A framework for interactive learning in emerging technologies

Innovation is an interactive learning process which is of special interest for emerging technologies in which complex complementary knowledge from heterogeneous stakeholders is combined. In the emerging phase of technology development a lot of knowledge is tacit and can only be transferred face-to-face. At the same time a shared vision between stakeholders is being formed that acts as a driver for innovation. Although the importance of interactive learning is widely acknowledged, an adequate framework for studying interactive learning processes in emerging technologies is still missing. Therefore we formulated the leading research question: How to understand and conceptualize interactive learning in the context of emerging technologies? We did not only take the outcome of interactive learning into account, but also focused on opening the black box of the interactive learning process. We developed a framework based on characteristic elements of the interactive learning process in emerging technologies (i.e. prime mover, intermediaries, network formation and knowledge flows), influencing conditions (geographical, cognitive, regulatory, cultural and organisational proximity), and the outcome of the interactive learning process (single-loop and double-loop, tacit and codified knowledge). Clarifying examples are taken from the empirical field of the development of novel food products (functional foods).

A COMPARATIVE STUDY BETWEEN ICT LEARNING RESULTS USING INTERACTIVE COMPUTER-ASSISTED LEARNING AND THE ONES USING TEXTBOOKS FOR GRADE VII STUDENTS AT SMP N 4 WATES

This research aims to examine the comparison between interactive computer assisted-learning and textbook-based learning on the ICT learning results upon computer software of the grade VII students at SMP N 4 Wates Kulon Progo. The research methods employed was quasi-experimental. The research design selected was non-equivalent control group design. The research was conducted towards the grade VII students, they were class VIIA (by using interactive computer assisted-learning) as the experimental class and class VIIB (by using textbook-based learning) as the control class in which each class consisted of 32 students, at SMP N 4 Wates Kulon Progo in the semester 2. The sample collection technique of the research employed purposive sampling. The data collection of the research used documentations which comprised of the ICT subject syllabus and the result of the initial condition by using pre-test and the final condition by using post-test. The trials of the instruments utilized the test of item validity according to the expert judgement and the reliability test using Cronbach's Alpha. The data analysis technique to examine the research findings employed tests for normality and homogeneity as well as T-test using SPSS 17.0 program to analyze the data obtained. The research finding suggested that interactive computer-assisted learning was the better media to deliver the learning materials of the ICT subject than textbooks-based learning in term of students’ learning results. This was confirmed by the post-hypothesis test. Using T-test, the significance value of the posthypothesis test result was less than the value of α = 0,05, therefore Ho was rejected and Ha was accepted, meaning that the textbooks based-learning was different from interactive computer-assisted learning as the media to deliver the learning materials of ICT subject in term of students’ learning results. Based on the descriptive analysis upon the post-test data, it could be concluded that the interactive computer-assisted learning was the better media to deliver the learning materials of the ICT subject than textbooks-based learning in term of students’ learning results on computer software. Key Words: Comparison, learning media, interactive computer-assisted learning, textbooks, learning results

Virtual Reality Interactive Learning Environment

Open Building Manufacturing (ManuBuild) aims to promote the European construction industry beyond the state of the art. However, this requires the different stakeholders to be well informed of what ‘Open Building Manufacturing’ actually entails with respect to understanding the underlying concepts, benefits and risks. This is further challenged by the ‘traditional ways of learning’ which have been predominantly criticised for being entrenched in theories with little or no emphasis on practical issues. Experiential learning has long been suggested to overcome the problems associated with the traditional ways of learning. In this respect, it has the dual benefit of appealing to adult learner's experience base, as well as increasing the likelihood of performance change through training. On-the-job-training (OJT) is usually sought to enable ‘experiential’ learning; and it is argued to be particularly effective in complex tasks, where a great deal of independence is granted to the task performer. However, OJT has been criticised for being expensive, limited, and devoid of the actual training context. Consequently, in order to address the problems encountered with OJT, virtual reality (VR) solutions have been proposed to provide a risk free environment for learning without the ‘do-or-die’ consequences often faced on real construction projects. Since ManuBuild aims to promote the EU construction industry beyond the state of the art; training and education therefore needs also to go beyond the state of the art in order to meet future industry needs and expectations. Hence, a VR interactive learning environment was suggested for Open Building Manufacturing training to allow experiential learning to take place in a risk free environment, and consequently overcome the problems associated with OJT. This chapter discusses the development, testing, and validation of this prototype

Interactive multiple object learning with scanty human supervision

© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/We present a fast and online human-robot interaction approach that progressively learns multiple object classifiers using scanty human supervision. Given an input video stream recorded during the human robot interaction, the user just needs to annotate a small fraction of frames to compute object specific classifiers based on random ferns which share the same features. The resulting methodology is fast (in a few seconds, complex object appearances can be learned), versatile (it can be applied to unconstrained scenarios), scalable (real experiments show we can model up to 30 different object classes), and minimizes the amount of human intervention by leveraging the uncertainty measures associated to each classifier.; We thoroughly validate the approach on synthetic data and on real sequences acquired with a mobile platform in indoor and outdoor scenarios containing a multitude of different objects. We show that with little human assistance, we are able to build object classifiers robust to viewpoint changes, partial occlusions, varying lighting and cluttered backgrounds. (C) 2016 Elsevier Inc. All rights reserved.Peer ReviewedPostprint (author's final draft

Online Learning and Experimentation via Interactive Learning Resources

Recent trends in online learning like Massive Open Online Courses (MOOCs) and Open Educational Resources (OERs) are changing the landscape in the education sector by allowing learners to self-regulate their learning and providing them with an abundant amount of free learning materials. This paper presents FORGE, a new European initiative for online learning and experimentation via interactive learning resources. FORGE provides learners and educators with access to world- class facilities and high quality learning materials, thus enabling them to carry out experiments on e.g. new Internet protocols. In turn, this supports constructivist and self-regulated learning approaches, through the use of interactive learning resources, such as eBooks

Learning an Interactive Segmentation System

Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. This paper proposes a new evaluation and learning method which brings the user in the loop. It is based on the use of an active robot user - a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem.Comment: 11 pages, 7 figures, 4 table