Pub Date : 2018-10-01DOI: 10.1109/FIE.2018.8658929
Jun Dai
This Research to Practice Full Paper presents a new methodology in cybersecurity education. In the context of the cybersecurity profession, the ‘isolation problem’ refers to the observed isolation of different knowledge units, as well as the isolation of technical and business perspectives. Due to limitations in existing cybersecurity education, professionals entering the field are often trapped in microscopic perspectives, and struggle to extend their findings to grasp the big picture in a target network scenario. Guided by a previous developed and published framework named “cross-layer situation knowledge reference model” (SKRM), which delivers comprehensive level big picture situation awareness, our new methodology targets at developing suites of teaching modules to address the above issues. The modules, featuring interactive hands-on labs that emulate real-world multiple-step attacks, will help students form a knowledge network instead of isolated conceptual knowledge units. Students will not just be required to leverage various techniques/tools to analyze breakpoints and complete individual modules; they will be required to connect logically the outputs of these techniques/tools to infer the ground truth and gain big picture awareness of the cyber situation. The modules will be able to be used separately or as a whole in a typical network security course.
{"title":"Situation Awareness-Oriented Cybersecurity Education","authors":"Jun Dai","doi":"10.1109/FIE.2018.8658929","DOIUrl":"https://doi.org/10.1109/FIE.2018.8658929","url":null,"abstract":"This Research to Practice Full Paper presents a new methodology in cybersecurity education. In the context of the cybersecurity profession, the ‘isolation problem’ refers to the observed isolation of different knowledge units, as well as the isolation of technical and business perspectives. Due to limitations in existing cybersecurity education, professionals entering the field are often trapped in microscopic perspectives, and struggle to extend their findings to grasp the big picture in a target network scenario. Guided by a previous developed and published framework named “cross-layer situation knowledge reference model” (SKRM), which delivers comprehensive level big picture situation awareness, our new methodology targets at developing suites of teaching modules to address the above issues. The modules, featuring interactive hands-on labs that emulate real-world multiple-step attacks, will help students form a knowledge network instead of isolated conceptual knowledge units. Students will not just be required to leverage various techniques/tools to analyze breakpoints and complete individual modules; they will be required to connect logically the outputs of these techniques/tools to infer the ground truth and gain big picture awareness of the cyber situation. The modules will be able to be used separately or as a whole in a typical network security course.","PeriodicalId":354904,"journal":{"name":"2018 IEEE Frontiers in Education Conference (FIE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134013069","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 : 2018-10-01DOI: 10.1109/FIE.2018.8658460
Anastasia M. Rynearson, L. Rynearson
This work-in-progress paper provides initial details in an early and ongoing process to embed affective assessment of student development throughout a new engineering program. Assessment of student knowledge, skills, attitudes & values, and behaviors is an important part of course and program assessment. Affective assessment covers the attitudes, values, and behaviors that students develop throughout their undergraduate engineering career. The overarching framework that guides the affective development, and therefore the affective assessment, of undergraduate engineers in this program is the Community of Practice framework. Current status of the affective development and assessment program is shared, including results from an engineering beliefs and identity survey and methods for incorporating the Community of Practice framework into the undergraduate engineering curriculum.
{"title":"Embedded Affective Assessment","authors":"Anastasia M. Rynearson, L. Rynearson","doi":"10.1109/FIE.2018.8658460","DOIUrl":"https://doi.org/10.1109/FIE.2018.8658460","url":null,"abstract":"This work-in-progress paper provides initial details in an early and ongoing process to embed affective assessment of student development throughout a new engineering program. Assessment of student knowledge, skills, attitudes & values, and behaviors is an important part of course and program assessment. Affective assessment covers the attitudes, values, and behaviors that students develop throughout their undergraduate engineering career. The overarching framework that guides the affective development, and therefore the affective assessment, of undergraduate engineers in this program is the Community of Practice framework. Current status of the affective development and assessment program is shared, including results from an engineering beliefs and identity survey and methods for incorporating the Community of Practice framework into the undergraduate engineering curriculum.","PeriodicalId":354904,"journal":{"name":"2018 IEEE Frontiers in Education Conference (FIE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134069177","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 : 2018-10-01DOI: 10.1109/FIE.2018.8658580
K. V. Lehmkuhl-Dakhwe
This Innovative Practice, Work in Progress paper outlines an Instructional Framework for integrating computing into science instruction in 4th-12th grade classrooms. It presents a model lesson example and results from two years of implementing a Professional Learning (PL) program for teachers developed and offered by the STEM Education Office of the College of Science at San José State University and teachers from eight high need school districts. The program model focuses on increasing teachers’ skills and improving practices related to Scientific Computational Modeling as the Next Generation Science Standards and Science and Engineering Practices 2 (develop and use models), 4 (analyze and interpret data), and 5 (use math and computational thinking) are implemented.
