S-COL: A Copernican turn for the development of flexibly reusable collaboration scripts

Collaboration scripts are usually implemented as parts of a particular collaborative-learning platform. Therefore, scripts of demonstrated effectiveness are hardly used with learning platforms at other sites, and replication studies are rare. The approach of a platform-independent description language for scripts that allows for easy implementation of the same script on different platforms has not succeeded yet in making the transfer of scripts feasible. We present an alternative solution that treats the problem as a special case of providing support on top of diverse Web pages: In this case, the challenge is to trigger support based on the recognition of a Web page as belonging to a specific type of functionally equivalent pages such as the search query form or the results page of a search engine. The solution suggested has been implemented by means of a tool called S-COL (Scripting for Collaborative Online Learning) and allows for the sustainable development of scripts and scaffolds that can be used with a broad variety of content and platforms. The tool’s functions are described. In order to demonstrate the feasibility and ease of script reuse with S-COL, we describe the flexible re-implementation of a collaboration script for argumentation in S-COL and its adaptation to different learning platforms. To demonstrate that a collaboration script implemented in S-COL can actually foster learning, an empirical study about the effects of a specific script for collaborative online search on learning activities is presented. The further potentials and the limitations of the S-COL approach are discussed

The method of educational assessment affects children’s neural processing and performance: behavioural and fMRI Evidence.

Standardised educational assessments are now widespread, yet their development has given comparatively more consideration to what to assess than how to optimally assess students’ competencies. Existing evidence from behavioural studies with children and neuroscience studies with adults suggest that the method of assessment may affect neural processing and performance, but current evidence remains limited. To investigate the impact of assessment methods on neural processing and performance in young children, we used functional magnetic resonance imaging to identify and quantify the neural correlates during performance across a range of current approaches to standardised spelling assessment. Results indicated that children’s test performance declined as the cognitive load of assessment method increased. Activation of neural nodes associated with working memory further suggests that this performance decline may be a consequence of a higher cognitive load, rather than the complexity of the content. These findings provide insights into principles of assessment (re)design, to ensure assessment results are an accurate reflection of students’ true levels of competency

E-learning interventions are comparable to user's manual in a randomized trial of training strategies for the AGREE II

<p>Abstract</p> <p>Background</p> <p>Practice guidelines (PGs) are systematically developed statements intended to assist in patient and practitioner decisions. The AGREE II is the revised tool for PG development, reporting, and evaluation, comprised of 23 items, two global rating scores, and a new User's Manual. In this study, we sought to develop, execute, and evaluate the impact of two internet interventions designed to accelerate the capacity of stakeholders to use the AGREE II.</p> <p>Methods</p> <p>Participants were randomized to one of three training conditions. 'Tutorial'--participants proceeded through the online tutorial with a virtual coach and reviewed a PDF copy of the AGREE II. 'Tutorial + Practice Exercise'--in addition to the Tutorial, participants also appraised a 'practice' PG. For the practice PG appraisal, participants received feedback on how their scores compared to expert norms and formative feedback if scores fell outside the predefined range. <it>'</it>AGREE II User's Manual PDF (control condition)'<it>--</it>participants reviewed a PDF copy of the AGREE II only. All participants evaluated a test PG using the AGREE II. Outcomes of interest were learners' performance, satisfaction, self-efficacy, mental effort, time-on-task, and perceptions of AGREE II.</p> <p>Results</p> <p>No differences emerged between training conditions on any of the outcome measures.</p> <p>Conclusions</p> <p>We believe these results can be explained by better than anticipated performance of the AGREE II PDF materials (control condition) or the participants' level of health methodology and PG experience rather than the failure of the online training interventions. Some data suggest the online tools may be useful for trainees new to this field; however, this requires further study.</p

Quality specifications in postgraduate medical e-learning: an integrative literature review leading to a postgraduate medical e-learning model

BACKGROUND: E-learning is driving major shifts in medical education. Prioritizing learning theories and quality models improves the success of e-learning programs. Although many e-learning quality standards are available, few are focused on postgraduate medical education. METHODS: We conducted an integrative review of the current postgraduate medical e-learning literature to identify quality specifications. The literature was thematically organized into a working model. RESULTS: Unique quality specifications (n = 72) were consolidated and re-organized into a six-domain model that we called the Postgraduate Medical E-learning Model (Postgraduate ME Model). This model was partially based on the ISO-19796 standard, and drew on cognitive load multimedia principles. The domains of the model are preparation, software design and system specifications, communication, content, assessment, and maintenance. CONCLUSION: This review clarified the current state of postgraduate medical e-learning standards and specifications. It also synthesized these specifications into a single working model. To validate our findings, the next-steps include testing the Postgraduate ME Model in controlled e-learning settings