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Uit het vooronderzoekvan het project Duurzamelearning communities: Oogstenin de Greenportblijkt dat12 factorenhierbijvan belangrijk zijn. Deze succesfactoren staan centraal in de interactieve tool Seeds of Innovation. Ook komen uit het vooronderzoek, aangevuld met inzichten uit de literatuur en tips om de samenwerking door te ontwikkelen en meer gebruik te maken van de opbrengsten 12 succesfactoren met toelichting, belangrijkste bevindingen en tips voor ‘hoe nu verder’, Poster, Walk through, De app die learning communities helptde samenwerkingnaareenhogerplan te tillenen innovatieveopbrengstenoptimaalte benutten.
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Presenting novices with examples and problems is an effective and efficient way to acquire new problem-solving skills. Nowadays, examples and problems are increasingly presented in computer-based learning environments, in which learners often have to self-regulate their learning (i.e., choose what type of task to work on and when). Yet, it is questionable how novices self-regulate their learning from examples and problems, and to what extent their choices match with effective principles from instructional design research. In this study, 147 higher education students had to learn how to solve problems on the trapezoidal rule. During self-regulated learning, they were free to select six tasks from a database of 45 tasks that varied in task format (video examples, worked examples, practice problems), complexity level (level 1, 2, 3), and cover story. Almost all students started with (video) example study at the lowest complexity level. The number of examples selected gradually decreased and task complexity gradually increased during the learning phase. However, examples and lowest level tasks remained relatively popular throughout the entire learning phase. There was no relation between students' total score on how well their behavior matched with the instructional design principles and learning outcomes, mental effort, and motivational variables.
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Despite the promises of learning analytics and the existence of several learning analytics implementation frameworks, the large-scale adoption of learning analytics within higher educational institutions remains low. Extant frameworks either focus on a specific element of learning analytics implementation, for example, policy or privacy, or lack operationalization of the organizational capabilities necessary for successful deployment. Therefore, this literature review addresses the research question “What capabilities for the successful adoption of learning analytics can be identified in existing literature on big data analytics, business analytics, and learning analytics?” Our research is grounded in resource-based view theory and we extend the scope beyond the field of learning analytics and include capability frameworks for the more mature research fields of big data analytics and business analytics. This paper’s contribution is twofold: 1) it provides a literature review on known capabilities for big data analytics, business analytics, and learning analytics and 2) it introduces a capability model to support the implementation and uptake of learning analytics. During our study, we identified and analyzed 15 key studies. By synthesizing the results, we found 34 organizational capabilities important to the adoption of analytical activities within an institution and provide 461 ways to operationalize these capabilities. Five categories of capabilities can be distinguished – Data, Management, People, Technology, and Privacy & Ethics. Capabilities presently absent from existing learning analytics frameworks concern sourcing and integration, market, knowledge, training, automation, and connectivity. Based on the results of the review, we present the Learning Analytics Capability Model: a model that provides senior management and policymakers with concrete operationalizations to build the necessary capabilities for successful learning analytics adoption.
MULTIFILE
Nowadays, students often practice problem-solving skills in online learning environments with the help of examples and problems. This requires them to self-regulate their learning. It is questionable how novices self-regulate their learning from examples and problems and whether they need support. The present study investigated the open questions (1) to what extent students' (novices) task selections align with instructional design principles and (2) whether informing them about these principles would improve their task selections, learning outcomes, and motivation. Higher education students (N = 150) learned a problem-solving procedure by fixed sequences of examples and problems (FS-condition), or by self-regulated learning (SRL). The SRL participants selected tasks from a database, varying in format, complexity, and cover story, either with (ISRL-condition) or without (SRL-condition) watching a video detailing the instructional design principles. Students' task-selection patterns in both SRL conditions largely corresponded to the principles, although tasks were built up in complexity more often in the ISRL-condition than in the SRL-condition. Moreover, there was still room for improvement in students' task selections after solving practice problems. The video instruction helped students to better apply certain principles, but did not enhance learning and motivation. Finally, there were no test performance or motivational differences among conditions. Although these findings might suggest it is relatively ‘safe’ to allow students to independently start learning new problems-solving tasks using examples and problems, caution is warranted: It is unclear whether these findings generalize to other student populations, as the students participating in this study have had some experience with similar tasks or learning with examples. Moreover, as there was still room for improvement in students' task selections, follow-up research should investigate how we can further improve self-regulated learning from examples and practice problems.