{"title":"An instructional framework, model lessons, and professional learning program for science standards-aligned computing in 4th-12th grade classrooms","authors":"K. V. Lehmkuhl-Dakhwe","doi":"10.1109/FIE.2018.8658580","DOIUrl":"https://doi.org/10.1109/FIE.2018.8658580","url":null,"abstract":"This Innovative Practice, Work in Progress paper outlines an Instructional Framework for integrating computing into science instruction in 4th-12th grade classrooms. It presents a model lesson example and results from two years of implementing a Professional Learning (PL) program for teachers developed and offered by the STEM Education Office of the College of Science at San José State University and teachers from eight high need school districts. The program model focuses on increasing teachers’ skills and improving practices related to Scientific Computational Modeling as the Next Generation Science Standards and Science and Engineering Practices 2 (develop and use models), 4 (analyze and interpret data), and 5 (use math and computational thinking) are implemented.","PeriodicalId":354904,"journal":{"name":"2018 IEEE Frontiers in Education Conference (FIE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134442456","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 : 2018-10-01DOI: 10.1109/FIE.2018.8658786
Samantha R. Brunhaver, K. Yasuhara, J. Case, W. Newstetter, J. Turns, Adam R. Carberry, C. Allendoerfer, C. Finelli, S. Sheppard, J. London, C. Atman, A. McKenna, K. Smith, K. Watson
This panel session combines principles from graduate student socialization and intergenerational mentorship to provide a unique opportunity for early career scholars and pioneers in engineering education to interact face-to-face. Pioneers will serve as panelists and give their personal tips and reflections on networking and mentorship. Session attendees will then meet with the pioneers in a roundtable format, to ask questions, seek advice, and get feedback. This work builds on the National Science Foundation-funded Engineering Education Pioneers Project, which documented the stories of more than 40 engineering education pioneers through online profiles. The intended audience for this panel includes graduate students, junior faculty, and other individuals interested in the engineering education community. Expected benefits include better understanding, increased belonging, and new or enhanced interest in engineering education. Future efforts associated with this session include understanding the impact of such exposure to the pioneers on attendees and exploring the possibility of offering this event at future engineering education conferences.
