Publications

  • H. Yun, A. Fortenbacher, N. Pinkwart, T. Bisson, and F. Moukayed, “A pilot study of emotion detection using sensors in a learning context: Towards an affective learning companion,” in Ceur workshop proceedings, 2018.
    [Bibtex]
    @inproceedings{Yun2018,
    abstract = {{\textcopyright} 2018 CEUR-WS. All rights reserved. Emotions facilitate knowledge attainment and also affect learners on their current behavior and future choice. Sensors which detect physiological signals have been studied and related to emotions, and specifically electro dermal activity (EDA) and heart rate variability (HRV) have been adopted to detect emotion. In this pilot study, we have presented visual emotional stimuli to 6 participants and attained their ratings on a picture. Their EDA and HRV values were recorded and investigated to find any relation between the stimulated emotion, self-assessment and physiological signals. The study explored EDA and HRV signal changes due to the visual stimuli and some signal changes in EDA were observed, when joyful or satisfied pictures were presented. However, limitations need to be overcome to provide clearer interpretations. A future study on providing awareness to learners using a learning companion is suggested.},
    author = {Yun, H. and Fortenbacher, A. and Pinkwart, N. and Bisson, T. and Moukayed, F.},
    booktitle = {CEUR Workshop Proceedings},
    issn = {16130073},
    keywords = {Adaptive learning,Emotion detection,IAPS,Learning analytics,Learning companion,Sensor based learning},
    title = {{A pilot study of emotion detection using sensors in a learning context: Towards an affective learning companion}},
    volume = {2092},
    year = {2018}
    }
  • H. Yun, A. Fortenbacher, N. Pinkwart, and T. Bisson, “A Pilot Study of Emotion Detection using Sensors in a Learning Context : Towards an Affective Learning Companion,” in Proceedings of delfi and gmw workshops 2017, 2017, p. 15–25.
    [Bibtex]
    @inproceedings{Yun2017,
    author = {Yun, Haeseon and Fortenbacher, Albrecht and Pinkwart, Niels and Bisson, Tom},
    booktitle = {Proceedings of DeLFI and GMW Workshops 2017},
    editor = {Ullrich, Carsten and Wessner, Ullrich},
    file = {:Users/forte/Downloads/YunFortenbacherPinkwartBissonMoukayed-PilotStudyEmotionDetection.pdf:pdf},
    keywords = {adaptive learning,emotion detection,iaps,learning analytics,learning companion,sensor based learning},
    pages = {15--25},
    publisher = {CEUR Workshop Proceedings, RWTH Aachen},
    title = {{A Pilot Study of Emotion Detection using Sensors in a Learning Context : Towards an Affective Learning Companion}},
    url = {CEUR-WS.org/Vol-2092/},
    year = {2017}
    }
  • [DOI] R. Jaakonmäki, S. Dietze, H. Drachsler, A. Fortenbacher, M. Kickmeier-Rust, and I. Marenzi, “Cooking with learning analytics recipes,” in Acm international conference proceeding series, 2017.
    [Bibtex]
    @inproceedings{Jaakonmaki2017,
    abstract = {{\textcopyright} 2017 ACM. Learning Analytics is a melting pot for a multitude of research fields and origin of many developments about learning and its environment. There is a serious hype over the concepts of learning analytics, however, concrete solutions and applications are comparably scarce. Of course, data rich environments, such as MOOCs, come with statistical analytics dashboards, although the educational value is often limited. Practical solutions for scenarios in data-lean environments or for small-scale organizations are rarely adopted. The LA4S project is dedicated to gather practical solutions, provide a tool box for practitioners, and publish a cook book with concrete learning analytics recipes for everyone.},
    author = {Jaakonm{\"{a}}ki, R. and Dietze, S. and Drachsler, H. and Fortenbacher, A. and Kickmeier-Rust, M. and Marenzi, I.},
    booktitle = {ACM International Conference Proceeding Series},
    doi = {10.1145/3027385.3029465},
    isbn = {9781450348706},
    keywords = {Applications,Cookbook,Learning Analytics,Recipes,Solutions},
    title = {{Cooking with learning analytics recipes}},
    year = {2017}
    }
  • [DOI] V. Brovkov, A. Fortenbacher, H. Yun, and D. Junker, “Prototype of a sensor device for learning environments,” in 2016 ieee 3rd international symposium on wireless systems within the ieee international conferences on intelligent data acquisition and advanced computing systems, idaacs-sws 2016 – proceedings, 2017.
