The STELLAR Network of Excellence has developed a set of Grand Challenge Problems in Technology Enhanced Learning. These are aimed at engaging creative minds across scientific disciplines to work on solutions that could lead to breakthroughs that improve learning and educational systems in Europe.
In order to help us develop a system for prioritising the STELLAR Grand Challenge Problems we invite you to rate each problem, using criteria that have been developed by stakeholders and researchers within STELLAR.
Below is the full list of challenges for you to inspect.
This Grand Challenge Problem (GCP) highlights new opportunities for improving teaching that arise from the introduction of information and communication technologies (ICTs) in learning settings. The use of ICTs in learning settings creates a new channel of information for teachers and learners by generating data as a side product of learning activities. The massive amount of data (real time and outcome data) represents a challenge and opportunity at the same time: The main challenge for future teachers and learners is to make sense and intelligent use of the data provided by ICTs in order to facilitate learning. This GCP aims to incorporate the data that becomes available with the use of ICTs in the teaching and learning practices.
We propose to investigate the potential of real time data as well as summative/outcome data for deliberately informing teachers about their students’ progress and success in learning and for providing feedback to students.
In order to provide teachers with intelligent technology that assists them in monitoring their students’ learning progress, the following research questions have to be investigated: What real time data do teachers need for monitoring their students? And how can this data be collected and presented in an efficient and useful way? How can teachers adapt their teaching after having received real time data in order to improve their students learning? How can students themselves benefit from real time data collection? As an example, can students be challenged cognitively or provided with feedback through representations of real time data?
Another field of application for data usage is the summative assessment of student learning outcomes: How can we analyze students' usage data (i.e., stored by TEL tools such as learning management system) to identify conditions that impede or facilitate student achievement?
In order to create a foundation for the learning societies of the 21st century, we need to rethink the way we support interest-driven lifelong learning. Moving responsibility for education beyond ‘school-only’ to lifelong learning requires and empowers the individual learner to navigate and manage their own learning trajectories in networked learning ecologies. The notion of networked learning ecologies aims to stress and understand the inter-connections and the knowledge flow between people and resources in differently tied networks (collaborative, individual) of varying scale (group to mass-collaboration), with changing participants and contexts (work-based, institutional, non-institutional). The emphasis of this GCP is on providing a fuller picture of learning trajectories and developing organic, continually changing technologies (networked learning ecologies) which support these complex trajectories.
The development of these networked learning ecologies requires first a deep understanding of how people or groups collect information, make sense of it and create knowledge among them over time. One approach would be to examine learners at different stages of their expertise/lifespan and to investigate the following questions: What pathways/trajectories have these people taken to reach their professional practice and expertise? Which institutions, learning settings, contexts (physical and digital) were involved and at which stages in their learning processes? What skills and influences were enabling factors on their journey?
Overall, the networked learning ecologies should afford that a) learners can individually or collectively monitor, follow, re-represent and document their learning in continually updated portfolios; b) learners can connect with each other and the wider world and form or join groups/communities thus creating personal learning networks. In addition, this personal space of lifelong learning would require a) interoperability in data sharing between different educational organisations and b) data privacy and management regulations.
More than three decades of media effects research have demonstrated what Clark (1983) asserted long ago: Technology integration research can no longer focus upon educational technologies' effects, since it is how the technologies are used pedagogically, in terms of curriculum, and relative to learners' needs and preferences that determine the success or failure of a particular technological use. Improved student learning results from appropriate 'matches' among curriculum-based learning goals, the learning activities, and the tools/resources that support those activities. Practitioners often seek advice on how to match these factors effectively. The main question that this GCP is addressing is the following: Which technologically-supported curriculum-based learning activities are most effective for which learning goals?
In order to solve this challenge, we need to develop, test, refine, and apply a strategy for testing the efficacy of technology-supported learning activities within specific curriculum content areas and for students with particular learning needs. First, we could look at technologies that have been developed far enough to be easily usable by teachers who are not experts in educational technology, and investigate for which learning tasks/goals they can be applied. In more generic terms, we should ask: What are the context factors that make a specific best practice a success? What is the role of teachers when using ICT in the classroom? What kind of knowledge / skills do teachers and pupils need to benefit from ICT (in relation to the Technological Pedagogical Content Knowledge framework)? How can we embed ICT in a smooth/seamless way through blending technology-based and non-technology instruction?
In the end, the best practices identified from many similarly structured studies can advise teachers when they are planning curriculum-based, student-centered instruction.