{"title":"Meet the Engineering Education Pioneers — Panel & Roundtable","authors":"Samantha R. Brunhaver, K. Yasuhara, J. Case, W. Newstetter, J. Turns, Adam R. Carberry, C. Allendoerfer, C. Finelli, S. Sheppard, J. London, C. Atman, A. McKenna, K. Smith, K. Watson","doi":"10.1109/FIE.2018.8658786","DOIUrl":"https://doi.org/10.1109/FIE.2018.8658786","url":null,"abstract":"This panel session combines principles from graduate student socialization and intergenerational mentorship to provide a unique opportunity for early career scholars and pioneers in engineering education to interact face-to-face. Pioneers will serve as panelists and give their personal tips and reflections on networking and mentorship. Session attendees will then meet with the pioneers in a roundtable format, to ask questions, seek advice, and get feedback. This work builds on the National Science Foundation-funded Engineering Education Pioneers Project, which documented the stories of more than 40 engineering education pioneers through online profiles. The intended audience for this panel includes graduate students, junior faculty, and other individuals interested in the engineering education community. Expected benefits include better understanding, increased belonging, and new or enhanced interest in engineering education. Future efforts associated with this session include understanding the impact of such exposure to the pioneers on attendees and exploring the possibility of offering this event at future engineering education conferences.","PeriodicalId":354904,"journal":{"name":"2018 IEEE Frontiers in Education Conference (FIE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131531906","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 : 2018-10-01DOI: 10.1109/FIE.2018.8658531
Han Wan, Kangxu Liu, Xiaopeng Gao
This Research to Practice Work in Progress Paper presents a token-based approach to detecting plagiarism in university courses with hardware programming assignments. Detecting plagiarism manually is a difficult and time-consuming work. In the last two decades, various of plagiarism detection tools have been developed. These techniques could be mainly divided into the following categories: Textual Match, Program Dependence Graph Comparison, Abstract Syntax Tree Analysis and Low-Level Form Code Comparison. Although there had been a lot of researches on detecting code clones in software programming languages (e.g. Basic, C/C++, Java, Python, etc.), research that focused on hardware description languages is still lacking. Based on the effective of the locality sensitive hash function (simhash), which was usually used in detecting near duplicates for web crawling, we proposed an improved real-time plagiarism detection approach for Verilog HDL (hardware description language) programming assignments. The core detecting steps are extracting weighted tokens from source code as high-dimensional feature, and mapping it to a f-bit fingerprints with simhash technique. On account of the syntax characteristics of Verilog HDL, a token extraction strategy was designed to maximize the valid information that a fixed length hash value could represent. Experiments over real course data sets were conducted to evaluate the performance of token-based approach comparing with an existing plagiarism detection tool (Moss). The result shows that our token-based approach does qualify the plagiarism detecting job for both online-query and batch-query in digital designs. Furthermore, token-based plagiarism detection approach could enable conduct incremental plagiarism detection for a single submission without excessive overhead. Finally, we also give a discussion of current way limitations and future research directions.
{"title":"Token-based Approach for Real-time Plagiarism Detection in Digital Designs","authors":"Han Wan, Kangxu Liu, Xiaopeng Gao","doi":"10.1109/FIE.2018.8658531","DOIUrl":"https://doi.org/10.1109/FIE.2018.8658531","url":null,"abstract":"This Research to Practice Work in Progress Paper presents a token-based approach to detecting plagiarism in university courses with hardware programming assignments. Detecting plagiarism manually is a difficult and time-consuming work. In the last two decades, various of plagiarism detection tools have been developed. These techniques could be mainly divided into the following categories: Textual Match, Program Dependence Graph Comparison, Abstract Syntax Tree Analysis and Low-Level Form Code Comparison. Although there had been a lot of researches on detecting code clones in software programming languages (e.g. Basic, C/C++, Java, Python, etc.), research that focused on hardware description languages is still lacking. Based on the effective of the locality sensitive hash function (simhash), which was usually used in detecting near duplicates for web crawling, we proposed an improved real-time plagiarism detection approach for Verilog HDL (hardware description language) programming assignments. The core detecting steps are extracting weighted tokens from source code as high-dimensional feature, and mapping it to a f-bit fingerprints with simhash technique. On account of the syntax characteristics of Verilog HDL, a token extraction strategy was designed to maximize the valid information that a fixed length hash value could represent. Experiments over real course data sets were conducted to evaluate the performance of token-based approach comparing with an existing plagiarism detection tool (Moss). The result shows that our token-based approach does qualify the plagiarism detecting job for both online-query and batch-query in digital designs. Furthermore, token-based plagiarism detection approach could enable conduct incremental plagiarism detection for a single submission without excessive overhead. Finally, we also give a discussion of current way limitations and future research directions.","PeriodicalId":354904,"journal":{"name":"2018 IEEE Frontiers in Education Conference (FIE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131649344","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 : 2018-10-01DOI: 10.1109/FIE.2018.8659063
V. Raj, Preston Goulet, S. Bansal
This Research full paper addresses the challenge of creating an effective course design that has tight alignment between various course components and incorporates appropriate pedagogy and assessment techniques. Many systems have been proposed solely for the purpose of guiding instructors through the process of course design so as to achieve maximum student learning. One such system is the Instructional Module Development System (IMODS). IMODS is a web-based software system that has been developed to assist instructors in curriculum design based on the principles of Outcome-based Education (OBE) and Bloom’s Taxonomy. It is not just enough to come up with a model that theoretically facilitates effective result-oriented course design. There should be facts, experiments and proof that any model succeeds in achieving what it aims to achieve. This paper focuses on testing and evaluation of the IMODS platform and deliver feedback to its development team. We present our methodology and instruments used for testing and evaluation of the tool. Data collection and analysis is described and the results of evaluation of IMODS are presented.
