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  • 1.
    Anttila, L.
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Eklund, Patrik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Kallin, Lena
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Koskinen, P.
    Penttilä, T.-A.
    The Generalised Preprocessing Perceptron for Medical Data Analysis: A Case Study for the Polycystic Ovary Syndrome1996Ingår i: Cybernetics and Systems '96: Proceedings of the 13th European Meeting on Cybernetics and Systems Research / [ed] Robert Trappl, 1996, s. 597-602Konferensbidrag (Övrigt vetenskapligt)
  • 2.
    Börstler, Jurgen
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Christensen, Henrik B
    Nordström, Marie
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Kallin Westin, Lena
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Moström, Jan Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Caspersen, Michael E
    Evaluating OO Example Programs for CS12008Ingår i: Proceedings of the 13th annual conference on Innovation and technology in computer science education, 2008, s. 47-52Konferensbidrag (Refereegranskat)
  • 3.
    Börstler, Jürgen
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Nordström, Marie
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Kallin Westin, Lena
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Moström, Jan Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Christensen, Henrik B.
    Bennedsen, Jens
    An Evaluation Instrument for Object-Oriented Example Programs for Novices2008Rapport (Övrigt vetenskapligt)
  • 4.
    Börstler, Jürgen
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Nordström, Marie
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Kallin Westin, Lena
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Moström, Jan Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Eliasson, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Transitioning to OOP/Java: A never ending story2008Ingår i: Reflections on the teaching of programming, Springer , 2008, s. 80-97Kapitel i bok, del av antologi (Övrigt vetenskapligt)
  • 5.
    Eklund, Patrik
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Kallin, Lena
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Selén, Gustaf
    Dept. of Computer Science, Åbo Akademi University.
    Computational intelligence for medical information analysis and refinement1999Ingår i: Soft computing in human-related sciences / [ed] Horia Nicolai Teodorescu, Abraham Kandel and Lakhmi C Jain, Boca Raton, FL: CRC Press , 1999, s. 115-134Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    In this chapter we will describe an architecture of a workstation that combines data mining tools with analysis tools in building new decision support systems and automatically integrates the new system into a diagnosis classification skeleton. The workstation is intended to support fast and efficient navigation towards a correct diagnosis and consists of modules each capable of supporting the diagnosis of specific diseases. The production line within which domain experts can generate decision support systems for a specific disease themselves will be described together with one of the analysis tools used in the production line, the generalised preprocessing perceptron (GPP). The GPP has a structure that allows results comparable to a multilayer perceptron, yet perceiving parameter interpretations relevant within the medical domain.

  • 6.
    Eklund, Patrik
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Kallin Westin, Lena
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Preprocessing perceptrons and multivariate reference values2009Ingår i: Data mining and medical knowledge management: cases and applications / [ed] Petra Berka, Jan Rauch, and Djamel Abdelkader Zighed, Medical Information Science Reference , 2009, s. 108-121Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    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.

  • 7.
    Eliasson, Johan
    et al.
    Umeå universitet, Teknisk-naturvetenskaplig fakultet, Datavetenskap.
    Kallin Westin, Lena
    Umeå universitet, Teknisk-naturvetenskaplig fakultet, Datavetenskap.
    Nordström, Marie
    Umeå universitet, Teknisk-naturvetenskaplig fakultet, Datavetenskap.
    Investigating students' change of confidence during CS1 - four case studies2006Rapport (Övrigt vetenskapligt)
  • 8.
    Eliasson, Johan
    et al.
    Umeå universitet, Teknisk-naturvetenskaplig fakultet, Datavetenskap.
    Kallin Westin, Lena
    Umeå universitet, Teknisk-naturvetenskaplig fakultet, Datavetenskap.
    Nordström, Marie
    Umeå universitet, Teknisk-naturvetenskaplig fakultet, Datavetenskap.
    Investigating students' confidence in programming and problem solving2006Ingår i: 36th ASEE/IEEE Frontiers in Education Conference (FIE2006), 2006, s. M4E-22Konferensbidrag (Refereegranskat)
    Abstract [en]

    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.

  • 9.
    Kallin, Lena
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Studenters åsikter om studiemiljön vid Institutionen för datavetenskap1999Rapport (Övrigt vetenskapligt)
    Abstract [sv]

    En enkät har skickats ut till 152 studenter som läst/läser Teknisk Datavetenskap eller Datavetenskapligaprogrammet vid Institutionen för datavetenskap i Umeå under åren 1992-1998. Studenternautfrågades om sin bakgrund, hur undervisningen fungerar, hur man ser på sin socialastudiemiljö, hur man upplevt övergången mellan universitet och gymnasium och hur man ser påden fysiska arbetsmiljön. De som avslutat sin utbildning utfrågades också om hur de uppleverarbetslivet.Svarsfrekvensen var låg (35 procent) och därför hamnar fokus på en beskrivande sammanfattningav svaren snarare än på statistiska slutsatser utifrån svaren. Svaren har sammanfattatsoch diskuterats utifrån aspekter på kön, ålder och programtillhörighet. Rapporten avslutas medett antal förslag på åtgärder som kan förbättra studiemiljön.

