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  • 1.
    Avdic, Anders
    et al.
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Åkerblom, Leif
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Flipped classroom and learning strategies2015In: Proceedings of 14th European Conference on e-Learning / [ed] Jefferies, A., Cubric, M., Barton, K. & Lilley, M., Reading, UK: Academic Conferences Publishing, 2015, p. 41-49Conference paper (Refereed)
    Abstract [en]

    This paper seeks to answer the research question "How does the flipped classroom affect students’ learning strategies?" In e-learning research, several studies have focused on how students and teachers perceive the flipped classroom approach. In general, these studies have reported pleasing results. Nonetheless, few, if any, studies have attempted to find out the potential effects of the flipped classroom approach on how students learn.

    This study was based on two cases: 1) a business modelling course and 2) a research methodology course. In both cases, participating students were from information systems courses at Dalarna University in Sweden. Recorded lectures replaced regular lectures. The recorded lectures were followed by seminars that focused on the learning content of each lecture in various ways.

    Three weeks after the final seminar, we arranged for two focus group interviews to take place in each course, with 8 to 10 students participating in each group. We asked open questions on how the students thought they had been affected and more dedicated questions that were generated from a literature study on the effects of flipped classroom courses. These questions dealt with issues about mobility, the potential for repeating lectures, formative feedback, the role of seminars, responsibility, empowerment, lectures before seminars, and any problems encountered.

    Our results show that, in general, students thought differently about learning after the courses in relation to more traditional approaches, especially regarding the need to be more active. Most students enjoyed the mobility aspect and the accessibility of recorded lectures, although a few claimed it demanded a more disciplined attitude. Most students also expressed a feeling of increased activity and responsibility when participating in seminars. Some even felt empowered because they could influence seminar content. The length of and possibility to navigate in recorded lectures was also considered important. The arrangement of the seminar rooms should promote face-to-face discussions. Finally, the types of questions and tasks were found to affect the outcomes of the seminars.

    The overall conclusion with regard to students’ learning strategies is that to be an active, responsible, empowered, and critical student you have to be an informed student with possibilities and mandate to influence how, where and when to learn and be able to receive continuous feedback during the learning process. Flipped classroom can support such learning.

  • 2.
    Song, William Wei
    et al.
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Lin, Chenlu
    Avdic, Anders
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Forsman, Anders
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Åkerblom, Leif
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Collaborative Filtering with Data Classification: A Combined Approach to Hotel Recommendation Systems2016In: 25th International Conference on Information Systems Development (ISD2016 Poland), 2016Conference paper (Refereed)
    Abstract [en]

    Recommendation systems aim to help users make decisions more efficiently. The most widely used method in recommendation systems is collaborative filtering, of which, a critical step is to analyze a user's preferences and make recommendations of products or services based on similarity analysis with other users' ratings. However, collaborative filtering is less usable for recommendation facing the "cold start" problem, i.e. few comments being given to products or services. To tackle this problem, we propose an improved method that combines collaborative filtering and data classification. We use hotel recommendation data to test the proposed method. The accuracy of the recommendation is determined by the rankings. Evaluations regarding the accuracies of Top-3 and Top-10 recommendation lists using the 10-fold cross-validation method and ROC curves are conducted. The results show that the Top-3 hotel recommendation list proposed by the combined method has the superiority of the recommendation performance than the Top-10 list under the cold start condition in most of the times.

  • 3.
    Song, William Wei
    et al.
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Lin, Chenlu
    Forsman, Anders
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Avdic, Anders
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Åkerblom, Leif
    Dalarna University, School of Technology and Business Studies, Information Systems.
    An Euclidean similarity measurement approach for hotel rating data analysis2017In: Proceedings 2017 2nd IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2017, 2017, p. 293-298Conference paper (Refereed)
    Abstract [en]

    The most widely used method in recommendation systems is collaborative filtering, of which, a critical step is to analyze a user's preferences and make recommendations of products or services based on similarity analysis with other users' ratings. However, collaborative filtering is less usable for recommendation facing the 'cold start' problem, i.e. few comments being given to products or services. To tackle this problem, we propose an improved method that combines collaborative filtering and data classification. We use hotel recommendation data to test the proposed method. The accuracy of the recommendation is determined by the rankings. Evaluations regarding the accuracies of Top-3 and Top-10 recommendation lists using the 10-fold cross-validation method and ROC curves are conducted. The results show that the Top-3 hotel recommendation list proposed by the combined method has the superiority of the recommendation performance than the Top-10 list under the cold start condition in most of the times.

  • 4.
    Åkerblom, Leif
    et al.
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Eriksson, Owen
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Tourist Information Systems based on Trail Network Information2003In: ENTER2003, Helsinki, Finland, 2003Conference paper (Refereed)
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