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

Demographic structure and firm productivity in Austria

We assess the relation between firms' productivity levels and the age composition of the employees using a unique Austrian dataset. We control for firm-specific characteristics (including number of employees, firm type and size) as well as employees' characteristics (including age, length and type of education, gender and marital status). We find a negative productivity effect of the share of older workers (50 years and older) for the whole sample, as well as for small sized firms (irrespective of the economic sector we consider). This is similar to the finding of most employer-employee studies. If we restrict our sample to large sized firms, the age-productivity profile changes. For firms in the mining and manufacturing industries the age structure of the workforce is not significantly related to productivity while for firms in the non-manufacturing sectors the share of younger workers is negatively related to productivity

VizieR Online Data Catalog: 3D shape of Orion A from Gaia DR2 (Grossschedl+, 2018)

VizieR On-line Data Catalog: J/A+A/619/A106. Originally published in: 2018A&A...619A.106GCatalog of the 682 YSOs, used to infer on the cloud's shape. We use Gaia DR2 parallaxes of these YSOs, which can be used as a good proxy for cloud distances in Orion A. (1 data file)