  • 10.
    Kallin, Lena
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Nordström, Marie
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Vad ska vi kunna det här för: kommer det på tentan?2001Ingår i: Lärarens liv - vision och verklighet: Konferensrapport från Universitetspedagogisk konferens in Umeå, February 15-16, 2001, 2001, s. 141-156Konferensbidrag (Refereegranskat)
    Abstract [sv]

    Vi upplever att studenternas attityd till studier och kunskap har förändrats. I mycket högre utsträckning än tidigare tvingas vi, vilket kan kännas både positivt och negativt, som lärare att motivera studenterna. Dels måste den aktuella kursen ges ett berättigande och dels måste enskilda teoriavsnitt troliggöras med konkreta exempel. I uppsatsen försöker vi identifiera orsaker till och effekter av denna attitydförändring.

  • 11.
    Kallin, Lena
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Nordström, Marie
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Palmquist, Lena
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Att utbilda tjejer i datavetenskap:  erfarenheter och reflexioner1997Ingår i: Utbildning i förändring: Konferensrapport från Universitetspedagogisk konferens i Umeå 20-21 februari 1997 / [ed] Berit Bylund, 1997, s. 308-321Konferensbidrag (Refereegranskat)
    Abstract [sv]

    Vad beror det på att datavetenskap är det ämne som lockar den minsta andelen kvinnor av alla naturvetenskapliga utbildningar? Varför ses teknik som ett manligt område? Hur ser de kvinnliga studenterna på undervisningen och miljön vid institutionen? Finns det en skillnad på hur kvinnor och män närmar sig datorer och kunskap? Detta är några av de frågor som vi ställer oss i artikeln. Vi försöker ge en bakgrund till den nuvarande situationen och bidrar med frågeställningar i syfte att skapa debatt och diskussion kring dessa. Vi redovisar också kort en enkät som kvinnliga studenter på institutionen besvarat och diskuterar några av de åtgärder som har genomförts eller kommer att genomföras vid institutionen.

  • 12.
    Kallin, Lena
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Räty, R.
    Selén, G.
    Spencer, K.
    A Comparison of Numerical Risk Computational Techniques in Screening for Down's Syndrome1998Ingår i: Industrial Applications of Neural Networks, 1998, s. 425-432Kapitel i bok, del av antologi (Övrigt vetenskapligt)
  • 13.
    Kallin Westin, Lena
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Artificiell intelligens sedd ur ett könsperspektiv2001Ingår i: Från siffror till surfning: könsperspektiv på informationsteknik / [ed] Lena Kallin Westin och Lena Palmquist, Lund: Studentlitteratur , 2001, s. 41-62Kapitel i bok, del av antologi (Övrig (populärvetenskap, debatt, mm))
  • 14.
    Kallin Westin, Lena
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Missing data and the preprocessing perceptron2004Rapport (Övrigt vetenskapligt)
    Abstract [en]

    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.

  • 15.
    Kallin Westin, Lena
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Preprocessing perceptrons2004Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Reliable results are crucial when working with medical decision support systems. A decision support system should be reliable but also be interpretable, i.e. able to show how it has inferred its conclusions. In this thesis, the preprocessing perceptron is presented as a simple but effective and efficient analysis method to consider when creating medical decision support systems. The preprocessing perceptron has the simplicity of a perceptron combined with a performance comparable to the multi-layer perceptron.

    The research in this thesis has been conducted within the fields of medical informatics and intelligent computing. The original idea of the production line as a tool for a domain expert to extract information, build decision support systems and integrate them in the existing system is described. In the introductory part of the thesis, an introduction to feed-forward neural networks and fuzzy logic is given as a background to work with the preprocessing perceptron. Input to a decision support system is crucial and it is described how to gather a data set, decide how many and what kind of inputs to use. Outliers, errors and missing data are covered as well as normalising of the input. Training is done in a backpropagation-like manner where the division of the data set into a training and a test set can be done in several different ways just as the training itself can have variations. Three major groups of methods to estimate the discriminance effect of the preprocessing perceptron are described and a discussion of the trade-off between complexity and approximation strength are included.

    Five papers are presented in this thesis. Case studies are shown where the preprocessing perceptron is compared to multi-layer perceptrons, statistical approaches and other mathematical models. The model is extended to a generalised preprocessing perceptron and the performance of this new model is compared to the traditional feed-forward neural networks. Results concerning the preprocessing layer and its connection to multivariate decision limits are included. The well-known ROC curve is described and introduced fully into the field of computer science as well as the improved curve, the QROC curve. Finally a tutorial to the program trainGPP is presented. It describes how to work with the preprocessing perceptron from the moment when a data file is provided to the moment when a new decision support system is built.