Both hardware (smartphones, tablet/slate PCs, etc.) and software/services are rapidly developing towards increasing personalization and adaptation to the individual user. The current trend in the educational sector is to personalize instruction to best assist each individual student/learner. In order to develop technologies for personalization of learning, the dynamic and adaptive monitoring, diagnosing, and remediation of root-causes of educational problems have to be explored.
This GCP addresses the questions many educational technologist and computer scientists are struggling with on a daily basis in designing personalized learning environments (PLEs) concerning learner support and interoperability: What variables are essential to assess when monitoring/adapting instruction to individual learner's needs? How do these variables interact – how should they be weighed in an assessment or task selection algorithm? How can the experience of past learners be effectively aggregated to inform recommendations for future learners? How to reduce the cost of developing intelligent tutoring systems (ITS)? How to apply ITS to ill-defined domains and to integrate more elaborated pedagogical strategies in ITS? How to create an intelligent agent who models the behaviour of an effective human tutor? How to create shareable and reusable e-learning designs? How can learning management systems and PLEs interact/complement each other? How can methods be explored for adaptive learning objects retrieval form learning objects repositories?
First of all, receiving answers/solutions to the multiple questions and issues illustrated above should be the basis for identifying design principles for PLEs. The next step is the integration of these technological solutions with educational goals/theories in a generic PLE and to test these PLEs in schools/universities/businesses from all over the world. It would also be important to implement these PLEs in several educational phases - e.g., primary education, K-12, secondary education, and workplace learning.
Personalized learning environments (PLEs) aim to offer learning experiences that are tailored to the individual learning profile. Existing theories for creating PLEs are often partial, contradictory and cannot provide good models for practice. Researchers and industry have to involve pilot schools, universities and companies in extensive and large-scale participatory design research focusing on next-generation PLEs. This will enhance our understanding of individual learning paths in relation to personal differences.
Before designing any TEL application for personalization of learning, a number of conceptual issues need to be resolved. For example, many studies do not distinguish between level type of constructs (ability, intelligence; the question here is, how much?) and style constructs (cognitive style, learning style; the question here is, in what way?). This conceptual confusion leads to wrong technological solutions.
Therefore, this GCP addresses main conceptual research questions: Does a personalized trajectory lead to better learning than one-size-fits–all solutions? Who should be responsible for personalization (the system or the learner)? Does a self-directed PLE lead to more motivation and less frustration? These generic research questions have to be complemented by questions focusing on the underlying pedagogy and the implementation of PLEs: What learning designs best support personalised learning? How is prior knowledge accounted for in the personalization process? How to reach a predefined accredited level of expertise? How can we offer ill-structured and ill-defined learning in PLE? How to promote and sustain teachers’ interest in and enthusiasm about PLEs? How to build secure personalized/student owned databanks (secure privacy and portability of data by student control of his/her data)?
First of all, receiving answers/solutions to the questions and issues illustrated above should be the basis for identifying design principles for PLEs. In the next step, scale is the key for testing PLEs based on these principles and involving schools/universities/businesses from all over the world would add to the research. It would also be important to test the hypotheses in different educational phases - e.g., primary education, K-12, secondary education, and workplace learning.
In today's knowledge society, mobile devices and other technological innovations have changed the basic conditions for learning, and introduced new learning spaces in informal learning settings and to a lesser extend in formal learning settings. The resources that especially young people use for learning and constructing knowledge can be characterized by mobility and multiplicity and ubiquitous access to multiple resources for information. This means that schools are not the only privileged source of knowledge; young people participate and learn in a broad range of contexts and have to translate/transform knowledge between these spaces. This characteristic of today’s learning landscape leads to either an interconnectedness or divide between student learning inside and outside the formal classroom.
First of all, we need to better comprehend the characteristics of how students smooth over the boundaries between contexts, especially between school and informal learning settings by answering the following research questions: Which technologies are used and how are they used by students in informal learning contexts? What are the cognitive, emotional and motivational processes when learning in informal learning contexts as opposed to learning in the formal classroom? How do students translate/transform knowledge between those contexts? After generating empirical evidence on these research questions, a comprehensive framework describing the interconnectedness of learning in formal and informal settings should be created. In a second step, we should explore and evaluate possibilities for facilitating, that is, orchestrating, the translation of knowledge between informal and formal contexts for ultimately creating a unified learning landscape.
Researchers specializing in formal learning as well as informal learning should work together with practitioners in both learning contexts for investigating the research questions presented above.
Indicators for the increasing number of unmotivated students are high attrition rates and low interest in school, especially in STEM subjects. Debates about academic achievement often focus on cognitive aspects and neglect the role motivation plays in it. In general, the importance of intrinsic motivation isn't new, but the education systems around the world should get on board quickly and should aim to make learning personally rewarding and valuable for every student. Technology could be a useful instrument for providing learning experiences that meet those goals. It could provide tools to educators, researchers, parents, and learners that enable self-directed learning driven by need for mastery.
