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| 2. |
- Vilhelmsson, Andreas, et al.
(författare)
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Ingen har koll på biverkningarna
- 2014
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Ingår i: Svenska Dagbladet. - 1101-2412.
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Tidskriftsartikel (populärvet., debatt m.m.)
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| 3. |
- Eklund, Patrik, et al.
(författare)
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Preprocessing perceptrons and multivariate reference values
- 2009
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Ingår i: Data mining and medical knowledge management. - : Medical Information Science Reference. - 9781605662183 - 1605662186 - 9781605662190 ; , s. 108-121
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Classification networks, consisting of preprocessing layers combined with well-known classification networks, are well suited for medical data analysis. Additionally, by adjusting network complexity to corresponding complexity of data, the parameters in the preprocessing network can, in comparison with networks of higher complexity, be more precisely understood and also effectively utilised as decision limits. Further, a multivariate approach to preprocessing is shown in many cases to increase correctness rates in classification tasks. Handling network complexity in this way thus leads to efficient parameter estimations as well as useful parameter interpretations.
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| 4. |
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| 5. |
- Börstler, Jürgen, 1960-, et al.
(författare)
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Evaluating OO Example Programs for CS1
- 2008
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Ingår i: Proceedings of the 13th annual conference on Innovation and technology in computer science education. - New York, NY, USA : ACM. ; , s. 47-52
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Konferensbidrag (refereegranskat)
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| 6. |
- Börstler, Jürgen, 1960-, et al.
(författare)
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Transitioning to OOP/Java : A never ending story
- 2008
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Ingår i: Reflections on the teaching of programming. - Berlin, Heidelberg : Springer. - 9783540779339 ; , s. 80-97
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Bokkapitel (refereegranskat)
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| 7. |
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| 8. |
- Eliasson, Johan, et al.
(författare)
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Investigating students' confidence in programming and problem solving
- 2006
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Ingår i: 36th ASEE/IEEE Frontiers in Education Conference (FIE2006). ; , s. M4E-22
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Konferensbidrag (refereegranskat)abstract
- Many students feel insecure making their first attempts to solve programming problems. Despite finishing the introductory programming course successfully, these students refrain from pursuing their CS studies. Hence, this aversion towards problem solving and programming is not fully explained by lack of subject understanding and performance. In order to better understand the components of students’ comfort, a first attempt to model a student’s confidence regarding problem solving and programming has been made. The model consists of two dimensions; Course topic and Student’s mindset. Two questionnaires have been developed in order to capture if and how students’ confidence is affected by taking the CS1 course. Data has been collected for four course offerings with three different study programmes. Results confirm the suspicion that the confidence is lowered by the course, and that student groups with different ambition and motivation for taking the course seem to be affected by different aspects of the course.
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| 9. |
- Nordström, Marie, et al.
(författare)
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SI - Small Scale Advantages
- 2006
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Not being part of a larger SI-organisation has both advantages and disadvantages. In this paper we try to illustrate the advantages of doing SI small scale. In a large scale SI-organisation the supervisors are often not teachers themselves and/or not familiar with the practices of a specific course. To have teaching staff supervising a SIproject completely focused on one course is favourable in many ways. The decision to introduce SI was taken by the department of Computing Science to support the students at the introductory course in object oriented programming. This course demands a high level of abstract thinking and is very heavy on many of the new students majoring in computing science. In 2002 we started off with a general training course for eight new SI-leaders, but soon discovered that much could be gained from making the training specifically working with the course at hand.Working with the course material in the training course makes it possible to use all ideas, experiences, and material developed during the training directly in the SI-meetings. The SI-leaders can prepare their information to the students; they can even start planning their first meeting. Another import part of the training is simulated SI-meetings. We “stage” them to illustrate different aspects of group dynamics and to control that not all problematic situations happen at one single meeting. Supervisor meetings are another important component of a SI-project. They benefit from small scale since they provide an excellent possibility for feedback and exchange of ideas if the supervisors themselves have experience in teaching the actual course. Working in small scale with one specific course also makes it possible to refine the study strategies and techniques used in the SI-meetings. The course is heavily targeted towards problem solving and this has influenced the meetings in different ways. For example it is not uncommon to let some of the students try out ideas in the computer labs and report back to the SI-group during a meeting. Results from six projects finished since 2002 will be presented and discussed. We have seen that the performance and grades are higher in the group attending SI and we are currently doing follow ups to check the retention rate.
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| 10. |
- Kallin Westin, Lena
(författare)
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Missing data and the preprocessing perceptron
- 2004
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this paper, several ways to handle missing data, e.g. removing cases, mean imputation, and multiple imputation, are described and discussed. The Pima-Indians-Diabetes data set is used as a case study. This particular data set is interesting to use since it has not been obvious to all users that it actually contains a substantial amount of missing data. The data set is described in detail and the methods for coping with missing data mentioned in the text is applied on the data set.The preprocessing perceptron is used to train decision support systems on the data sets. A sketch of a way to impute missing data using the preprocessing perceptron is also proposed and discussed. The accuracy of the trained decision support systems, at the optimal efficiency point, lied in the interval 76-82% for the different methods. The highest values were obtained when all missing data cases were removed both from the test and the training set. This is, however, not a good way to handle missing data since the resulting decision support system is biased. Furthermore it will not be able to handle missing data when used on real data in the future. The results of the remaining methods were surprisingly similar, a reason for this might be that the data set used is rather large. Differences between methods would probably be larger in a smaller data set with larger amount of missing data.
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