    [Bibtex]
    @inproceedings{Brovkov2017,
    abstract = {{\textcopyright} 2016 IEEE. This paper presents a prototype of a sensor device including heart rate, EDA and accelerometer sensors to investigate learners' internal state. Through experiments, sensor data were collected, visualized and correlated with information on learner's' emotional state derived from a self-report questionnaire. The results can be used to improve signal processing, and help find appropriate indicators from physiological data for learning environment design.},
    author = {Brovkov, V. and Fortenbacher, A. and Yun, H. and Junker, D.},
    booktitle = {2016 IEEE 3rd International Symposium on Wireless Systems within the IEEE International Conferences on Intelligent Data Acquisition and Advanced Computing Systems, IDAACS-SWS 2016 - Proceedings},
    doi = {10.1109/IDAACS-SWS.2016.7805802},
    isbn = {9781509043163},
    keywords = {electrodermal activity,emotional intelligence,learning indicators,physiological sensors},
    title = {{Prototype of a sensor device for learning environments}},
    year = {2017}
    }
  • [DOI] H. Yun, A. Fortenbacher, and N. Pinkwart, “Improving a mobile learning companion for self-regulated learning using sensors,” in Csedu 2017 – proceedings of the 9th international conference on computer supported education, 2017.
    [Bibtex]
    @inproceedings{Yun2017a,
    abstract = {Copyright {\textcopyright} 2017 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved. The ability to efficiently manage learning is linked to positive learning experience and outcome. However, attaining the ability of self-regulating is not a matter of course for students and it requires an external assistance. To support learners to be equipped with self-regulated learning skills, a mobile device can serve as an ideal learning companion which provides valuable feedback. A learning companion stemming from intelligent tutoring system (ITS) has non-authoritative, co-present effect as a learning support and with available sensor technology, self-regulated learning can be better promoted. Sensor enhanced learning companion can detect learning states, learning behaviours and context and provide valuable feedback to learners to increase their awareness of learning progress and to effectively manage their learning. Considering the mobility, autonomy of learners along together with current trend in open online learning resources and contents, available embedded sensors are suitable for realising the concept of learning companion for self-regulated learning. The paper reviews a self-regulated learning concept, a learning companion pedagogy and proposes that self-regulated learning skills can be promoted using sensor technology and a learning companion pedagogy.},
    author = {Yun, H. and Fortenbacher, A. and Pinkwart, N.},
    booktitle = {CSEDU 2017 - Proceedings of the 9th International Conference on Computer Supported Education},
    doi = {10.5220/0006375405310536},
    isbn = {978-989-758-239-4},
    keywords = {Intelligent tutoring system,Learning companion,Mobile learning,Pervasive learning,Self-regulated learning,Sensor-based learning},
    title = {{Improving a mobile learning companion for self-regulated learning using sensors}},
    volume = {1},
    year = {2017}
    }
  • [DOI] A. Fortenbacher, N. Pinkwart, and H. Yun, “[LISA] learning analytics for sensor-based adaptive learning,” in Acm international conference proceeding series, 2017.
