Pub Date : 2020-10-21DOI: 10.1109/FIE44824.2020.9273982
Luiz Carlos Begosso, Luiz Ricardo Begosso, Natalia Aragao Christ
This Research Full Paper presents our experience in analyzing and selecting block-based programming environments to support the teaching of algorithms for the students starting the introductory courses of a Computer Science major. The teaching of algorithms and programming concepts to students of the first years of Computer Science and Engineering courses has been a major challenge because students often have difficulty understanding the logic and abstraction, leading to a high dropout rate. Some strategies have been conducted to further the mission of helping students understand better those basic concepts, but this topic still remains a major problem for students in the initial grades of those courses. In previous projects developed at our university, we have already proposed the use of learning objects and gamification, with very positive results. One of the questions that arise when we adopt new teaching approaches is to know how this new path will contribute to the student’s learning. In this project, we conducted a study on eight block-based programming environments and sought to identify which aspects of those environments comply with the Computer Science reference curriculum. Our work was based on the joint task force on Computing Curricula conducted by the ACM and IEEE Computer Society CS2013 curriculum guidelines for undergraduate programs in Computer Science. We studied the virtual programming environments Alice, MIT App Inventor, Blockly Games, Code.org, Gameblox, Pencil Code, Microsoft MakeCode and Scratch. Then, we crossed the characteristics of each, identified the positive and negative points of each teaching environment in relation to the topics established by the guidelines. We have classified the main characteristics of those programming environments, establishing criteria such as: prior programming knowledge requirements; ease of interaction with users; programming language code; availability of documentation for learning; programming practices addressed by the environment; and ease of learning programming. We believe that this work can contribute to the selection process of a suitable programming environment to be adopted in an introductory course of computer programming.
{"title":"An analysis of block-based programming environments for CS1","authors":"Luiz Carlos Begosso, Luiz Ricardo Begosso, Natalia Aragao Christ","doi":"10.1109/FIE44824.2020.9273982","DOIUrl":"https://doi.org/10.1109/FIE44824.2020.9273982","url":null,"abstract":"This Research Full Paper presents our experience in analyzing and selecting block-based programming environments to support the teaching of algorithms for the students starting the introductory courses of a Computer Science major. The teaching of algorithms and programming concepts to students of the first years of Computer Science and Engineering courses has been a major challenge because students often have difficulty understanding the logic and abstraction, leading to a high dropout rate. Some strategies have been conducted to further the mission of helping students understand better those basic concepts, but this topic still remains a major problem for students in the initial grades of those courses. In previous projects developed at our university, we have already proposed the use of learning objects and gamification, with very positive results. One of the questions that arise when we adopt new teaching approaches is to know how this new path will contribute to the student’s learning. In this project, we conducted a study on eight block-based programming environments and sought to identify which aspects of those environments comply with the Computer Science reference curriculum. Our work was based on the joint task force on Computing Curricula conducted by the ACM and IEEE Computer Society CS2013 curriculum guidelines for undergraduate programs in Computer Science. We studied the virtual programming environments Alice, MIT App Inventor, Blockly Games, Code.org, Gameblox, Pencil Code, Microsoft MakeCode and Scratch. Then, we crossed the characteristics of each, identified the positive and negative points of each teaching environment in relation to the topics established by the guidelines. We have classified the main characteristics of those programming environments, establishing criteria such as: prior programming knowledge requirements; ease of interaction with users; programming language code; availability of documentation for learning; programming practices addressed by the environment; and ease of learning programming. We believe that this work can contribute to the selection process of a suitable programming environment to be adopted in an introductory course of computer programming.","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"25 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74820946","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 : 2020-10-21DOI: 10.1109/FIE44824.2020.9273950
Joethe Moraes de Carvalho, J. F. D. M. Netto
This Research Full paper presents a Systematic Mapping Study of actions that are being carried out in the academic environment, aiming at teaching robotics and programming through collaborative procedures at a distance. Recent studies point to an increasing utilization of collaborative activities in teaching-learning process of robotics and programming, favoring the STEM fields, with encouraging results regarding the improvement of student’s skills and better use of educational institutions infrastructure. Our goal is to collect information about state of the art on collaborative learning, employed through Groupware. To carry out this investigation, goals were defined by the Systematic Mapping Process, with the purpose of providing a better process understanding. The Research Questions were stipulated to be answered after the results analysis and the Search Strings, which allowed to select publications that satisfy the objective of this research articles in main digital repositories, all carried out in the last 5 years. After applying filters, 22 articles were considered more relevant according to the research objective. The results show the activities, modalities, methodologies, accomplished pedagogical concepts and the conclusions obtained. This work aims to assist researchers who seek information on referred topic.
