Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

Advances in photonic reservoir computing on an integrated platform

Reservoir computing is a recent approach from the fields of machine learning and artificial neural networks to solve a broad class of complex classification and recognition problems such as speech and image recognition. As is typical for methods from these fields, it involves systems that were trained based on examples, instead of using an algorithmic approach. It originated as a new training technique for recurrent neural networks where the network is split in a reservoir that does the `computation' and a simple readout function. This technique has been among the state-of-the-art. So far implementations have been mainly software based, but a hardware implementation offers the promise of being low-power and fast. We previously demonstrated with simulations that a network of coupled semiconductor optical amplifiers could also be used for this purpose on a simple classification task. This paper discusses two new developments. First of all, we identified the delay in between the nodes as the most important design parameter using an amplifier reservoir on an isolated digit recognition task and show that when optimized and combined with coherence it even yields better results than classical hyperbolic tangent reservoirs. Second we will discuss the recent advances in photonic reservoir computing with the use of resonator structures such as photonic crystal cavities and ring resonators. Using a network of resonators, feedback of the output to the network, and an appropriate learning rule, periodic signals can be generated in the optical domain. With the right parameters, these resonant structures can also exhibit spiking behaviour