    [Bibtex]
    @inproceedings{Fortenbacher2017,
    abstract = {{\textcopyright} 2017 ACM. This paper reports on research conducted in a project named LISA which aims at supporting learners through learnercentered learning analytics using physiological sensor data as well as environmental sensors. We present the concept and a prototypical realization of a mobile sensor device used in LISA.},
    author = {Fortenbacher, A. and Pinkwart, N. and Yun, H.},
    booktitle = {ACM International Conference Proceeding Series},
    doi = {10.1145/3027385.3029476},
    isbn = {9781450348706},
    keywords = {Adaptive learning,Learner awareness,Learner-centric learning analytics,Self-regulated learning,Sensor based learning},
    title = {{[LISA] learning analytics for sensor-based adaptive learning}},
    year = {2017}
    }
  • H. Yun, M. Domanska, A. Fortenbacher, M. Ghomi, and N. Pinkwart, “Sensor data for learning support: Achievements, open questions and opportunities,” in Ceur workshop proceedings, 2016.
    [Bibtex]
    @inproceedings{Yun2016,
    abstract = {A recent trend in the field of learning analytics is to use sensor data about learners to support self-regulated learning. Combining personal, sensor based data with log data derived from a learning environment is a very promising approach, but also poses big challenges for the design of learner models and learner interaction methods, for the interpretation techniques of such data, and on applicable learning scenarios with their ethical and privacy demands. This paper provides a brief review of the emerging field of sensory aided learning analytics, and presents first results towards modeling and developing solutions for sensor-based adaptive learning in different learning contexts.},
    author = {Yun, H. and Domanska, M. and Fortenbacher, A. and Ghomi, M. and Pinkwart, N.},
    booktitle = {CEUR Workshop Proceedings},
    issn = {16130073},
    keywords = {Adaptive learning,Learning analytics,Self-regulated learning,Sensors,Smart wearable devices},
    title = {{Sensor data for learning support: Achievements, open questions and opportunities}},
    volume = {1669},
    year = {2016}
    }
  • M. Klüsener and A. Fortenbacher, “Analyse erfolgreicher studenten in massive open online courses,” in Lecture notes in informatics (lni), proceedings – series of the gesellschaft fur informatik (gi), 2015.
    [Bibtex]
    @inproceedings{Klusener2015,
    author = {Kl{\"{u}}sener, M. and Fortenbacher, A.},
    booktitle = {Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)},
    isbn = {9783885796411},
    issn = {16175468},
    title = {{Analyse erfolgreicher studenten in massive open online courses}},
    volume = {247},
    year = {2015}
    }
  • M. Klüsener, W. Konitzer, and A. Fortenbacher, “Interactive visualization for the presentation and assessment of results of learning analytics in forums with many participants | Interaktive visualisierung zur darstellung und bewertungvon learning-analytics-ergebnissen in foren mit vielenteilnehmern,” in Ceur workshop proceedings, 2015, p. 110–117.
    [Bibtex]
    @inproceedings{Klusener2015a,
    author = {Kl{\"{u}}sener, M. and Konitzer, W. and Fortenbacher, A.},
    booktitle = {CEUR Workshop Proceedings},
    issn = {16130073},
    pages = {110--117},
    title = {{Interactive visualization for the presentation and assessment of results of learning analytics in forums with many participants | Interaktive visualisierung zur darstellung und bewertungvon learning-analytics-ergebnissen in foren mit vielenteilnehmern}},
    volume = {1443},
    year = {2015}
    }
  • A. Fortenbacher and N. Pinkwart, “Learning analytics,” in Ceur workshop proceedings, 2015.
    [Bibtex]
    @inproceedings{Fortenbacher2015,
    author = {Fortenbacher, A. and Pinkwart, N.},
    booktitle = {CEUR Workshop Proceedings},
    issn = {16130073},
    title = {{Learning analytics}},
    volume = {1443},
    year = {2015}
    }
  • [DOI] M. Klusener and A. Fortenbacher, “Predicting students’ success based on forum activities in MOOCs,” in Proceedings of the 2015 ieee 8th international conference on intelligent data acquisition and advanced computing systems: technology and applications, idaacs 2015, 2015, p. 925–928.