{"title":"Currents Trends in Use of Collaborative Learning in Teaching of Robotics and Programming - A Systematic Review of Literature","authors":"Joethe Moraes de Carvalho, J. F. D. M. Netto","doi":"10.1109/FIE44824.2020.9273950","DOIUrl":"https://doi.org/10.1109/FIE44824.2020.9273950","url":null,"abstract":"This Research Full paper presents a Systematic Mapping Study of actions that are being carried out in the academic environment, aiming at teaching robotics and programming through collaborative procedures at a distance. Recent studies point to an increasing utilization of collaborative activities in teaching-learning process of robotics and programming, favoring the STEM fields, with encouraging results regarding the improvement of student’s skills and better use of educational institutions infrastructure. Our goal is to collect information about state of the art on collaborative learning, employed through Groupware. To carry out this investigation, goals were defined by the Systematic Mapping Process, with the purpose of providing a better process understanding. The Research Questions were stipulated to be answered after the results analysis and the Search Strings, which allowed to select publications that satisfy the objective of this research articles in main digital repositories, all carried out in the last 5 years. After applying filters, 22 articles were considered more relevant according to the research objective. The results show the activities, modalities, methodologies, accomplished pedagogical concepts and the conclusions obtained. This work aims to assist researchers who seek information on referred topic.","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"203 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76652867","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 : 2020-10-21DOI: 10.1109/FIE44824.2020.9274129
I. T. Sanusi, S. Oyelere
This research Full paper presents the pedagogies of machine learning in K-12. The new learning pedagogies and technologies are introduced with the aim of enhancing student engagement, experience and learning outcome. This study examined how machine learning has been taught in the recent past and further explores the ways and suitable approaches for K-12 context. Literatures on pedagogies associated with machine learning were reviewed to understand the dynamics and suitability of these pedagogies to support machine learning teaching. Though studies have explored pedagogies for machine learning in higher education context, few studies explored pedagogical strategies for teaching machine learning in K-12. In all, the pedagogies employed in teaching and learning of machine learning has not witnessed much research in literature. The pedagogical strategies revealed in the literature are mostly adopted in the higher education institutions to enable the of teaching machine learning concepts. The literature survey revealed several pedagogical strategies such as problem-based learning, project-based learning and collaborative learning used in higher education institutions. The revealed pedagogies suggest learners-centered approaches such as active learning, inquiry-based, participatory learning, design-oriented learning among others will be suitable for teaching machine learning in K-12 settings.
