The EDUCAtional Technology Exchange programme (EDUCATE) at UCL Institute of Education provides the context for this paper, which describes the programme’s vision, objectives and key activities, and sets the context for the collection of articles that follow. This university-led programme was underpinned by Luckin’s (2016) golden triangle of evidence-informed educational technology (edtech) as it sought to support 252 small and medium-sized enterprises to become more research-informed through a six-month research training and mentoring programme. The evaluation of the programme’s design-based research cycles revealed the importance of the careful choice and evolution of its boundary objects. These boundary objects, namely each enterprise’s ‘logic model’ and research proposal, facilitated meaningful conversations between the programme’s research mentors and the enterprises. These boundary objects involved several iterations, during which the language of the two communities became more aligned, helping to bridge the academic knowledge and practices with those of the enterprises.

This paper explores the relationship between unsupervised machine learning models, and the mental models of those who develop or use them. In particular, we consider unsupervised models, as well as the 'organisational colearning process' that creates them, as learning affordances. The co-learning process involves inputs originating both from the human participants' shared semantics, as well as from the data. By combining these, the process as well as the resulting computational models afford a newly shaped mental model, which is potentially more resistant to the biases of human mental models. We illustrate this organisational co-learning process with a case study involving unsupervised modelling via commonly used methods such as dimension reduction and clustering. Our case study describes how a trading and training company engaged in the co-learning process, and how its mental models of trading behavior were shaped (and afforded) by the resulting unsupervised machine learning model. The paper argues that this kind of co-learning process can play a significant role in human learning, by shaping and safeguarding participants' mental models, precisely because the models are unsupervised, and thus potentially lead to learning from unexpected or inexplicit patterns.

Abstract-Retrieved documents from queries are clustered to help users find information needed more significant in information retrieval. There are some frequent queries try finding information on an issue from the aspect of another issue. But current methods of clustering do not pay attention to the concept of the aspect included in these queries after retrieval process. In this paper we introduce aspect-oriented document clustering to group documents more significant and based on a special point of view. In our approach, text documents are represented based on a special aspect and the similarity between them is computed on the basis of its features. We use Wikipedia as background knowledge to emphasize and enrich the concept of the aspect. Then we evaluate the proposed approach with selected documents from two popular datasets, 20 Newsgroups and Reuters 21578. Results demonstrate that aspect-oriented clustering enhances clustering performance of those documents which can be equivalent to retrieved documents from aspect based queries significantly.