    [Bibtex]
    @inproceedings{Klusener2015b,
    abstract = {{\textcopyright} 2015 IEEE. Massive Open Online Courses (MOOCs) have the potential to scale university education, allowing for many thousands of students to participate in a single online course. But even successful MOOC platforms like Coursera, edX or Iversity face the problem of very low completion rates. Analyzing learning activities in a MOOC, learning analytics could help to identify necessary interventions. In Iversity MOOCs, a forum is the basic communication platform, both for student-student and student-instructor communication. In this paper, features of successful students are derived from forum activities and combined to a learning profile. From this learning profile, feedback could be generated for students who are classified as 'risk students'. An analytics tool was developed, based on machine learning, which classifies students in Iversity MOOCs, using features like number of answers in a forum or number of up-votes. Thus, features of successful students can be determined, and visualized in an intuitive way.},
    author = {Klusener, M. and Fortenbacher, A.},
    booktitle = {Proceedings of the 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2015},
    doi = {10.1109/IDAACS.2015.7341439},
    isbn = {9781467383615},
    keywords = {Classification,Completion Rate,Learning Profile,MOOC,Predictive Analytics,Social Network Analytics},
    pages = {925--928},
    title = {{Predicting students' success based on forum activities in MOOCs}},
    volume = {2},
    year = {2015}
    }
  • A. Fortenbacher, M. Klüsener, and S. Schwarzrock, “A generic data model for learning analytics | Ein generisches datenmodell für learning analytics,” in Ceur workshop proceedings, 2014, p. 80–87.
    [Bibtex]
    @inproceedings{Fortenbacher2014,
    author = {Fortenbacher, A. and Kl{\"{u}}sener, M. and Schwarzrock, S.},
    booktitle = {CEUR Workshop Proceedings},
    issn = {16130073},
    pages = {80--87},
    title = {{A generic data model for learning analytics | Ein generisches datenmodell f{\"{u}}r learning analytics}},
    volume = {1227},
    year = {2014}
    }
  • A. Fortenbacher and G. Frank, “Checking of learning paths with learning analytics | Überprüfung von lernpfaden mit learning analytics,” in Ceur workshop proceedings, 2014, p. 124–131.
    [Bibtex]
    @inproceedings{Fortenbacher2014a,
    author = {Fortenbacher, A. and Frank, G.},
    booktitle = {CEUR Workshop Proceedings},
    issn = {16130073},
    pages = {124--131},
    title = {{Checking of learning paths with learning analytics | {\"{U}}berpr{\"{u}}fung von lernpfaden mit learning analytics}},
    volume = {1227},
    year = {2014}
    }
  • L. Beuster, M. Elkina, A. Fortenbacher, L. Kappe, A. Merceron, A. Pursian, S. Schwarzrock, and B. Wenzlaf, “Learning analytics and visualization with the LeMo tool | Learning analytics und visualisierung mit dem LeMo-Tool,” in Lecture notes in informatics (lni), proceedings – series of the gesellschaft fur informatik (gi), 2013, p. 245–250.
    [Bibtex]
    @inproceedings{Beuster2013,
    author = {Beuster, L. and Elkina, M. and Fortenbacher, A. and Kappe, L. and Merceron, A. and Pursian, A. and Schwarzrock, S. and Wenzlaf, B.},
    booktitle = {Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)},
    isbn = {9783885796121},
    issn = {16175468},
    pages = {245--250},
    title = {{Learning analytics and visualization with the LeMo tool | Learning analytics und visualisierung mit dem LeMo-Tool}},
    volume = {P-218},
    year = {2013}
    }
  • A. Merceron, M. Elkina, and A. Fortenbacher, “Learning Analytics und Foren,” in Delfi 2013, 2013, p. 139–144.
