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Dalipi, Fisnik, Senior lecturerORCID iD iconorcid.org/0000-0001-7520-695x
Publikasjoner (10 av 29) Visa alla publikasjoner
Salah Uddin, A., Aasnæs, S., Dalipi, F. & Hesten, K. (2019). Analytics-driven digital platform for regional growth and development: a case study from Norway. In: Lasse Berntzen (Ed.), ICDS 2019, The Thirteenth International Conference on Digital Society and eGovernments: . Paper presented at ICDS 2019: The Thirteenth International Conference on Digital Society and eGovernments, Athens, Greece, February 24-28, 2019 (pp. 56-62). International Academy, Research and Industry Association (IARIA), Article ID icds_2019_3_30_10048.
Åpne denne publikasjonen i ny fane eller vindu >>Analytics-driven digital platform for regional growth and development: a case study from Norway
2019 (engelsk)Inngår i: ICDS 2019, The Thirteenth International Conference on Digital Society and eGovernments / [ed] Lasse Berntzen, International Academy, Research and Industry Association (IARIA), 2019, s. 56-62, artikkel-id icds_2019_3_30_10048Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In this paper, we present the growth barometer (Vekstbarometer in Norwegian), which is a digital platform that provides the development trends in the regional context in a visual and user-friendly way. The platform is developed to use open data from different sources that is presented mainly in five main groups: goals, premises or prerequisites for growth, industries, growth, and expectations. Furthermore, it also helps to improve decision-making and transparency, as well as provide new knowledge for research and society. The platform uses sensitive and non-sensitive open data. In contrast to other similar digital platforms from Norway, where the data is presented as raw data or with basic level of presentations, our platform is advantageous since it provides a range of options for visualization that makes the statistics more comprehensive.

sted, utgiver, år, opplag, sider
International Academy, Research and Industry Association (IARIA), 2019
Serie
ICDS, International Conference on Digital Society, ISSN 2308-3956
Emneord
digital platform, growth barometer, regional growth, analytics, visualization
HSV kategori
Forskningsprogram
Data- och informationsvetenskap
Identifikatorer
urn:nbn:se:lnu:diva-81526 (URN)9781612086859 (ISBN)
Konferanse
ICDS 2019: The Thirteenth International Conference on Digital Society and eGovernments, Athens, Greece, February 24-28, 2019
Prosjekter
Vekstbarometer
Tilgjengelig fra: 2019-03-29 Laget: 2019-03-29 Sist oppdatert: 2019-04-12bibliografisk kontrollert
Uddin Ahmed, S., Dalipi, F. & Aasnæs, S. (2019). Open Data Based Digital Platform for Regional Growth and Development in Norway: A Heuristic Evaluation of the User Interface. International Journal On Advances in Software, 12(3&4), 239-248
Åpne denne publikasjonen i ny fane eller vindu >>Open Data Based Digital Platform for Regional Growth and Development in Norway: A Heuristic Evaluation of the User Interface
2019 (engelsk)Inngår i: International Journal On Advances in Software, ISSN 1942-2628, E-ISSN 1942-2628, Vol. 12, nr 3&4, s. 239-248Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Even though a homogenous growth is more desired and expected, socio-economic disparities can still be observed across different regions of a country. Measuring regional growth is essential for the regions that are falling behind or struggling to achieve desired growth. While there exists different statistics of regional growth data, there is a lack of digital tools that represent the growth data in a visual friendly manner to the different stakeholders involved in the region. We have developed a digital platform to address this issue for a region in Norway. The platform is developed to use open data from different sources that is presented in five major groups related to growth: goals, premises or prerequisites for growth, industries, growth, and expectations. The platform helps to improve decision-making and transparency, as well as provide new knowledge for research and society. Compared to other similar digital platforms from Norway, our platform has the strength of providing more options for visualization that makes the statistics more comprehensive. However, we believe that proper usability of the tool can be improved by getting feedback from real users. Therefore, a heuristic evaluation is conducted in order to find out the usability issues and further improve the tool for its users. In this article, we report the results of evaluation and implications for the future improvement along with the presentation of our tool.