{"title":"Evaluation of Instructional Module Development System","authors":"V. Raj, Preston Goulet, S. Bansal","doi":"10.1109/FIE.2018.8659063","DOIUrl":"https://doi.org/10.1109/FIE.2018.8659063","url":null,"abstract":"This Research full paper addresses the challenge of creating an effective course design that has tight alignment between various course components and incorporates appropriate pedagogy and assessment techniques. Many systems have been proposed solely for the purpose of guiding instructors through the process of course design so as to achieve maximum student learning. One such system is the Instructional Module Development System (IMODS). IMODS is a web-based software system that has been developed to assist instructors in curriculum design based on the principles of Outcome-based Education (OBE) and Bloom’s Taxonomy. It is not just enough to come up with a model that theoretically facilitates effective result-oriented course design. There should be facts, experiments and proof that any model succeeds in achieving what it aims to achieve. This paper focuses on testing and evaluation of the IMODS platform and deliver feedback to its development team. We present our methodology and instruments used for testing and evaluation of the tool. Data collection and analysis is described and the results of evaluation of IMODS are presented.","PeriodicalId":354904,"journal":{"name":"2018 IEEE Frontiers in Education Conference (FIE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131080985","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 : 2018-10-01DOI: 10.1109/FIE.2018.8658533
C. Garcia, Jaime Leonardo Barbosa Perez, Jorge Luis Herrera Ochoa
Due to poor performance in Statics at Eafit University, since 2012 the Mechanical Engineering department implemented a virtual tool for the training and assessment of Statics course. Although the implementation led to better performance in the Statics course, students were still showing lack of comprehension of the basic concepts underlying the subject. The department applied a Concept Inventory test to 195 students from the second semester of 2017 in order to check if there was a correlation between the grades obtained in the class examinations and the performance in a conceptual test of Statics. The Concept Inventory was applied one week before the final examination and it was held inside the University facilities. The students were monitored all the time. For the course examinations were taken only three out of the four partial exams, they were computed and then compared to the results of the concept inventory. The results show that almost all the students with the highest grades in the class performed above the mean in the Concept Inventory. The overall mean for the conceptual test was 9.27 and the standard deviation was 5.28. The course grades were discriminated by career and an Analysis of Variance was conducted to determine if there were significant differences among the groups. A correlation analysis suggests that there is not a strong correlation between the course grades and the concept inventory results. One possible explanation for this can be due to the fact that the regular teaching method for these kind of basic courses in engineering in the University is merely procedural and problem-solving oriented, conceptual approaches are often neglected in both teaching and assessment.