    [Bibtex]
    @inproceedings{Merceron2013,
    abstract = {Dieser Beitrag reflektiert {\"{u}}ber den Einsatz von Learning Analytics f{\"{u}}r die Analyse von Foren. Nach einer Darstellung der Herausforderungen werden exemplarisch Arbeiten vorgestellt, welche die Vielfalt von Learning Analytics f{\"{u}}r Foren sowohl in den Zielen als auch in die Methoden zeigen. Fragestellungen aus der Praxis (Bildungsanbieter) unterstreichen die Wichtigkeit dieser Thematik.},
    author = {Merceron, Agathe and Elkina, Margarita and Fortenbacher, Albrecht},
    booktitle = {DeLFI 2013},
    pages = {139--144},
    title = {{Learning Analytics und Foren}},
    year = {2013}
    }
  • A. Fortenbacher, “Learning Analytics for higher education – Perspectives and challenges,” in Odes’kyi politechnichnyi universytet. pratsi: scientific, science and technology collected articles, 2013, p. 184–187.
    [Bibtex]
    @inproceedings{Fortenbacher2013,
    abstract = {Learning analytics is a new and emerging issue in higher education. Analyzing students' data, universities want to learn more about their students, teachers would like to monitor performance of individual students, and students might receive feedback from a learning-analytics system. In the project "LeMo: monitoring of the learning process", a prototype of a learning-analytics tool has been developed. It is shown how the LeMo tool can be applied to the analytics needs in higher education.},
    author = {Fortenbacher, Albrecht},
    booktitle = {Odes'kyi Politechnichnyi Universytet. Pratsi: Scientific, science and technology collected articles},
    keywords = {learning analytics,learning management system,quality teaching and learning},
    number = {40},
    pages = {184--187},
    title = {{Learning Analytics for higher education - Perspectives and challenges}},
    volume = {1},
    year = {2013}
    }
  • A. Fortenbacher, L. Beuster, M. Elkina, L. Kappe, A. Merceron, A. Pursian, S. Schwarzrock, and B. Wenzlaff, “LeMo: A learning analytics application focussing on user path analysis and interactive visualization,” in Proceedings of the 2013 ieee 7th international conference on intelligent data acquisition and advanced computing systems, idaacs 2013, 2013, p. 748–753.
    [Bibtex]
    @inproceedings{Fortenbacher2013a,
    abstract = {Abstract – LeMo is the prototype of an applicationfor learning analytics, which collects data about learners'activities from different learning platforms. The articledescribes design principles of LeMo and their implicationsfor efficient learning analytics. Focus is on the LeMo systemarchitecture, user path analysis by algorithms of sequentialpattern mining, and visualization of learners' activities.},
    author = {Fortenbacher, Albrecht and Beuster, Liane and Elkina, Margarita and Kappe, Leonard and Merceron, Agathe and Pursian, Andreas and Schwarzrock, Sebastian and Wenzlaff, Boris},
    booktitle = {Proceedings of the 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems, IDAACS 2013},
    keywords = {learning analytics,sequential pattern mining,user path analysis,visual analytics},
    pages = {748--753},
    title = {{LeMo: A learning analytics application focussing on user path analysis and interactive visualization}},
    volume = {2},
    year = {2013}
    }
  • A. Fortenbacher, L. Beuster, M. Elkina, L. Kappe, A. Merceron, A. Pursian, S. Schwarzrock, and B. Wenzlaff, “Learning Analytics und Visualisierung mit dem LeMo-Tool,” in Gi-edition proceedings band 218 – delfi 2013 – die 11. e-learning fachtagung informatik der gesellschaft für informatik e.v., 2013, p. 245–250.
