Prof. Dr. Marcus Specht is Professor for Digital Education at the Technical University of Delft and Director of the Leiden-Delft-Erasmus Center for Education and Learning. He received his Diploma in Psychology in 1995 and a Dissertation from the University of Trier in 1998 on adaptive information technology. From 2001 he headed the department "Mobile Knowledge" at the Fraunhofer Institute for Applied Information Technology (FIT). From 2005 to 2018 he was Professor for Learning Technologies at the Open Universiteit Nederland and head of the Learning Innovation Lab. His research focus is on Mobile and Contextualized Learning Technologies and Social and Immersive Media for Learning. Prof. Specht is an Apple Distinguished Educator and was President (2013-2015) of the International Association of Mobile Learning.
Mobile, context-aware and seamless learning
I started doing research on mobile learning in the domain of museum information systems and museum pedagogics (Gross & Specht, 2001; Oppermann, Specht, & Jaceniak, 1999; Oppermann & Specht, 2000). In that setting I have researched different approaches for ubiquitous user modeling and context-aware mobile learning in rich physical environments. Most of this work has been continued in context-aware learning (De Jong, Specht, & Koper, 2008; Jong, Specht, & Koper, 2010; Zimmermann, Specht, & Lorenz, 2005) in architecture and teacher education.
In the last 8 years I differentiated this research into different components of a seamless learning environment taking into account the learning in situ and contextualized support (De Jong et al., 2008; Jong, Specht, Koper, & de Jong, 2008) by mobile social software, the distribution of information and approaches for contextualized learning support in distributed systems (Glahn & Specht, 2010; Verpoorten, Glahn, Kravcik, Ternier, & Specht, 2009) and the use of situated and ambient displays for learning (D Börner, Kalz, & Specht, 2014; Dirk Börner, Kalz, & Specht, 2013) and applied situated display technology in different school and professional environments. Most of this work is also documented in a book from 2015 I co-edited with Marcelo Milrad and Lung-Hsiang Wong (Wong, Milrad, & Specht, 2015).
In the last three years I focused on the use of sensor information (Schneider, Börner, van Rosmalen, & Specht, 2015) and the design of real-time feedback (Schneider, Börner, Rosmalen, & Specht, 2014a, 2014b) in ubiquitous and seamless learning support. Other works in this domain also include links to learning analytics and personal experience sampling with mobiles (Tabuenca, Kalz, Ternier, & Specht, 2014).
Personalised Learning Technologies
Actually since my PhD I worked on personalized learning technology and different research aspects of it. In the 90’s I did some work on Intelligent Tutoring Systems for learning programming and Statistics (Specht & Oppermann, 1998; Specht, Weber, Heitmeyer, & Schöch, 1997) this work was also related to recommender systems and pedagogical agents in the context of web-based learning systems (Schöch et al., 1998).
Shifting in perspective from control-based systems towards human interaction focused systems is well described in the paper I wrote with a group of people working on personalization for human learning (Dominique Verpoorten, Glahn, Kravcik, Ternier, & Specht, 2009) and this especially for personalized reflection support directed my work towards Learning Analytics and Dashboards for self-regulation and reflection support (Jivet, Scheffel, Drachsler, & Specht, 2017; D. Verpoorten, Westera, & Specht, 2011). Recent works in Learning Analytics combine sensor-networks, augmented reality, and AI to create intelligent interactive systems also embedded in real-world learning and training (Praharaj, Scheffel, Drachsler, & Specht, 2018; Schneider, Börner, van Rosmalen, & Specht, 2015).
For future work on personalized learning I think several developments are interesting and worth some exploration: a) new forms of AI for supporting personalized learning experiences with much stronger AI components for individualized coaching, assessment and training b) massive scale access to learning content and c) real-time manipulation of content and feedback also based on sensor networks.
Game-based learning and Game Design Patterns
In the last ten years I did research on using game design patterns in instructional design and learning solutions. In my research I started mostly from a design-pattern approach and applied different combination of game-design pattern for gamification of learning solutions. In two PhD thesis the overall effects of game-design patterns on learning outcomes have been analyzed for desktop computer games (Kelle, Klemke, & Specht, 2013) as also for mobile games (Schmitz, Klemke, & Specht, 2012). A recent PhD project on mobile learning games has focused on the effects of game patterns in pervasive mobile learning and coupled games to combine real world learning on the job with instruction and informal learning support (Schmitz, Klemke, Walhout, & Specht, 2015). I am currently continue this line of work with two new PhD students about real-time feedback and game design in sensor-based learning systems (Marcus Specht, 2014).