sted, utgiver, år, opplag, sider
International Academy, Research and Industry Association (IARIA), 2019
Emneord
digital platform; growth barometer; regional growth; open data; analytics; visualization; usability; heuristic evaluation
HSV kategori
Forskningsprogram
Data- och informationsvetenskap; Datavetenskap, Informations- och programvisualisering; Datavetenskap, Informations- och programvisualisering
Identifikatorer
urn:nbn:se:lnu:diva-90946 (URN)
Prosjekter
Vekstbarometer
Tilgjengelig fra: 2020-01-15 Laget: 2020-01-15 Sist oppdatert: 2020-01-29bibliografisk kontrollert
Ahmed, S. U., Dalipi, F. & Ferati, M. (2019). Plugin: a Crowdsourcing Mobile App for Easy Discovery of Public Charging Outlets. In: Stephanidis C. (Ed.), HCI International 2019: Posters. HCII 2019. Paper presented at DUXU: 8TH INTERNATIONAL CONFERENCE ON DESIGN, USER EXPERIENCE AND USABILITY (pp. 323-329). Springer, 1034
Åpne denne publikasjonen i ny fane eller vindu >>Plugin: a Crowdsourcing Mobile App for Easy Discovery of Public Charging Outlets
2019 (engelsk)Inngår i: HCI International 2019: Posters. HCII 2019 / [ed] Stephanidis C., Springer, 2019, Vol. 1034, s. 323-329Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Nowadays, the growth of mobile apps is so fast and viral; they have the potential of transforming our everyday lives by creating huge opportunities to individuals and businesses. This translates into a growing demand for developing such apps, which need to be easy to learn and use. In this paper, we conduct an evaluation of an android mobile app, which we designed and developed to find and register power outlets in public spaces. Our evaluation of the prototype consisted of two stages. First, we provided the users with two tasks, with an additional option to indicate their perception of how easy it was to complete these tasks. Second, upon completing both tasks and offering their comments, participants were asked to take the SUS (System Usability Scores) questionnaire. The results of the evaluation indicate that the app usability and learnability is acceptable despite being a prototype. The findings and participants’ comments give us a direction on how this app can be improved in the future.

sted, utgiver, år, opplag, sider
Springer, 2019
Serie
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1034
HSV kategori
Forskningsprogram
Data- och informationsvetenskap, Informatik
Identifikatorer
urn:nbn:se:lnu:diva-86941 (URN)10.1007/978-3-030-23525-3_42 (DOI)2-s2.0-85069680156 (Scopus ID)978-3-030-23525-3 (ISBN)978-3-030-23524-6 (ISBN)
Konferanse
DUXU: 8TH INTERNATIONAL CONFERENCE ON DESIGN, USER EXPERIENCE AND USABILITY
Tilgjengelig fra: 2019-07-22 Laget: 2019-07-22 Sist oppdatert: 2019-09-06bibliografisk kontrollert
Imran, A. S., Dalipi, F. & Kastrati, Z. (2019). Predicting Student Dropout in a MOOC: An Evaluation of a Deep Neural Network Model. In: Proceedings of the 2019 5th International Conference on Computing and Artificial Intelligence: . Paper presented at 5th International Conference on Computing and Artificial Intelligence, April 19-22, 2019 (pp. 190-195). ACM Publications
Åpne denne publikasjonen i ny fane eller vindu >>Predicting Student Dropout in a MOOC: An Evaluation of a Deep Neural Network Model
2019 (engelsk)Inngår i: Proceedings of the 2019 5th International Conference on Computing and Artificial Intelligence, ACM Publications, 2019, s. 190-195Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Massive Open Online Courses (MOOCs) have transformed the way educational institutions deliver high-quality educational material to the onsite and distance learners across the globe. As a result, a new paradigm shifts as to how learners acquire and benefit from the wealth of knowledge provided by a MOOC at their doorstep nowadays in contrast to the brick and mortar settings is visible. Learners are therefore showing a profound interest in the MOOCs offered by top universities and industry giants. They have also attracted a vast number of students from far-flung areas of the world. The massive number of registered students in MOOCs, however, pose one major challenge, i.e., 'the dropouts'. Course planners and content providers are struggling to retain the registered students, which give rise to a new research agenda focusing on predicting and explaining student dropout and low completion rates in a MOOC. Machine learning techniques utilizing deep learning approaches can efficiently predict the potential dropouts and can raise an alert well before time. In this paper, we have focused our study on the application of feed-forward deep neural network architectures to address this problem. Our model achieves not only high accuracy, but also low false negative rate while predicting dropouts on the MOOC data. Moreover, we also provide an in-depth comparison of the proposed architectures concerning precision, recall, and F1 measure.