    [Bibtex]
    @inproceedings{Fortenbacher2013b,
    abstract = {Die Entwicklung des Lernprozess Monitoring Werkzeugs (LeMo) zielt darauf hin, Lehrende, Forschende und Anbieter von e-Learning bei der Analyse von Nutzungsdaten ihrer Online- und Blended-Learning Lernszenarien zu unterstützen. LeMo ermöglichst es Verkehrsdaten sowohl von personalisierenden Lernplattformen, wie Clix oder Moodle, als auch von nicht-personalisierenden Plattformen mit frei zugänglichen Inhalten, auszuwerten. Das Tool ermöglicht verschiedene Analysen, wie zum Beispiel den Verlauf der Intensität der Aktivitäten über die Zeit, die durchschnittliche Nutzung des Angebots zu bestimmten Zeiten in der Woche, das Erkennen häufiger Pfade, einen Graphen über die Navigation zwischen den verschiedenen Lernobjekten eines Kurses und einen Überblick über die durchschnittlichen Testergebnisse. Filtereinstellungen zur Wahl des Zeitraums, der Lernobjekte, des Lernobjekt-Typs, der Nutzergruppe und visuelle Einstellungen erlauben spezifischere Analysen. Ein Hauptaugenmerk bei der Entwicklung des LeMo-Tools liegt auf der Nutzerfreundlichkeit und der dynamischen Visualisierung der Analyseergebnisse.},
    author = {Fortenbacher, Albrecht and Beuster, Liane and Elkina, Margarita and Kappe, Leonard and Merceron, Agathe and Pursian, Andreas and Schwarzrock, Sebastian and Wenzlaff, Boris},
    booktitle = {GI-Edition Proceedings Band 218 - DeLFI 2013 - Die 11. E-Learning Fachtagung Informatik der Gesellschaft f{\"{u}}r Informatik e.V.},
    pages = {245--250},
    publisher = {K{\"{o}}llen},
    title = {{Learning Analytics und Visualisierung mit dem LeMo-Tool}},
    year = {2013}
    }
  • L. Beuster, A. Fortenbacher, A. Merceron, M. Elkina, A. Pursian, B. Wenzlaff, S. Schwarzrock, and L. Kappe, “Prototyp einer plattformunabhängigen Learning Analytics Applikation – fokussiert auf Nutzungsanalyse und Pfadanalyse,” E-learning symposium 2012 – aktuelle anwendungen, innovative prozesse und neueste ergebnisse aus der e-learning-praxis, p. 69–72, 2012.
    [Bibtex]
    @article{Beuster2012,
    abstract = {Bei der hier vorgestellten Demo handelt es sich um den Prototyp einer webbasierten Applikation zur Analyse von Nutzungsdaten der AnwenderInnen von eLearning Angeboten. Die Applikation wurde dabei explizit f{\"{u}}r die Nutzung durch unterschiedliche Zielgruppen wie eLearning-AnbieterInnen, Lehrende und WissenschaftlerInnen entworfen. Die Anwendung kann Daten verschiedener Lernplattformen auswerten und nutzt hierbei Methoden des Educational Data Mining. Sie unterst{\"{u}}tzt dabei sowohl klassische Plattformen mit personalisiertem Zugang sowie offene Plattformen, deren Angebote ohne Authentifizierung der Nutzer erreichbar sind.},
    author = {Beuster, Liane and Fortenbacher, Albrecht and Merceron, Agathe and Elkina, Margarita and Pursian, Andreas and Wenzlaff, Boris and Schwarzrock, Sebastian and Kappe, Leonard},
    journal = {E-Learning Symposium 2012 - Aktuelle Anwendungen, innovative Prozesse und neueste Ergebnisse aus der E-Learning-Praxis},
    pages = {69--72},
    title = {{Prototyp einer plattformunabh{\"{a}}ngigen Learning Analytics Applikation - fokussiert auf Nutzungsanalyse und Pfadanalyse}},
    url = {http://www.uni-potsdam.de/fileadmin/projects/elearning-symposium/assets/tagungsband.pdf},
    year = {2012}
    }
  • A. Fortenbacher and M. Dux, “Mahara und Facebook als Instrumente der Portfolioarbeit und des Self-Assessments,” in Wissensgemeinschaften: digitale medien – öffnung und offenheit in forschung und lehre, Waxmann, Ed., , 2011, p. 220–228.