Collaborative and distributed learning technology
From 2001 to 2005 I coordinated the European Research Project Remote Access to Field Trips (RAFT) with 2.9 Mil Euro project funding. In the project we developed new technologies to extend current field trip approaches with video-conferencing, distributed task management, and shared data collection in school excursions (M. Kravcik, Kaibel, Specht, & Terrenghi, 2004; Milos Kravcik et al., 2003; M Specht, Kaibel, & Apelt, 2005; Marcus Specht & Kravcik, 2006). Research enabled remote participation and connection of distributed groups working on inquiry-based learning scenarios. From 2012 to 2015 I coordinated the European Project weSPOT (wespot.net) with 2.9 Mil Euro funding, which focused on mobile inquiry-based learning in schools and higher education making use of mobile technologies for linking formal and informal learning support (Firssova et al., 2014; Kalz et al., 2014; Mikroyannidis et al., 2013).
Out of this research close cooperation with several Dutch schools in our close environment developed as also with the AGORA school. In the AGORA school system all classical curricula and scheduling components are dropped and the students basically work with a SCRUM solution to develop their individual learning plans and work on them in groups as also monitor their progress. In my recent research I am focusing on these possibilities of agile learning and making use of agile planning and agile processes in learning and especially school contexts.
Selected Publications 2018/2019
Mobile and Inquiry-Based Learning
Suárez, Á., Specht, M., Prinsen, F., Kalz, M., & Ternier, S. (2018). A review of the types of mobile activities in mobile inquiry-based learning. Computers & Education, 118, 38-55.
Specht, M., & Suarez, A. (2018). Eine Analyse von Interaktionsmustern für mobiles forschendes Lernen. In Handbuch Mobile Learning (pp. 345-363). Springer VS, Wiesbaden.
Rusman, E., Ternier, S., & Specht, M. (2018). Early Second Language Learning and Adult Involvement in a Real-World Context: Design and Evaluation of the" ELENA Goes Shopping" Mobile Game. Journal of Educational Technology & Society, 21(3).
Jivet, I., Scheffel, M., Specht, M., & Drachsler, H. (2018, March). License to evaluate: Preparing learning analytics dashboards for educational practice. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 31-40). ACM.
Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2018). From signals to knowledge: A conceptual model for multimodal learning analytics. Journal of Computer Assisted Learning, 34(4), 338-349.
Augmented Reality and Sensor Technology
Specht, M., Hang, L. B., & Barnes, J. S. (2019). Sensors for Seamless Learning. In Seamless Learning (pp. 141-152). Springer, Singapore.
Limbu, B. H., Jarodzka, H., Klemke, R., & Specht, M. (2018). Using sensors and augmented reality to train apprentices using recorded expert performance: A systematic literature review. Educational Research Review.
Limbu, B. H., Jarodzka, H., Klemke, R., Wild, F., & Specht, M. (2018). From AR to expertise: A user study of an augmented reality training to support expertise development. Journal of Universal Computer Science, 24(2), 108-128.
Praharaj, S., Scheffel, M., Drachsler, H., & Specht, M. (2018, September). Multimodal Analytics for Real-Time Feedback in Co-located Collaboration. In European Conference on Technology Enhanced Learning (pp. 187-201). Springer, Cham.
Schneider, J., Börner, D., van Rosmalen, P., & Specht, M. (2018). Do you Want to be a Superhero? Boosting Emotional States with the Booth. J. UCS, 24(2), 85-107.
Adoption of ICT in Higher Education
Jokiaho, A., May, B., Specht, M., & Stoyanov, S. (2018). Obstacles to using E-Learning in an Advanced Way. In The International Conference on E-Learning in the Workplace.
Jokiaho, A., May, B., Specht, M., & Stoyanov, S. (2018). Barriers to using E Learning in an Advanced Way. International Journal of Advanced Corporate Learning (iJAC), 11(1), 17-22.
Gamification of Learning
Facey-Shaw, L., Specht, M. M., Van Rosmalen, P., Boerner, D., & Bartley-Bryan, J. (2017). Educational Functions and Design of Badge Systems: A Conceptual Literature Review. IEEE Transactions on Learning Technologies.
Antonaci, A., Klemke, R., Kreijns, K., & Specht, M. (2018). Get Gamification of MOOC right!. International Journal of Serious Games, 5(3), 61-78.
Facey-Shaw, L., Specht, M., Bartley-Bryan, J., & van Rosmalen, P. (2018). Technological and Implementation Issues in Moodle-Based Digital Badge System. In ECGBL 2018 12th European Conference on Game-Based Learning (p. 82). Academic Conferences and publishing limited.
Specht, M., & So, H. J. (2018, November). Mobile Inquiry-based Learning: Relationship among levels of inquiry, learners’ autonomy and environmental interaction. In World Conference on Mobile and Contextual Learning (pp. 22-29).