{"title":"Pedagogies of Machine Learning in K-12 Context","authors":"I. T. Sanusi, S. Oyelere","doi":"10.1109/FIE44824.2020.9274129","DOIUrl":"https://doi.org/10.1109/FIE44824.2020.9274129","url":null,"abstract":"This research Full paper presents the pedagogies of machine learning in K-12. The new learning pedagogies and technologies are introduced with the aim of enhancing student engagement, experience and learning outcome. This study examined how machine learning has been taught in the recent past and further explores the ways and suitable approaches for K-12 context. Literatures on pedagogies associated with machine learning were reviewed to understand the dynamics and suitability of these pedagogies to support machine learning teaching. Though studies have explored pedagogies for machine learning in higher education context, few studies explored pedagogical strategies for teaching machine learning in K-12. In all, the pedagogies employed in teaching and learning of machine learning has not witnessed much research in literature. The pedagogical strategies revealed in the literature are mostly adopted in the higher education institutions to enable the of teaching machine learning concepts. The literature survey revealed several pedagogical strategies such as problem-based learning, project-based learning and collaborative learning used in higher education institutions. The revealed pedagogies suggest learners-centered approaches such as active learning, inquiry-based, participatory learning, design-oriented learning among others will be suitable for teaching machine learning in K-12 settings.","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"31 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74824172","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 : 2020-01-01DOI: 10.1109/FIE44824.2020.9274045
Julie Henry, Bruno Dumas
{"title":"Approach to Develop a Concept Inventory Informing Teachers of Novice Programmers' Mental Models","authors":"Julie Henry, Bruno Dumas","doi":"10.1109/FIE44824.2020.9274045","DOIUrl":"https://doi.org/10.1109/FIE44824.2020.9274045","url":null,"abstract":"","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"75 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80516498","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/FIE43999.2019.9028653
K. Amanullah
Teaching programming to school children is a challenging task. This report summarizes the research problem, and outlines the research questions around the issues with block-based languages and suitability of elementary patterns as a possible solution. Results to date show a little use of important programming elements even after many years of use, and there is no clear sign of progression in skills with or without remixing.
{"title":"Teaching Programming to School Children Using Elementary Patterns","authors":"K. Amanullah","doi":"10.1109/FIE43999.2019.9028653","DOIUrl":"https://doi.org/10.1109/FIE43999.2019.9028653","url":null,"abstract":"Teaching programming to school children is a challenging task. This report summarizes the research problem, and outlines the research questions around the issues with block-based languages and suitability of elementary patterns as a possible solution. Results to date show a little use of important programming elements even after many years of use, and there is no clear sign of progression in skills with or without remixing.","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"58 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73565697","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/FIE43999.2019.9028577
S. Malla, Jing Wang, William E. Hendrix, Kenneth J. Christensen
In this Work in Progress Innovative Practice paper, we describe a process for finding predictors for student success – and failure – for Computer Science and Computer Engineering students with a focus on the second programming course (CS2). We use readily available off-the-shelf statistical and data mining tools for generating summary statistics, calculating correlations, testing statistical significance, and creating decision trees. We analyze grade data from the first programming course (CS1), entry-level STEM courses (Calculus and Physics), and an English course to determine success predictors for CS2. Not surprisingly, the grade in CS1 is the best predictor for success in CS2. We also find that success in CS2 is independent of gender. Looking deeper into the data, we find characteristics of students who are very likely to pass or fail CS2. Being able to identify predictors for success is useful for calibrating admission criteria and designing appropriate interventions (e.g., requiring prereq classes, recitation sessions, and so on) to improve success probability for all students. A key contribution of this paper is a step-by-step process that can be used by other programs to find success predictors and design appropriate interventions.