sted, utgiver, år, opplag, sider
ACM Publications, 2019
Emneord
ANN, Dropout prediction, MOOC, deep learning, distance learning, e-Learning, online learning
HSV kategori
Forskningsprogram
Data- och informationsvetenskap, Datavetenskap
Identifikatorer
urn:nbn:se:lnu:diva-87087 (URN)10.1145/3330482.3330514 (DOI)978-1-4503-6106-4 (ISBN)
Konferanse
5th International Conference on Computing and Artificial Intelligence, April 19-22, 2019
Forskningsfinansiär
Knowledge Foundation, 67110033
Tilgjengelig fra: 2019-08-04 Laget: 2019-08-04 Sist oppdatert: 2019-09-04bibliografisk kontrollert
Dalipi, F., Ferati, M. & Kurti, A. (2018). Integrating MOOCs in Regular Higher Education: Challenges and Opportunities from a Scandinavian Perspective. In: Panayiotis Zaphiris and Andri Ioannou (Ed.), Learning and Collaboration Technologies: Design, Development and Technological Innovation. LCT 2018. Paper presented at 5th International Conference on Learning and Collaboration Technologies, LCT 2018 Held as Part of HCI International 2018; Las Vegas; United States; 15-20 July 2018 (pp. 193-204). Springer, 10924
Åpne denne publikasjonen i ny fane eller vindu >>Integrating MOOCs in Regular Higher Education: Challenges and Opportunities from a Scandinavian Perspective
2018 (engelsk)Inngår i: Learning and Collaboration Technologies: Design, Development and Technological Innovation. LCT 2018 / [ed] Panayiotis Zaphiris and Andri Ioannou, Springer, 2018, Vol. 10924, s. 193-204Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

MOOCs are increasingly being considered by universities as an integral part of their curriculum. Nevertheless, there are several challenges that to some extent slow this process, where the most important one is the accreditation challenges and financing. These challenges are particularly important in the context of universities in Scandinavian countries where education is mostly free. In order to gain more insights on the status of proliferation of MOOCs in Scandinavian universities and understand any specific challenges, we conducted a study by analyzing two sources of data: research publications and university websites. Further on, these data have been analyzed using a framework that differentiates and categorizes MOOCs in terms of accreditation and scalability. As a result of this analysis, we have identified the remaining challenges as well as a number of opportunities regarding the full integration of MOOCs in the educational system of the Scandinavian Higher Education Institutions.

sted, utgiver, år, opplag, sider
Springer, 2018
Serie
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10924
Emneord
Denmark, Higher education, MOOCs, Norway, Online learning, Opportunities, Scandinavia, Sweden, Artificial intelligence, Computer science, Computers, Accreditation
HSV kategori
Forskningsprogram
Data- och informationsvetenskap, Informatik
Identifikatorer
urn:nbn:se:lnu:diva-77804 (URN)10.1007/978-3-319-91743-6_15 (DOI)2-s2.0-85050572535 (Scopus ID)9783319917429 (ISBN)
Konferanse
5th International Conference on Learning and Collaboration Technologies, LCT 2018 Held as Part of HCI International 2018; Las Vegas; United States; 15-20 July 2018
Forskningsfinansiär
Knowledge Foundation, 67110033
Tilgjengelig fra: 2018-09-14 Laget: 2018-09-14 Sist oppdatert: 2018-10-04bibliografisk kontrollert
Dalipi, F., Imran, A. S. & Kastrati, Z. (2018). MOOC Dropout Prediction Using Machine Learning Techniques: Review and Research Challenges. In: Proceedings of 2018 IEEE Global Engineering Education Conference (EDUCON) - Emerging Trends and Challenges of Engineering Education: . Paper presented at IEEE Global Engineering Education Conference (EDUCON) - Emerging Trends and Challenges of Engineering Education, APR 17-20, 2018, Santa Cruz de Tenerife, SPAIN (pp. 1007-1014). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>MOOC Dropout Prediction Using Machine Learning Techniques: Review and Research Challenges
2018 (engelsk)Inngår i: Proceedings of 2018 IEEE Global Engineering Education Conference (EDUCON) - Emerging Trends and Challenges of Engineering Education, IEEE, 2018, s. 1007-1014Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

