOIUrl":"https://doi.org/10.1109/FIE43999.2019.9028444","url":null,"abstract":"The primary purpose of this Student Research Poster Paper is to discuss two grouping methodologies: cluste analysis and the Q-Methodology. Each of these statistical methodologies quantitatively group similar individuals but do so in two separate ways. In cluster analysis, individuals are grouped by optimizing proximity measures. For example, the single link clustering algorithm groups individuals together that have the smallest Euclidean distance from each other. In Q-Methodology, individuals are grouped by evaluating person-to-person correlations. For example, if two individuals have a correlation of 0.78, they are likely to be grouped together whereas individuals with a correlation of 0.09 are likely to be in different groups. In this paper, we outline multiple clustering approaches, the grouping mechanism in Q-Methodology, and discuss the differences between these two approaches when grouping participants. During this discussion, we will use an example from our own engineering education research to compare grouping results from the same data set. This paper contributes to the research field by describing and utilizing a relatively unknown methodology in engineering education. It will also add to our knowledge of cluster analysis techniques and compare those algorithms to another robust grouping method.","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"14 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86370922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/FIE43999.2019.9028503
H. Shoaib, M. Cardella, Aasakiran Madamanchi, David M. Umulis
This research study presents our work focused on studying the development of introductory computational thinking in undergraduate biomedical engineering students. In response to the growing computational intensity of the healthcare industry, biomedical engineering (BME) undergraduate education is starting to emphasize computation and computational thinking. Computational thinking is a way of thinking that uses concepts and methodologies of computing to solve problems in interdisciplinary and multidisciplinary subjects. In broader terms, computational thinking is not only associated with using computational tools but also with the thought process of solving a problem by data representation, problem decomposition, and algorithm design. Despite being so important, there is little research work or information available on the development of computational thinking in BME undergraduate students. Our research focuses on how BME undergraduate students develop computational thinking skills while performing group activities related to problem-solving. In order to conduct this study, we incorporate a teaching methodology that prompts computational thinking in a thermodynamics course being taught at a public mid-western university to approximately 120 sophomore students. We observe classroom activities involving analytical problem solving followed by pseudo code generation for computational coding. In order to investigate computational thinking, we collect classroom observations of small groups of students as they come up with a solution to an analytical problem with each other. We complement the observation notes of the classroom activities with follow up semi structured interviews with individual students from five groups. Thematic analysis of the student interviews is used in order to analyze student responses towards the incorporation of computation intensive teaching methodology. This Work in Progress helps us expand our understanding of computational thinking development and the challenges involved in performing computational thinking activity in BME undergraduate students.
{"title":"An Investigation of Undergraduates’ Computational Thinking in a Sophomore-Level Biomedical Engineering Course","authors":"H. Shoaib, M. Cardella, Aasakiran Madamanchi, David M. Umulis","doi":"10.1109/FIE43999.2019.9028503","DOIUrl":"https://doi.org/10.1109/FIE43999.2019.9028503","url":null,"abstract":"This research study presents our work focused on studying the development of introductory computational thinking in undergraduate biomedical engineering students. In response to the growing computational intensity of the healthcare industry, biomedical engineering (BME) undergraduate education is starting to emphasize computation and computational thinking. Computational thinking is a way of thinking that uses concepts and methodologies of computing to solve problems in interdisciplinary and multidisciplinary subjects. In broader terms, computational thinking is not only associated with using computational tools but also with the thought process of solving a problem by data representation, problem decomposition, and algorithm design. Despite being so important, there is little research work or information available on the development of computational thinking in BME undergraduate students. Our research focuses on how BME undergraduate students develop computational thinking skills while performing group activities related to problem-solving. In order to conduct this study, we incorporate a teaching methodology that prompts computational thinking in a thermodynamics course being taught at a public mid-western university to approximately 120 sophomore students. We observe classroom activities involving analytical problem solving followed by pseudo code generation for computational coding. In order to investigate computational thinking, we collect classroom observations of small groups of students as they come up with a solution to an analytical problem with each other. We complement the observation notes of the classroom activities with follow up semi structured interviews with individual students from five groups. Thematic analysis of the student interviews is used in order to analyze student responses towards the incorporation of computation intensive teaching methodology. This Work in Progress helps us expand our understanding of computational thinking development and the challenges involved in performing computational thinking activity in BME undergraduate students.","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"11 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89177435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/FIE43999.2019.9028609
Wen-li Wang, M. Tang
This is an Innovative Practice Full Paper. This paper introduces a multi-program assessment system for a department offering multiple majors to seek accreditation from ABET CAC and EAC. The Canvas LMS is our mandated tool for faculty to set up course rubrics, assign grades, and communicate with students. However, it falls short on solving several of our data issues, including data retrieval, data separation, data sharing, data integration, and data security. The Canvas LMS provides no data download for individual rubric criteria of an instrument, hindering analysis to a more granular level. There is also no information to identify a student’s major, making data separation for different academic programs difficult. Faculty are reluctant to share student data on Canvas for integration, worrying about a violation to FERPA. Moreover, data sharing and data integration can increase the risk of data security. Our assessment system is built atop of Canvas and is a service oriented web application for faculty to assess student performance and generate instrument reports. The reports reveal only computed statistics for data sharing, integration and security purposes. The provided web services can interact with another system to distinguish student majors and consolidate instruments whose rubric criteria cover the same performance indicators (PIs). The system is a safe and sound methodology to conquer our data problems and can facilitate both formative and summative analyses for the program outcomes evaluation.
{"title":"A Canvas Based Multi-Program Assessment System for ABET CAC and EAC Accreditations","authors":"Wen-li Wang, M. Tang","doi":"10.1109/FIE43999.2019.9028609","DOIUrl":"https://doi.org/10.1109/FIE43999.2019.9028609","url":null,"abstract":"This is an Innovative Practice Full Paper. This paper introduces a multi-program assessment system for a department offering multiple majors to seek accreditation from ABET CAC and EAC. The Canvas LMS is our mandated tool for faculty to set up course rubrics, assign grades, and communicate with students. However, it falls short on solving several of our data issues, including data retrieval, data separation, data sharing, data integration, and data security. The Canvas LMS provides no data download for individual rubric criteria of an instrument, hindering analysis to a more granular level. There is also no information to identify a student’s major, making data separation for different academic programs difficult. Faculty are reluctant to share student data on Canvas for integration, worrying about a violation to FERPA. Moreover, data sharing and data integration can increase the risk of data security. Our assessment system is built atop of Canvas and is a service oriented web application for faculty to assess student performance and generate instrument reports. The reports reveal only computed statistics for data sharing, integration and security purposes. The provided web services can interact with another system to distinguish student majors and consolidate instruments whose rubric criteria cover the same performance indicators (PIs). The system is a safe and sound methodology to conquer our data problems and can facilitate both formative and summative analyses for the program outcomes evaluation.","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"33 4 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75915739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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