In order to examine the potential of technology-enhanced learning for increasing students’ motivation, the following research questions should be investigated: What factors in TEL environments promote self-directed and intrinsically motivated learners? Extrinsic rewards have been shown to impede intrinsic motivation. Can ICTs offer alternatives to standardized testing and extrinsic/conditional rewards for good performance? How can technology relate learning in school to self realization, self expression, and identity formation?
While enabling students to be intrinsically motivated learners, we should not forget those students who are unmotivated and disengaged from learning as a whole and ask: How to identify disengaged and unmotivated learners? How can they be re-engaged in the learning process? How can we identify and differentiate states of demotivation and unmotivation? How can we remediate these different kinds of states in a way that improves the situation and causes minimal negative side-effects?
Overall, the implementation of potentially motivating technology-enhanced learning environments should lead to measurable increases in student motivation and in a second step to increases in academic achievement.
We live in a digital world that demands new skills and literacies. Being 'literate' in today's society has wider implications beyond typographic text. There is a need to develop more understanding into the way in which people construct and interpret multimodal texts. Students are increasingly exposed to an ever-widening array of graphical representations. Graphical and digital literacy is crucial for all STEM domains and as interactive graphical systems become ever more ubiquitous, students must be equipped to exploit them for their own applications. Taken together, students' digital literacy skills require increasing 'graphical literacy' or graphicacy as well as literacy. However, there is very little direct instruction in the selection, creation, and application of graphics and multimodal texts.
First, we need a better understanding of how students acquire graphicacy skills and common graphical misconceptions as well as how to best teach student teachers effective principles for assigning particular representational forms to educational contexts and problems, i.e., what some researchers have termed the 'applicability conditions'. Additionally, further examination is required into the way in which students 'read' multimodal texts and the way in which such texts can be integrated into the formal educational context by teachers. In a second step, multimodal texts and graphical teaching materials have to be designed. These study material should be based on the implications for formal literacy education that were revealed in the research advances described above. In addition these materials should support teachers in their efforts to design and implement lessons on digital literacy skills.
The clarification of the skills students acquire for deciphering multimodal texts and complex graphical representations is the first step towards solving this GCP. The cognitive processes that are relevant for learning with multimodal texts and multiple graphical representations should be described in an empirically-tested model or theory.
The daily work practices in health care and medicine require skills for imagining physical processes that are invisible to the human eye. Modern technologies can be used for visualizing the hidden processes through creating an augmented-reality (AR), a virtual, visual layer on top of the actual captured images. Students and nurses in medical training could view and experience former imperceptible medical procedures and create richer representations and deeper understanding of bio-physiological mechanisms. In the long-run, the integration of AR in medical and health care practices might lower error rates in diagnosis and treatments.
For developing a mobile AR tool for medical and health care training, a multidisciplinary team should base their work on the extended corpus of research within the field of mobile learning, medical training and visualization programming for answering the following research questions: Which medical tasks are appropriate and suitable to be visualized? Which support structures (content, pedagogy, contextualization) are needed by learners and practitioners to use the tool successfully? What are the human factors contributing to or limiting the up-take of ubiquitous and mobile learning tools such as the mobile AR tool within healthcare and medicine?
This GCP involves the following milestones: analysis of medical tasks and writing of software scripts, development of educational software tools, evaluation of these tools in laboratory and real-life settings, implementation and evaluation of AR tools in hospitals and medical practices in several European countries.
In order to foster computer-supported collaborative learning (CSCL) in classrooms, teachers have to develop professional practices for implementing various types of CSCL activities with ease and confidence. It is known that a teacher’s own learning experiences are reflected in his/her teaching style; therefore, teacher education has to be renewed to include new teaching methods such as CSCL with teachers as active learners. In addition, continuous professional teacher development could benefit from networked teachers who form a community of practice to build and share professional knowledge.
When implementing changes to the teacher education curriculum and the professional development of teachers, the following research questions/issues have to be investigated: How can a community of teachers grow and remain active through the implementation of CSCL? How and to what extent do networked learning environments enhance technological, pedagogical, and content knowledge for teachers? What is the impact these activities may have on teachers' expertise and their professional practices in classroom and on their students’ learning?
The renewed teacher education curriculum has to be monitored for difficulties and barriers (formative evaluation) and evaluated against the professional standards that the teachers in training have to reach (summative evaluation). Additionally, the changes in teacher education and teachers’ continuous professional development should be reflected in the quality of their teaching practice concerning the successful integration and execution of CSCL-activities in the lesson plans.