Computer science is a broad discipline, and computer scientists often disagree about the content, form, and practices of the discipline. The processes through which computer scientists create, maintain, and modify knowledge in computer science---processes which often are eclectic and anarchistic---are well researched, but knowledge of those processes is generally not considered to be a part of computer science. On the contrary, I argue that understanding of how computer science works is an important part of the knowledge of an educated computer scientist. In this paper I discuss some characteristics of computer science that are central to understanding how computer science works.
{"title":"The development of computer science: a sociocultural perspective","authors":"M. Tedre","doi":"10.1145/1315803.1315808","DOIUrl":"https://doi.org/10.1145/1315803.1315808","url":null,"abstract":"Computer science is a broad discipline, and computer scientists often disagree about the content, form, and practices of the discipline. The processes through which computer scientists create, maintain, and modify knowledge in computer science---processes which often are eclectic and anarchistic---are well researched, but knowledge of those processes is generally not considered to be a part of computer science. On the contrary, I argue that understanding of how computer science works is an important part of the knowledge of an educated computer scientist. In this paper I discuss some characteristics of computer science that are central to understanding how computer science works.","PeriodicalId":135065,"journal":{"name":"Baltic Sea '06","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116379468","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}
A source code plagiarism detection engine Plaggie is presented. It is a stand-alone Java application that can be used to check Java programming exercises. Plaggie's functionality is similar with previously published JPlag web service but unlike JPlag, Plaggie must be installed locally and its source code is open. Apparently, Plaggie is the only open-source plagiarism detection engine for Java exercises.
{"title":"Plaggie: GNU-licensed source code plagiarism detection engine for Java exercises","authors":"Aleksi Ahtiainen, Sami Surakka, Mikko Rahikainen","doi":"10.1145/1315803.1315831","DOIUrl":"https://doi.org/10.1145/1315803.1315831","url":null,"abstract":"A source code plagiarism detection engine Plaggie is presented. It is a stand-alone Java application that can be used to check Java programming exercises. Plaggie's functionality is similar with previously published JPlag web service but unlike JPlag, Plaggie must be installed locally and its source code is open. Apparently, Plaggie is the only open-source plagiarism detection engine for Java exercises.","PeriodicalId":135065,"journal":{"name":"Baltic Sea '06","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126788842","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}
This paper critically enquires into the value systems which rule the activities of teaching and research. This critique is intended to demonstrate the application of critical enquiry in Computer Science Education Research and therefore uses critical theory as a method of analysis. A framework of Research as a Discourse is applied to explore how the notions of research as opposed to teaching are presented, and how discipline and research communities are sustained. The concept of a discourse, based upon the work of Foucault, enables critical insight into the processes which regulate forms of thought. This paper positions the field of Computer Science Education Research, as an illustrative case, within the broader discourse of Research, and argues that Computer Science Education Researchers and educators need to understand and engage in this discourse and shape it to their own ends.
{"title":"Valuing computer science education research?","authors":"T. Clear","doi":"10.1145/1315803.1315806","DOIUrl":"https://doi.org/10.1145/1315803.1315806","url":null,"abstract":"This paper critically enquires into the value systems which rule the activities of teaching and research. This critique is intended to demonstrate the application of critical enquiry in Computer Science Education Research and therefore uses critical theory as a method of analysis.\u0000 A framework of Research as a Discourse is applied to explore how the notions of research as opposed to teaching are presented, and how discipline and research communities are sustained. The concept of a discourse, based upon the work of Foucault, enables critical insight into the processes which regulate forms of thought.\u0000 This paper positions the field of Computer Science Education Research, as an illustrative case, within the broader discourse of Research, and argues that Computer Science Education Researchers and educators need to understand and engage in this discourse and shape it to their own ends.","PeriodicalId":135065,"journal":{"name":"Baltic Sea '06","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123497485","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}
Programming is a challenging field of computer science for both to teach and learn. Although studied extensively, a definite method for teaching programming is yet to be found. In quest of finding success factors in both elementary and more advanced programming courses, this paper discusses some findings made studying exam success and home assignment activity in programming courses. Our claim is that there is no shortcut in learning to program, but extensive practise and sufficient time to become familiar with programming concepts is needed.