Please contact me if you are interested in one of these topics for doing a master thesis.
Learning Analytics & Analytics Infrastructure
Visual analytics dashboard for emotion tracking.
Description: Using visual metaphors, the visual analytics system enables teachers to monitor real-time learner emotional states (positiveness, arousal, dominance) be it individual or group work in classroom or online learning environment. In this way, teachers can manage the learning process and gauge the effectiveness of the learning activities and. teaching strategies.
Power to the learner: Effects of selecting personal data sources in dashboard building
Description: Learner agency supports the idea that learners should own their learning. For example, learners should create and follow their own learning goals and select the learning strategies and learning resources they wish to study. This project would look at how learning analytics can be used to support and develop learner agency by developing tools that allow learners to set their own goals, monitor their progress towards these goals and choose personal data sources that they wish to monitor.
Transparency in learning analytics interfaces
Description: Many learning analytics algorithms and systems that personalise interfaces or make decisions automatically for their users are often seen as black boxes, hiding the intricacies of decision making. This often leaves teachers and learners in the dark and erodes their trust in the learning analytics system. How can transparency be thus implemented in such a way that it’s not trivial, but at the same time does not overwhelm its users?
Writing Analytics and Feedback
Description: The project should focus on the analysis of student’s text-based assignments and the analysis of student submissions. The work should explore different ways to develop anc expert model and give feedback to learners based on their submitted text. The project can also be jointly developed with a company in the field of educational technologies. Feedback can be used for students or educators on formal text criteria but also on specified assignments in one domain.
Augmented and Virtual Reality
Building Virtual Reality Escape Rooms for joint problem-solving in collaborative virtual environments
Description: Virtual Reality (VR) shows great promise for educational processes such as experiential- and collaborative learning. To explore VR’s possibilities in these areas, this project will focus on creating Virtual Reality Escape Rooms, where users will have to work together in virtual space and solve puzzles in order to succeed. By designing, developing and testing such an application, the goal is to study different aspects of collaborative learning and how these relate to the virtual world of VR.
Exploring collaborative affordances of handheld augmented reality devices.
Description: Augmenting collaboration is an affordance of Augmented Reality (AR) mediums. As compared to traditional platforms, AR allows you to not only have physical shared space but also a virtual shared space, providing affordances for new methods of collaboration. However, because AR is still evolving, exploration of such affordances must be done.
A Path to success: Exploring AI/MLalgorithms for intelligent training planners.
Description: A key task of the coach/mentor is to plan practice routines keeping focus on the performance and weakness of the trainee. The mentor balances challenge and competence during practice, enables deliberate practice for efficient attainment of mastery while also maintaining the motivation of the trainee. This takes up a lot of time of the expert which is costly. AI/ML algorithms look promising for such orchestration of practice by feeding trainee data to automatically suggest optimal practice paths.
Back the past: Augmented storytelling for immersive history learning.
Description: The first wave of AR was heavily focused on technological advances, answering the question “Can we do this?”. However, now AR has matured technologically but much of the work still focuses on technical aspects lacking user studies and application of such technology in the real world. Therefore, much user experience related exploration is needed to define and objectify the benefits of AR as a medium. One such benefit is the potential of AR to redefine story telling. Using AR story can now revolve around the user, removing the user from being a static consumer of information.
Mobile and Seamless Learning
Mobile app for community trail e.g., Delft heritage/ architectural trail.
Description: Within an Inquiry-based learning framework that fosters user-generated enquiries, the app should enable HE students to interact with the physical environment; draw on prior knowledge resources, conceptual resources and contextual resources to create micro-sites for learning and create learning artifacts individually and/ or collectively.
Creating an experience sampling tool for personal data collection in quantified self. Quantified
Description: Self is a methodology for exploring hypotheses about personal behaviour and for behaviour change. The experience sampling tool should enable learners to specify contextual parameters (location, time, social contact) for which they would like to collect data points. Driven by the specified trigger data can be collected from sensor systems, web-based services, and personal log-book entries. These data entries can later be analysed to evaluate the given hypotheses based on the collected data.
Gamifying deliberate practice: Implications of computer games design principles for vocational skills training
Description: Gamification has been hailed as the tool for increasing engagement, and to certain extent the motivation of the user. On the other hand, maintaining motivation and engagement in deliberate practice is a challenge. Thus, this project will explore the effects of gamification and design implications specific towards the requirements of deliberate practice.
Prof. dr. M.M. Specht
2628 CD Delft
EEMCS, Web Information Systems
P.O. Box 5031, 2600 GA Delft