  • 16.
    Kallin Westin, Lena
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Receiver operating characteristic (ROC) analysis: valuating discriminance effects among decision support systems2001Rapport (Övrigt vetenskapligt)
    Abstract [en]

    An overview of the usage of Receiver Operating Characteristic (ROC) analysis within medicine and computer science is given. A survey of the theory behind the analysis is given together with a presentation of how to create the ROC curve and different methods to interpret it. Both methods that rely on binormal distributions and methods that rely on distribution free methods have been mentioned.

    A way to better know the quality of the measurements of sensitivity and specificity is presented. The quality measures and the QROC curve as is also included together with a discussion about optimal cut-offs and the connection to Bayesian decision theory. Results from earlier experiments and case studies will be used to exemplify the use of the ROC and QROC curves.

  • 17.
    Kallin Westin, Lena
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    trainGPP - Users' manual2004Rapport (Övrigt vetenskapligt)
    Abstract [en]

    The generalised preprocessing perceptron (GPP) is a model that has the advantage of few parameters and still good discriminance ability. Still the GPP is very general and the user might need some support when building and training the model. The toolbox trainGPP , written by the author in MATLAB, is described. The toolbox can be provided by the author and is used to create different types of GPP:s and to train them.

    In this paper a tutorial of the different parts of trainGPP is given. It is assumed that the reader is familiar with the GPP and the theory behind it. However, an appendix is included where a presentation of the generalised preprocessing perceptron is given together with descriptions of different kind of GPPmodels.

  • 18.
    Kallin Westin, Lena
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Nordström, Marie
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Supplemental Instruction (SI) - Applied on the course Object-Oriented Programming Methodology2003Rapport (Övrig (populärvetenskap, debatt, mm))
    Abstract [en]

    The introduction of the Supplemental Instruction (SI) method to the introductory programming course was initialised by the fact that the rate of students passing the course had been constantly decreasing for the last few years. This, in combination with the decreasing in student admission, made it necessary to further assist the students in some way. This was done during the autumn of 2002 and this paper describes the work concerning the entire SI-project.

  • 19.
    Kallin Westin, Lena
    et al.
    Umeå universitet, Teknisk-naturvetenskaplig fakultet, Datavetenskap.
    Nordström, Marie
    Umeå universitet, Teknisk-naturvetenskaplig fakultet, Datavetenskap.
    Teaching OO Concepts - A new Approach2004Ingår i: Proceedings of the 2004 ASEE/IEEE Frontiers in Education Conference (FIE2004), 2004, s. F3C-6Konferensbidrag (Refereegranskat)
    Abstract [en]

    In recent years, students have become less active, resulting in lower attendance in lectures and practical sessions. In addition to this, the number of students enlisting in our programmes has decreased. Moreover the passing rates for initial courses have dropped severely. This generates problems because the students failing first year courses cannot move on to higher level courses. Not only can this be devastating for individual students, but it can also affect the variety of higher level courses. In an attempt to prevent these problems we focused on the introductory programming courses (CS1) in order to enhance the opportunities for the students to become successful. One action taken was a research project initiated to radically change the way object-oriented programming is taught in CS1. Another action was to introduce the Supplemental Instruction programme (SI). SI helps students master content while they develop and integrate learning and study strategies. This paper will give a short introduction to these actions. Results are presented along with a discussion concerning the problems in teaching object-oriented concepts and problem solving.

  • 20.
    Kallin Westin, Lena
    et al.
    Umeå universitet, Teknisk-naturvetenskaplig fakultet, Datavetenskap.
    Nordström, Marie
    Umeå universitet, Teknisk-naturvetenskaplig fakultet, Datavetenskap.
    Två projekt som hjälper elever bli studenter2003Ingår i: En öppen högskola - vilka kunskaper och vilket lärande: Proceedings of Universitetspedagogisk konferens in Umeå, February 19-20, 2003, s. 34-47Konferensbidrag (Refereegranskat)
    Abstract [sv]

    Studentunderlaget har förändrats markant de senaste åren. Antalet studenter har ökat och deras förkunskaper och motiv till studierna har ändrats. Vi upplever att andelen studenter som läser "för att klara sig" och snabbt få ett välbetalt jobb har ökat medan andelen som kommer hit av genuint ämnesintresse har minskat. En vanlig fråga är "Vad ska vi kunna det här för?".

    Enskilda lärare har omedvetet successivt anpassat kursinnehåll och genomförande för att kompensera för de ändrade förutsättningarna. Vi anser att förändringar måste diskuteras mer allmänt och på institutions-/universitetsnivå. Ska kursinnehållet anpassas och vem ska ta ansvar för att det blir rimliga justeringar?

  • 21.
    Nordström, Marie
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Kallin Westin, Lena
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    SI - Small Scale Advantages2006Rapport (Övrigt vetenskapligt)
    Abstract [en]

    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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