{"title":"Learning programming by programming: a case study","authors":"M. Hassinen, Hannu Mäyrä","doi":"10.1145/1315803.1315824","DOIUrl":"https://doi.org/10.1145/1315803.1315824","url":null,"abstract":"Programming is a challenging field of computer science for both to teach and learn. Although studied extensively, a definite method for teaching programming is yet to be found. In quest of finding success factors in both elementary and more advanced programming courses, this paper discusses some findings made studying exam success and home assignment activity in programming courses. Our claim is that there is no shortcut in learning to program, but extensive practise and sufficient time to become familiar with programming concepts is needed.","PeriodicalId":135065,"journal":{"name":"Baltic Sea '06","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123119069","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}
Supporting creativity is a learning objective in general school education. In computer science the creation of products plays an important role. We want to find out the impact of creative action in computer science education classes on task motivation and understanding of computer science concepts. Underlying elements and environments of the creative process are analyzed by interviewing creative students. First results indicate creativity as an important factor for motivation. Furthermore the use of a concept we call "building blocks" helps to achieve creative products.
{"title":"Creative students: what can we learn from them for teaching computer science?","authors":"R. Romeike","doi":"10.1145/1315803.1315835","DOIUrl":"https://doi.org/10.1145/1315803.1315835","url":null,"abstract":"Supporting creativity is a learning objective in general school education. In computer science the creation of products plays an important role. We want to find out the impact of creative action in computer science education classes on task motivation and understanding of computer science concepts. Underlying elements and environments of the creative process are analyzed by interviewing creative students. First results indicate creativity as an important factor for motivation. Furthermore the use of a concept we call \"building blocks\" helps to achieve creative products.","PeriodicalId":135065,"journal":{"name":"Baltic Sea '06","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134114920","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}
This paper introduces progress reports and describes how these can be used as a tool to evaluate student learning and understanding during programming courses. A progress report includes a short piece of program code (on paper), covering topics recently introduced in the course, and four questions. The two first questions are of a "trace and explain" type, asking the students to describe the functionality of the program using their own words - both line by line and as a whole. The two other questions aim at gaining insight into the students' own opinions of their learning, as they are asked to write down what they think they have learned so far in the course and what they have experienced as most difficult. Results from using progress reports in an introductory programming course at secondary level are presented. The responses to the "trace and explain" questions were categorized based on the level of overall understanding manifested. We also analyzed students' understanding of individual programming concepts, both based on their code explanations and on their own opinions on what they had experienced as difficult. Our initial experience from using the progress reports is positive, as we feel that they provide valuable information during the course, which most likely would remain uncovered otherwise.
{"title":"Progress reports and novices' understanding of program code","authors":"Linda Mannila","doi":"10.1145/1315803.1315810","DOIUrl":"https://doi.org/10.1145/1315803.1315810","url":null,"abstract":"This paper introduces progress reports and describes how these can be used as a tool to evaluate student learning and understanding during programming courses. A progress report includes a short piece of program code (on paper), covering topics recently introduced in the course, and four questions. The two first questions are of a \"trace and explain\" type, asking the students to describe the functionality of the program using their own words - both line by line and as a whole. The two other questions aim at gaining insight into the students' own opinions of their learning, as they are asked to write down what they think they have learned so far in the course and what they have experienced as most difficult.\u0000 Results from using progress reports in an introductory programming course at secondary level are presented. The responses to the \"trace and explain\" questions were categorized based on the level of overall understanding manifested. We also analyzed students' understanding of individual programming concepts, both based on their code explanations and on their own opinions on what they had experienced as difficult. Our initial experience from using the progress reports is positive, as we feel that they provide valuable information during the course, which most likely would remain uncovered otherwise.","PeriodicalId":135065,"journal":{"name":"Baltic Sea '06","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131434895","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}
The question of which language to use in introductory programming has been cause for protracted debate, often based on emotive opinions. Several studies on the benefits of individual languages or comparisons between two languages have been conducted, but there is still a lack of objective data used to inform these comparisons. This paper presents a list of criteria based on design decisions used by prominent teaching-language creators. The criteria, once justified, are then used to compare eleven languages which are currently used in introductory programming courses. Recommendations are made on how these criteria can be used or adapted for different situations.
{"title":"An objective comparison of languages for teaching introductory programming","authors":"Linda Mannila, Michael de Raadt","doi":"10.1145/1315803.1315811","DOIUrl":"https://doi.org/10.1145/1315803.1315811","url":null,"abstract":"The question of which language to use in introductory programming has been cause for protracted debate, often based on emotive opinions. Several studies on the benefits of individual languages or comparisons between two languages have been conducted, but there is still a lack of objective data used to inform these comparisons. This paper presents a list of criteria based on design decisions used by prominent teaching-language creators. The criteria, once justified, are then used to compare eleven languages which are currently used in introductory programming courses. Recommendations are made on how these criteria can be used or adapted for different situations.","PeriodicalId":135065,"journal":{"name":"Baltic Sea '06","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121641960","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}
The degree requirements of American institutions offering top-level graduate programs in computer science were analyzed. The sample size was 59 institutions for undergraduate and 32 for graduate programs. The purpose was to solve which courses were the most commonly offered in the specializations of Artificial Intelligence, Computer Systems, and Theory. The results can be useful to professors who are responsible for any of these three specializations.