{"title":"Correlation Between Procedural and Conceptual Test in a Statics Course","authors":"C. Garcia, Jaime Leonardo Barbosa Perez, Jorge Luis Herrera Ochoa","doi":"10.1109/FIE.2018.8658533","DOIUrl":"https://doi.org/10.1109/FIE.2018.8658533","url":null,"abstract":"Due to poor performance in Statics at Eafit University, since 2012 the Mechanical Engineering department implemented a virtual tool for the training and assessment of Statics course. Although the implementation led to better performance in the Statics course, students were still showing lack of comprehension of the basic concepts underlying the subject. The department applied a Concept Inventory test to 195 students from the second semester of 2017 in order to check if there was a correlation between the grades obtained in the class examinations and the performance in a conceptual test of Statics. The Concept Inventory was applied one week before the final examination and it was held inside the University facilities. The students were monitored all the time. For the course examinations were taken only three out of the four partial exams, they were computed and then compared to the results of the concept inventory. The results show that almost all the students with the highest grades in the class performed above the mean in the Concept Inventory. The overall mean for the conceptual test was 9.27 and the standard deviation was 5.28. The course grades were discriminated by career and an Analysis of Variance was conducted to determine if there were significant differences among the groups. A correlation analysis suggests that there is not a strong correlation between the course grades and the concept inventory results. One possible explanation for this can be due to the fact that the regular teaching method for these kind of basic courses in engineering in the University is merely procedural and problem-solving oriented, conceptual approaches are often neglected in both teaching and assessment.","PeriodicalId":354904,"journal":{"name":"2018 IEEE Frontiers in Education Conference (FIE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131085371","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 : 2018-10-01DOI: 10.1109/FIE.2018.8659260
Rama Mwenesi, Emma Brennan‐Wydra, C. Sanchez, M. Ellis, Darryl Koch, Cinda-Sue Davis, J. Millunchick
This Research Work in Progress paper presents a typology for categorizing undergraduate extra-curricular activities. We observed that the all of the activities listed on a corpus of student resumes could be fully described by defining two levels of identifiers, the first of which describes the type of activity while the second is a descriptor of the activity. As a proof of concept, the typology was applied to resumes of participants in a program that serves underrepresented students studying engineering at a large public R1 institution. Simple descriptive findings are reported, and potential future applications are discussed.
{"title":"The WREASN Typology of Student Involvement Activities","authors":"Rama Mwenesi, Emma Brennan‐Wydra, C. Sanchez, M. Ellis, Darryl Koch, Cinda-Sue Davis, J. Millunchick","doi":"10.1109/FIE.2018.8659260","DOIUrl":"https://doi.org/10.1109/FIE.2018.8659260","url":null,"abstract":"This Research Work in Progress paper presents a typology for categorizing undergraduate extra-curricular activities. We observed that the all of the activities listed on a corpus of student resumes could be fully described by defining two levels of identifiers, the first of which describes the type of activity while the second is a descriptor of the activity. As a proof of concept, the typology was applied to resumes of participants in a program that serves underrepresented students studying engineering at a large public R1 institution. Simple descriptive findings are reported, and potential future applications are discussed.","PeriodicalId":354904,"journal":{"name":"2018 IEEE Frontiers in Education Conference (FIE)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131089636","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 : 2018-10-01DOI: 10.1109/FIE.2018.8659207
J. Eustace, P. Pathak
This paper presents a novel practice testing learning framework to enhance learning in electrical science. Several learning techniques are used by students to maximise their learning. Research has shown that most of those learning techniques are not effective. Retrieval practice also referred to as the 'testing effect', promotes practice testing as it is an effective technique. Accordingly, the testing effect phenomena and its potential application to electrical science education may be particularly crucial to student learning. However, research into practice testing has been limited to date in classroom contexts, and it is necessary to broaden the inquiry to enable instructors to implement practice testing as part of their teaching and learning strategy. This research seeks to address this gap by a study of n=161 apprentices on the National Electrical Apprenticeship programme in the Republic of Ireland, across multiple training centres, and how they benefited from e-assessment practice tests to enhance learning. For this purpose, a novel practice testing learning framework is applied supporting learning, item development, classification and mapping to learning outcomes. A One-Way ANOVA with the dependent variable the performance in the randomised unseen criterion test, with student test attempts as the factor found a significant improvement in performance between groups. A significant increase in performance was also evident for participants in the practice test topic not meeting the passing grade of the overall criterion test between groups with a mean performance of 72.35% following three or four practice tests compared to 52.94% with no practice tests in the practice test topic area. This finding suggests that participation in three or four practice tests enhances learning and improves self-regulation. The insights from this study will serve as guidelines for future educational research on practice testing towards enhancing learning in electrical science and other domains.