{"title":"Predicting Success for Computer Science Students in CS2 using Grades in Previous Courses","authors":"S. Malla, Jing Wang, William E. Hendrix, Kenneth J. Christensen","doi":"10.1109/FIE43999.2019.9028577","DOIUrl":"https://doi.org/10.1109/FIE43999.2019.9028577","url":null,"abstract":"In this Work in Progress Innovative Practice paper, we describe a process for finding predictors for student success – and failure – for Computer Science and Computer Engineering students with a focus on the second programming course (CS2). We use readily available off-the-shelf statistical and data mining tools for generating summary statistics, calculating correlations, testing statistical significance, and creating decision trees. We analyze grade data from the first programming course (CS1), entry-level STEM courses (Calculus and Physics), and an English course to determine success predictors for CS2. Not surprisingly, the grade in CS1 is the best predictor for success in CS2. We also find that success in CS2 is independent of gender. Looking deeper into the data, we find characteristics of students who are very likely to pass or fail CS2. Being able to identify predictors for success is useful for calibrating admission criteria and designing appropriate interventions (e.g., requiring prereq classes, recitation sessions, and so on) to improve success probability for all students. A key contribution of this paper is a step-by-step process that can be used by other programs to find success predictors and design appropriate interventions.","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"16 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73891260","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/FIE43999.2019.9028691
Candido Cabo
In this research to practice full paper we quantified progress in the ability of first-year students (n=54) to solve problems using computer programming control structures with different levels of complexity like sequencing, selection (if/else) and repetition (for/while). Students used both a flowchart interpreter and Python to write programs. We found that 70% of students could solve problems involving a sequence of statements (i.e. without the use of selection or repetition) using a flowchart interpreter or Python. The majority of the students who could not solve sequencing problems were not successful at solving problems involving selection and repetition (69% using flowcharts and 94% using Python). On the other hand, of the students who could solve sequencing problems 45% (flowchart) and 71% (Python) were able to solve problems involving selection and repetition. Therefore, the ability to solve problems involving a sequence of statements is a good early predictor of success/failure in solving problems with more complicated control structures like selection and repetition. Success in solving computer programming problems depends on the tool used for $sim37$% of students. Therefore, the ability of students to transfer problem solving abilities between tools (from flowcharting to Python) is not automatic.
{"title":"Student Progress in Learning Computer Programming: Insights from Association Analysis","authors":"Candido Cabo","doi":"10.1109/FIE43999.2019.9028691","DOIUrl":"https://doi.org/10.1109/FIE43999.2019.9028691","url":null,"abstract":"In this research to practice full paper we quantified progress in the ability of first-year students (n=54) to solve problems using computer programming control structures with different levels of complexity like sequencing, selection (if/else) and repetition (for/while). Students used both a flowchart interpreter and Python to write programs. We found that 70% of students could solve problems involving a sequence of statements (i.e. without the use of selection or repetition) using a flowchart interpreter or Python. The majority of the students who could not solve sequencing problems were not successful at solving problems involving selection and repetition (69% using flowcharts and 94% using Python). On the other hand, of the students who could solve sequencing problems 45% (flowchart) and 71% (Python) were able to solve problems involving selection and repetition. Therefore, the ability to solve problems involving a sequence of statements is a good early predictor of success/failure in solving problems with more complicated control structures like selection and repetition. Success in solving computer programming problems depends on the tool used for $sim37$% of students. Therefore, the ability of students to transfer problem solving abilities between tools (from flowcharting to Python) is not automatic.","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"5 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75649193","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/FIE43999.2019.9028602
Noah B. Salzman, Donald J. Winiecki, Amit Jain
Sense of community and belonging represent key components of students’ success in undergraduate degree programs. As part of a curriculum effort designed to strengthen these components, we conducted a Social Network Analysis of students in an undergraduate computer science program to establish a baseline measurement of students’ connectedness in the program. Analysis of these data showed no significant differences in connectedness between male and female students or white and non-white students. The analysis did identify significant differences based on students’ class year, interest and participation in computer games, and being employed by the computer science department.