{"title":"Most common courses of specializations in artificial intelligence, computer systems, and theory","authors":"Sami Surakka","doi":"10.1145/1315803.1315815","DOIUrl":"https://doi.org/10.1145/1315803.1315815","url":null,"abstract":"The degree requirements of American institutions offering top-level graduate programs in computer science were analyzed. The sample size was 59 institutions for undergraduate and 32 for graduate programs. The purpose was to solve which courses were the most commonly offered in the specializations of Artificial Intelligence, Computer Systems, and Theory. The results can be useful to professors who are responsible for any of these three specializations.","PeriodicalId":135065,"journal":{"name":"Baltic Sea '06","volume":"46 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132692983","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}
Visual algorithm simulation exercises test student knowledge of different algorithms by making them trace the steps of how a given algorithm would have manipulated a set of input data. When assessing such exercises the main difference between a human assessor and an automated assessment procedure is the human ability to adapt to the possible errors made by the student. A human assessor can continue past the point where the model solution and the student solution deviate and make a hypothesis on the source of the error based on the student's answer. Our goal is to bring some of that ability to automated assessment. We anticipate that providing better feedback on student errors might help reduce persistent misconceptions. The method described tries to automatically recreate erroneous student behavior by introducing a set of code mutations on the original algorithm code. The available mutations correspond to different careless errors and misconceptions held by the student. The results show that such automatically generated "misconceived" algorithms can explain much of the student behavior found in erroneous solutions to the exercise. Non-systematic mutations can also be used to simulate slips which greatly reduces the number of erroneous solutions without explanations.
{"title":"Modelling student behavior in algorithm simulation exercises with code mutation","authors":"O. Seppälä","doi":"10.1145/1315803.1315822","DOIUrl":"https://doi.org/10.1145/1315803.1315822","url":null,"abstract":"Visual algorithm simulation exercises test student knowledge of different algorithms by making them trace the steps of how a given algorithm would have manipulated a set of input data. When assessing such exercises the main difference between a human assessor and an automated assessment procedure is the human ability to adapt to the possible errors made by the student. A human assessor can continue past the point where the model solution and the student solution deviate and make a hypothesis on the source of the error based on the student's answer. Our goal is to bring some of that ability to automated assessment. We anticipate that providing better feedback on student errors might help reduce persistent misconceptions.\u0000 The method described tries to automatically recreate erroneous student behavior by introducing a set of code mutations on the original algorithm code. The available mutations correspond to different careless errors and misconceptions held by the student.\u0000 The results show that such automatically generated \"misconceived\" algorithms can explain much of the student behavior found in erroneous solutions to the exercise. Non-systematic mutations can also be used to simulate slips which greatly reduces the number of erroneous solutions without explanations.","PeriodicalId":135065,"journal":{"name":"Baltic Sea '06","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131049486","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}
In recent years a small number of web-based tools have been proposed to help students learn to write SQL query statements and also to assess students' SQL writing skills. SQLify is a new SQL teaching and assessment tool that extends the current state-of-the-art by incorporating peer review and enhanced automatic assessment based on database theory to produce more comprehensive feedback to students. SQLify is intended to yield a richer learning experience for students and reduce marking load for instructors. In this paper SQLify is compared with existing tools and important new features are demonstrated.
{"title":"Do students SQLify? improving learning outcomes with peer review and enhanced computer assisted assessment of querying skills","authors":"Michael de Raadt, Stijn Dekeyser, Tien Yu Lee","doi":"10.1145/1315803.1315821","DOIUrl":"https://doi.org/10.1145/1315803.1315821","url":null,"abstract":"In recent years a small number of web-based tools have been proposed to help students learn to write SQL query statements and also to assess students' SQL writing skills. SQLify is a new SQL teaching and assessment tool that extends the current state-of-the-art by incorporating peer review and enhanced automatic assessment based on database theory to produce more comprehensive feedback to students. SQLify is intended to yield a richer learning experience for students and reduce marking load for instructors. In this paper SQLify is compared with existing tools and important new features are demonstrated.","PeriodicalId":135065,"journal":{"name":"Baltic Sea '06","volume":"359 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122074983","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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