MOOC represents an ultimate way to deliver educational content in higher education settings by providing high-quality educational material to the students throughout the world. Considering the differences between traditional learning paradigm and MOOCs, a new research agenda focusing on predicting and explaining dropout of students and low completion rates in MOOCs has emerged. However, due to different problem specifications and evaluation metrics, performing a comparative analysis of state-of-the-art machine learning architectures is a challenging task. In this paper, we provide an overview of the MOOC student dropout prediction phenomenon where machine learning techniques have been utilized. Furthermore, we highlight some solutions being used to tackle with dropout problem, provide an analysis about the challenges of prediction models, and propose some valuable insights and recommendations that might lead to developing useful and effective machine learning solutions to solve the MOOC dropout problem.

sted, utgiver, år, opplag, sider
IEEE, 2018
Serie
IEEE Global Engineering Education Conference, ISSN 2165-9567
Emneord
MOOC, review, dropout prediction, machine learning, artificial intelligence
HSV kategori
Forskningsprogram
Data- och informationsvetenskap, Datavetenskap
Identifikatorer
urn:nbn:se:lnu:diva-76904 (URN)10.1109/EDUCON.2018.8363340 (DOI)000434866100141 ()2-s2.0-85048098466 (Scopus ID)978-1-5386-2957-4 (ISBN)
Konferanse
IEEE Global Engineering Education Conference (EDUCON) - Emerging Trends and Challenges of Engineering Education, APR 17-20, 2018, Santa Cruz de Tenerife, SPAIN
Tilgjengelig fra: 2018-07-17 Laget: 2018-07-17 Sist oppdatert: 2020-05-12bibliografisk kontrollert
Idrizi, F., Rustemi, A. & Dalipi, F. (2017). A new modified sorting algorithm: A comparison with state of the art. In: 2017 6th Mediterranean Conference on Embedded Computing (MECO): . Paper presented at 2017 6th Mediterranean Conference on Embedded Computing (MECO), 11-15 June 2017, Bar, Montenegro (pp. 419-424). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>A new modified sorting algorithm: A comparison with state of the art
2017 (engelsk)Inngår i: 2017 6th Mediterranean Conference on Embedded Computing (MECO), IEEE, 2017, s. 419-424Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Choosing the right method to sort numbers has a huge effect on how quickly a computer can process a task. The most used sorting algorithms today have been discovered years ago, and to this day, they have been the best for the job as there was no other competitive algorithm. Through this paper, we make an analysis and comparison between the state of the art algorithms in sorting and based on their analogy of functionality, we propose a new modified sorting algorithm. We then present a brief description of the new modified algorithm, conduct comparisons with the state of the art, and finally we give conclusions about the performance of the proposed algorithm versus the most popular sorting algorithms. Moreover, we highlight the benefits of using this algorithm in different fields by various business companies or software developers, in cases when they need faster and easier sorting for their data management.