    [Bibtex]
    @incollection{Fortenbacher2011,
    abstract = {In den vergangenen zwei Semestern wurden an der Hochschule für Technik und Wirtschaft Berlin drei Veranstaltungen mit einem Portfoliokonzept begleitet. Zur Umsetzung der studentischen Arbeiten wurde neben der Anwendung Mahara1 als vergleichende Anwendung eine Gruppe im sozialen Netzwerk Facebook2 einge- setzt. Dieser Artikel stellt das didaktische Konzept zur Begleitung der studen- tischen Arbeiten vor und diskutiert die beiden Anwendungen im Hinblick auf praktikable Einsatzszenarien sowie die Akzeptanz innerhalb der begleiteten Veranstaltungen.},
    author = {Fortenbacher, Albrecht and Dux, Marcel},
    booktitle = {Wissensgemeinschaften: Digitale Medien - {\"{O}}ffnung und Offenheit in Forschung und Lehre},
    editor = {Waxmann},
    isbn = {978-3-8309-2545-3},
    pages = {220--228},
    title = {{Mahara und Facebook als Instrumente der Portfolioarbeit und des Self-Assessments}},
    year = {2011}
    }
  • A. Fortenbacher and M. Dux, “ePortfolios and Agile Methods. A Case Study in eLearning,” in Elearning baltics 2010. proceedings of the 3rd international elba science conference, Stuttgart, 2010, p. 154–159.
    [Bibtex]
    @inproceedings{fortenbacher-dux-portfolio-2010,
    address = {Stuttgart},
    author = {Fortenbacher, Albrecht and Dux, Marcel},
    booktitle = {eLearning Baltics 2010. Proceedings of the 3rd International eLBa Science Conference},
    editor = {Hambach, S and Martens, A and Tavangarian, D and Urban, B},
    pages = {154--159},
    publisher = {Fraunhofer Verlag},
    title = {{ePortfolios and Agile Methods. A Case Study in eLearning}},
    year = {2010}
    }
  • A. Fortenbacher, Perl & CGI, Berlin: Teia Lehrbuch Verlag, 2003.
    [Bibtex]
    @book{Fortenbacher2003,
    address = {Berlin},
    author = {Fortenbacher, Albrecht},
    isbn = {978-3935539703},
    publisher = {Teia Lehrbuch Verlag},
    title = {{Perl {\&} CGI}},
    year = {2003}
    }
  • [DOI] M. Clausen and A. Fortenbacher, “Efficient solution of linear diophantine equations,” Journal of symbolic computation, vol. 8, iss. 1-2, p. 201–216, 1989.
    [Bibtex]
    @article{Clausen1989,
    abstract = {This paper presents a new method for finding complete information about the set of all nonnegative integer solutions of homogeneous and iuhomogeneous linear dio- phantine equations. Such solutions are fundamental for associative-commutative unification. Our algorithm finds all minimal solutions as "monotone" paths in a graph which encodes the linear diophantine equation. This encoding makes re- peated arithmetic operations obsolete and allows inexpensive tests for minimallty of solutions. This graph algorithm compares favourably with the known methods, namely lexicogragraphic algorithm and completion procedure. A PASCAL imple- mentation can be found in the Appendix.},
    author = {Clausen, Michael and Fortenbacher, Albrecht},
    doi = {10.1016/S0747-7171(89)80025-2},
    issn = {07477171},
    journal = {Journal of Symbolic Computation},
    number = {1-2},
    pages = {201--216},
    title = {{Efficient solution of linear diophantine equations}},
    url = {http://www.sciencedirect.com/science/article/pii/S0747717189800252},
    volume = {8},
    year = {1989}
    }