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Problem-solving tasks form an important part of (higher education) curricula, especially in STEM-domains. For learners with little or no prior knowledge (novices), an effective way to learn new problem-solving tasks is by studying examples. These can be written out step-by-step solution procedures of a problem or teachers’ demonstrations of how to solve a problem. Nowadays, video examples are increasingly common. Moreover, students increasingly acquire problem-solving skills via computer-based learning environments in which examples and practice problems are presented. However, it is an open question how examples and practice problems can be best sequenced to foster novices’ motivation and learning outcomes. Moreover, relatively little is known about how (well) novices can self-regulate their learning with examples and practice problems. Both questions were addressed in this dissertation. Results showed that studying examples or alternating examples and practice problems, resulted in higher learning outcomes attained with less effort investment and more confidence in one's abilities than solving practice problems only. Moreover, starting with an example prior to practice problem solving resulted in more confidence in one's abilities and less effort investment than the other way around. When novices could select examples and practice problems themselves, they made choices that corresponded quite well with principles for effective sequencing known from instructional design research. Perhaps for that reason, instructing students on effective instructional design principles did not increase self-regulated learning outcomes. However, caution is needed when implementing self-regulated learning: even after instruction on effective principles, there still was room for improvement in students' task selections.
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Learning objects are bits of learning content. They may be reused 'as is' (simple reuse) or first be adapted to a learner's particular needs (flexible reuse). Reuse matters because it lowers the development costs of learning objects, flexible reuse matters because it allows one to address learners' needs in an affordable way. Flexible reuse is particularly important in the knowledge economy, where learners not only have very spefic demands but often also need to pay for their own further education. The technical problems to simple and flexible are rapidly being resolved in various learning technology standardisation bodies. This may suggest that a learning object economy, in which learning objects are freely exchanged, updated and adapted, is about to emerge. Such a belief, however, ignores the significant psychological, social and organizational barriers to reuse that still abound. An inventory of these problems is made and possible ways to overcome them are discussed.
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This paper focuses on the changes in education and their implication for the university library. The impact of Avans's strategic educational vision on the design and lay-out of the buildings. Especially Xplora, the Learning Centre of Avans, will be described. The three locations of the Avans Learning Centre (opened up in 2006 and 2007) comprise a total of 2,000 student workplaces. The traditional library has changed into a multimedia learning centre and now resides under the Avans's Learning and Innovation Centre. New buildings and a new organisational structure demand new working arrangements with faculty staff. The transformation from library to learning centre and especially the consequences for library staff will be focussed upon. All staff were offered a comprehensive training programme. In addition, information specialists were trained to improve their acquaintance with educational knowledge. The benefits derived from the cooperation between library staff and colleagues from other disciplines (e.g. educational consultants, e-learning consultants, multimedia staff etc.) within the Avans Learning and Innovation Centre will be described. The results of relevant student surveys will also be described. At the end some conclusions will be drawn based on four years of working with new buildings, new educational models, a new organisation, new working arrangements, etc.
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This article reports on a literature review on empirical research investigating learning for vocations in the context of vocational education. We included 36 studies in which learning for vocations is empirically studied. Learning for vocations is characterised based upon prevalent research traditions in the field and framed from the perspective of vocational education and organised learning practices. This framing and characterisation directed the search terms for the review. Results show empirical data on vocational learning and illustrate how learning processes for the functions of vocational education - vocational identity development, development of a vocational repertoire of actions, and vocational knowledge development - actually take place. The review further shows that, empirical illustrations of learning processes that occur in the context of vocational education and organised learning practices are relatively scarce. The findings can be typified in relation to our theoretical framework in terms of three learning processes, that is learning as a process of (a) belonging, becoming, and being, (b) recontextualization, and (c) negotiation of meaning and sense-making. We argue that more empirical research should be carried out, using the functions of vocational education and the three learning processes to better understand vocational learning.
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Conference Paper From the article: Abstract Learning analytics is the analysis and visualization of student data with the purpose of improving education. Literature reporting on measures of the effects of data-driven pedagogical interventions on learning and the environment in which this takes place, allows us to assess in what way learning analytics actually improves learning. We conducted a systematic literature review aimed at identifying such measures of data-driven improvement. A review of 1034 papers yielded 38 key studies, which were thoroughly analyzed on aspects like objective, affected learning and their operationalization (measures). Based on prevalent learning theories, we synthesized a classification scheme comprised of four categories: learning process, student performance, learning environment, and departmental performance. Most of the analyzed studies relate to either student performance or learning process. Based on the results, we recommend to make deliberate decisions on the (multiple) aspects of learning one tries to improve by the application of learning analytics. Our classification scheme with examples of measures may help both academics and practitioners doing so, as it allows for structured positioning of learning analytics benefits.
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The principal aim of this study is to explore the relations between work domains and the work-related learning of workers. The article is intended to provide insight into the learning experiences of Dutch police officers during the course of their daily work. Interviews regarding actual learning events and subsequent changes in knowledge, skills or attitudes were conducted with police officers from different parts of the country and in different stages of their careers. Interpretative analyses grounded in the notion of intentionality and developmental relatedness revealed how and in what kinds of work domains police officers appear to learn. HOMALS analysis showed work-related learning activities to vary with different kinds of work domains. The implications for training and development involve the role of colleagues in different hierarchical positions for learning and they also concern the utility of the conceptualisation of work-related learning presented here.
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