sted, utgiver, år, opplag, sider
IEEE, 2017
Serie
Mediterranean Conference on Embedded Computing, ISSN 2377-5475
Emneord
sorting ; data management ; functionality analogy ; modified sorting algorithm ; Algorithm design and analysis ; Bars ; Embedded computing ; Flowcharts;Printing ; Software algorithms ; Sorting;comparison and analyses ; experimental setup ; implementation ; sorting algorithms ; state of the art
HSV kategori
Forskningsprogram
Data- och informationsvetenskap, Datavetenskap
Identifikatorer
urn:nbn:se:lnu:diva-67906 (URN)10.1109/MECO.2017.7977252 (DOI)000428759500104 ()2-s2.0-85027019499 (Scopus ID)978-1-5090-6742-8 (ISBN)978-1-5090-6743-5 (ISBN)978-1-5090-6740-4 (ISBN)
Konferanse
2017 6th Mediterranean Conference on Embedded Computing (MECO), 11-15 June 2017, Bar, Montenegro
Tilgjengelig fra: 2017-09-18 Laget: 2017-09-18 Sist oppdatert: 2019-08-29bibliografisk kontrollert
Dalipi, F., Imran, A. S., Idrizi, F. & Aliu, H. (2017). An Analysis of Learner Experience with MOOCs in Mobile and Desktop Learning Environment. In: Kantola, Jussi Ilari; Barath, Tibor; Nazir, Salman; Andre, Terence (Ed.), Learning and Collaboration Technologies. LCT 2016: . Paper presented at The AHFE 2016 International Conference on Human Factors, Business Management and Society, July 27-31, 2016, Florida, USA (pp. 393-402). Orlando, USA: Springer
Åpne denne publikasjonen i ny fane eller vindu >>An Analysis of Learner Experience with MOOCs in Mobile and Desktop Learning Environment
2017 (engelsk)Inngår i: Learning and Collaboration Technologies. LCT 2016, Orlando, USA: Springer, 2017, s. 393-402Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Massive Open Online Courses (MOOCs) are now the most recent topic within the field of e-learning. They have the potential to influence the higher education environments significantly worldwide by creating a completely new and large market of educational resources by overpassing the traditional universities market share due to their physical limitations. However, due to the many differences between mobile devices and desktop environments, the introduction of mobile technology in MOOC environment is challenging. Hence, the main objective of this paper is to study and compare the learner’s experience in different learning environments by using mobile devices and PCs while performing given tasks related to MOOCs. To achieve this goal, we conduct a subjective experiment with various MOOCs related tasks to be performed in mobile and desktop learning environment. The results of the findings show that the difficulties learners have experienced in the mobile environment are more expressed. Moreover, their satisfactory level is much higher in the desktop environment.

sted, utgiver, år, opplag, sider
Orlando, USA: Springer, 2017
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 9753
HSV kategori
Forskningsprogram
Data- och informationsvetenskap, Datavetenskap
Identifikatorer
urn:nbn:se:lnu:diva-67463 (URN)10.1007/978-3-319-42070-7_36 (DOI)978-3-319-39482-4 (ISBN)978-3-319-39483-1 (ISBN)
Konferanse
The AHFE 2016 International Conference on Human Factors, Business Management and Society, July 27-31, 2016, Florida, USA
Tilgjengelig fra: 2017-08-28 Laget: 2017-08-28 Sist oppdatert: 2017-09-18bibliografisk kontrollert
Kurti, A. & Dalipi, F. (2017). Bridging the Gap between Academia and Industry: Lessons Learned from a Graduate IT Professional Development Program. In: Gregory T. Papanikos (Ed.), Abstract Book: 2nd Annual International Conference on Engineering Education & Teaching, 5-8 June 2017, Athens, Greece. Paper presented at 2nd Annual International Conference on Engineering Education & Teaching, 5-8 June 2017, Athens (pp. 27-27). Athens
Åpne denne publikasjonen i ny fane eller vindu >>Bridging the Gap between Academia and Industry: Lessons Learned from a Graduate IT Professional Development Program
2017 (engelsk)Inngår i: Abstract Book: 2nd Annual International Conference on Engineering Education & Teaching, 5-8 June 2017, Athens, Greece / [ed] Gregory T. Papanikos, Athens, 2017, s. 27-27Konferansepaper, Oral presentation with published abstract (Annet vitenskapelig)
Abstract [en]