{"title":"Enhancing Electrical Science Learning Within A Novel Practice Testing Learning Framework","authors":"J. Eustace, P. Pathak","doi":"10.1109/FIE.2018.8659207","DOIUrl":"https://doi.org/10.1109/FIE.2018.8659207","url":null,"abstract":"This paper presents a novel practice testing learning framework to enhance learning in electrical science. Several learning techniques are used by students to maximise their learning. Research has shown that most of those learning techniques are not effective. Retrieval practice also referred to as the 'testing effect', promotes practice testing as it is an effective technique. Accordingly, the testing effect phenomena and its potential application to electrical science education may be particularly crucial to student learning. However, research into practice testing has been limited to date in classroom contexts, and it is necessary to broaden the inquiry to enable instructors to implement practice testing as part of their teaching and learning strategy. This research seeks to address this gap by a study of n=161 apprentices on the National Electrical Apprenticeship programme in the Republic of Ireland, across multiple training centres, and how they benefited from e-assessment practice tests to enhance learning. For this purpose, a novel practice testing learning framework is applied supporting learning, item development, classification and mapping to learning outcomes. A One-Way ANOVA with the dependent variable the performance in the randomised unseen criterion test, with student test attempts as the factor found a significant improvement in performance between groups. A significant increase in performance was also evident for participants in the practice test topic not meeting the passing grade of the overall criterion test between groups with a mean performance of 72.35% following three or four practice tests compared to 52.94% with no practice tests in the practice test topic area. This finding suggests that participation in three or four practice tests enhances learning and improves self-regulation. The insights from this study will serve as guidelines for future educational research on practice testing towards enhancing learning in electrical science and other domains.","PeriodicalId":354904,"journal":{"name":"2018 IEEE Frontiers in Education Conference (FIE)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132783223","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 : 2018-10-01DOI: 10.1109/FIE.2018.8658685
S. Lord, M. Ohland, R. Layton, M. Camacho
How successful are undergraduate students who begin in another major and migrate into engineering disciplines after matriculation? In this work in progress, we present quantitative data on outcomes for engineering migrators disaggregated by discipline, race/ethnicity, and sex. The study includes over 73,000 engineering students from nine U.S. universities, including first-time-in-college and transfer students who ever majored in the most common engineering disciplines: Chemical, Civil, Electrical, Industrial, and Mechanical Engineering. Adopting an ecosystem mindset, we have developed metrics including the graduation rate of migrators and “migration yield” to uncover dynamic information, not afforded by the conventional pipeline model, about the successes of students who migrate among the top five engineering disciplines. Our data show that the graduation rates of migrators are typically higher than those of starters for all engineering majors studied. Migration yield varies by race/ethnicity-sex as well as discipline. Migration yield for Chemical, Electrical, Industrial and Mechanical Engineering shows a sex-based effect, whereas Civil shows a race/ethnicity-based effect.
{"title":"“Not all those who wander are lost.” Examining outcomes for migrating engineering students using ecosystem metrics","authors":"S. Lord, M. Ohland, R. Layton, M. Camacho","doi":"10.1109/FIE.2018.8658685","DOIUrl":"https://doi.org/10.1109/FIE.2018.8658685","url":null,"abstract":"How successful are undergraduate students who begin in another major and migrate into engineering disciplines after matriculation? In this work in progress, we present quantitative data on outcomes for engineering migrators disaggregated by discipline, race/ethnicity, and sex. The study includes over 73,000 engineering students from nine U.S. universities, including first-time-in-college and transfer students who ever majored in the most common engineering disciplines: Chemical, Civil, Electrical, Industrial, and Mechanical Engineering. Adopting an ecosystem mindset, we have developed metrics including the graduation rate of migrators and “migration yield” to uncover dynamic information, not afforded by the conventional pipeline model, about the successes of students who migrate among the top five engineering disciplines. Our data show that the graduation rates of migrators are typically higher than those of starters for all engineering majors studied. Migration yield varies by race/ethnicity-sex as well as discipline. Migration yield for Chemical, Electrical, Industrial and Mechanical Engineering shows a sex-based effect, whereas Civil shows a race/ethnicity-based effect.","PeriodicalId":354904,"journal":{"name":"2018 IEEE Frontiers in Education Conference (FIE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133623322","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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