{"title":"Assessing Community in an Undergraduate Computer Science Program Using Social Network Analysis","authors":"Noah B. Salzman, Donald J. Winiecki, Amit Jain","doi":"10.1109/FIE43999.2019.9028602","DOIUrl":"https://doi.org/10.1109/FIE43999.2019.9028602","url":null,"abstract":"Sense of community and belonging represent key components of students’ success in undergraduate degree programs. As part of a curriculum effort designed to strengthen these components, we conducted a Social Network Analysis of students in an undergraduate computer science program to establish a baseline measurement of students’ connectedness in the program. Analysis of these data showed no significant differences in connectedness between male and female students or white and non-white students. The analysis did identify significant differences based on students’ class year, interest and participation in computer games, and being employed by the computer science department.","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"117 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":"75866201","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/FIE43999.2019.9028659
Timo Vasankari, C. I. Sedano, E. Sutinen
Technology industry with sufficient ability and investment in research, development and innovation is the core of successful growth of several national economies. Higher education institutions need to provide a wide range of engineers with an increasing level of cross-disciplinary skills. An active, local presence of a university providing degree programs and research in technology has a strong link with the volume and development of the technology industry in the region. While modern technologies, especially information and communication technology (ICT), allow research and development activities to be independent of the location, other sectors of technology industry, such as maritime industry, require significant investments in physical facilities, bound to the region. Governments face two main challenges to maintain their industry’s engineering competitiveness: (1) to prepare engineering graduates across expanding ranges of skills and knowledge, and (2) to provide the engineers in the regions where these skills are needed. Southwestern Finland is a region with the fastest growing technology industry in the country. Without a university with a full range of opportunities for academic engineering education in the region, there is an increasing number of job openings that cannot be filled. In Finland, students in engineering connect with the industry already during their studies and, after graduation, tend to stay in the region where they studied. To expose engineering students to the challenges of Southwestern Finland without creating new permanent higher education engineering structures in the region, the Finnish government in 2017 launched FITech, a 5-year project including all the seven Finnish universities offering engineering education. The purpose of FITech is to offer a nationwide cooperative solution to coordinate and provide engineering education and research in all domains of engineering to support the growth of Southwestern Finland region. This paper introduces the foundations and objectives of FITech and using the early indications of its results suggests a framework to evaluate the success of this complex nationwide project. Preliminary outcomes from the perspective of the University of Turku, one of the seven FITech partners, utilizing an adapted version of Hevner et al design science framework, show the challenges in achieving sufficient regional impact by the FITech approach and provide guidance for its ongoing development and implementation.
{"title":"Filling an engineering specialist void: FITech University Network - a cooperative education initiative to support the growth of Southwestern Finland","authors":"Timo Vasankari, C. I. Sedano, E. Sutinen","doi":"10.1109/FIE43999.2019.9028659","DOIUrl":"https://doi.org/10.1109/FIE43999.2019.9028659","url":null,"abstract":"Technology industry with sufficient ability and investment in research, development and innovation is the core of successful growth of several national economies. Higher education institutions need to provide a wide range of engineers with an increasing level of cross-disciplinary skills. An active, local presence of a university providing degree programs and research in technology has a strong link with the volume and development of the technology industry in the region. While modern technologies, especially information and communication technology (ICT), allow research and development activities to be independent of the location, other sectors of technology industry, such as maritime industry, require significant investments in physical facilities, bound to the region. Governments face two main challenges to maintain their industry’s engineering competitiveness: (1) to prepare engineering graduates across expanding ranges of skills and knowledge, and (2) to provide the engineers in the regions where these skills are needed. Southwestern Finland is a region with the fastest growing technology industry in the country. Without a university with a full range of opportunities for academic engineering education in the region, there is an increasing number of job openings that cannot be filled. In Finland, students in engineering connect with the industry already during their studies and, after graduation, tend to stay in the region where they studied. To expose engineering students to the challenges of Southwestern Finland without creating new permanent higher education engineering structures in the region, the Finnish government in 2017 launched FITech, a 5-year project including all the seven Finnish universities offering engineering education. The purpose of FITech is to offer a nationwide cooperative solution to coordinate and provide engineering education and research in all domains of engineering to support the growth of Southwestern Finland region. This paper introduces the foundations and objectives of FITech and using the early indications of its results suggests a framework to evaluate the success of this complex nationwide project. Preliminary outcomes from the perspective of the University of Turku, one of the seven FITech partners, utilizing an adapted version of Hevner et al design science framework, show the challenges in achieving sufficient regional impact by the FITech approach and provide guidance for its ongoing development and implementation.","PeriodicalId":6700,"journal":{"name":"2019 IEEE Frontiers in Education Conference (FIE)","volume":"30 3 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":"73189795","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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