The rapid advances of technologies, constantly brings new demands for new skills and expertise of the professionals in IT industry. There is a constant need for people that have in-depth understanding and know how to develop the new innovative services using these new technologies. In these settings, the real challenge is how to find the right persons with the right education in an industry where the in-thing yesterday may be out-of-date tomorrow? To add to this challenge, universities are still “increasingly stove-piped in highly specialized disciplinary fields” (Hurlburt et al., 2010) as well as there is a lack of flexibility for the professionals to have their competences developed. All this points out the great challenges that universities are facing for alignment between academic development within degree curricula and the requirements that industry demands for their specific needs (Falcone et al. 2014). In this research effort we report our experiences from an ongoing Graduate Professional Development Program where we address these challenges through a co-creation process with IT industry based on open innovation. Through this model we bring together research expertise, academic experience and experts from industry in a collaborative process for developing courses to suit the current needs of IT professionals. As an outcome of this process, the course content is tailor-made, as well as everything else in connection, such as: bite-size modules, adjustable pace, open and online educational resources, as well as a flipped classroom approach to teaching. As a result, we have developed and provided so far five courses that have been very well accepted by the IT professional. Thus, in this paper we aim to provide some insights on approaches for facilitating continuous competence development plans for IT professionals within regular university educational offer. 

sted, utgiver, år, opplag, sider
Athens: , 2017
HSV kategori
Forskningsprogram
Data- och informationsvetenskap, Datavetenskap
Identifikatorer
urn:nbn:se:lnu:diva-67486 (URN)978-960-598-128-0 (ISBN)
Konferanse
2nd Annual International Conference on Engineering Education & Teaching, 5-8 June 2017, Athens
Forskningsfinansiär
Knowledge Foundation
Tilgjengelig fra: 2017-08-29 Laget: 2017-08-29 Sist oppdatert: 2019-05-20bibliografisk kontrollert
Dalipi, F., Idrizi, F. & Kurti, A. (2017). Exploring the Impact of Social Learning Networks in M-Learning: a Case Study in a University Environment. In: Zaphiris, Panayiotis; Ioannou, Andri (Ed.), Panayiotis Zaphiris, Andri Ioannou (Ed.), Learning and Collaboration Technologies Novel Learning Ecosystems: 4th International Conference, LCT 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017, Proceedings, Part I. Paper presented at 4th International Conference, LCT 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017 (pp. 189-198). Vancouver: Springer
Åpne denne publikasjonen i ny fane eller vindu >>Exploring the Impact of Social Learning Networks in M-Learning: a Case Study in a University Environment
2017 (engelsk)Inngår i: Learning and Collaboration Technologies Novel Learning Ecosystems: 4th International Conference, LCT 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017, Proceedings, Part I / [ed] Panayiotis Zaphiris, Andri Ioannou, Vancouver: Springer, 2017, s. 189-198Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The high penetration of Internet, advances in mobile computing and the rise of smartphone usage has largely enhanced the use of social media in education. Moreover, nowadays social learning network (SLN) platforms have become an important educational technology component in higher education. Despite the fact that SLN are becoming ubiquitous in the higher education, there is relatively not much empirical work done investigating their purposefulness when integrated into the learning activities. This paper aims at exploring the impact of SLN in mobile assisted learning and to provide empirical evidence as to what extent SLN and mobile learning (M-learning) can improve the learning experiences. For this purpose, a quantitative experimental approach is used, and two survey questionnaires were conducted. The data is collected from 120 participants. In this study, we focus our intention on Edmodo and Kahoot platforms, which represent social media based tools that aid and support collaboration, knowledge sharing and group activities among students. Computer science students of the Tetovo University (TU) used these tools throughout one semester. From this study, there is significant evidence that students are very interested to use this SLN in a M-learning setting, indicating that SLN can be one of the promising pedagogical technologies that could contribute effectively to learning process.

sted, utgiver, år, opplag, sider
Vancouver: Springer, 2017
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10295
Emneord
M-learning, Social learning networks, Higher education, Edmodo, Kahoot
HSV kategori
Forskningsprogram
Data- och informationsvetenskap
Identifikatorer
urn:nbn:se:lnu:diva-67455 (URN)10.1007/978-3-319-58509-3_16 (DOI)000434087100016 ()2-s2.0-85025174998 (Scopus ID)978-3-319-58508-6 (ISBN)978-3-319-58509-3 (ISBN)
Konferanse
4th International Conference, LCT 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017
Forskningsfinansiär
Knowledge Foundation
Tilgjengelig fra: 2017-08-28 Laget: 2017-08-28 Sist oppdatert: 2019-08-29bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0001-7520-695x

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