6 research outputs found
Content analysis of open innovation communities using latent semantic indexing
Open innovation (OI) represents an emergent paradigm by which customers and users are
involved as part of the innovation processes of organisations. One of its most popular implementation
schemes is OI communities, which have been popularised by the use of social
software. Through these communities, users are free to post, share, comment and evaluate
other users’ ideas, and they can interact with other users as well as with the innovation
department and experts of the organisation. One of the challenges of OI communities is distinguishing
the most innovative ideas, as they receive hundreds or even thousands of ideas.
This paper proposes a novel approach for this task consisting of analysing the content of shared
ideas. Through this analysis, several conclusions about the decision processes of the organisation
can be inferred. The obtained results can help OI managers to improve ideas evaluation
processesMinisterio de EconomĂa y Competitividad ECO2013-43856-RJunta de Andalucia. ConsejerĂa de EconomĂa, InnovaciĂłn, Ciencia y Empleo P12-SEJ-32
Mathematical conjecturing and proving
Most university courses in mathematics programs are characterized by a strong focus on the axiomatic nature of mathematics, and thus also on proof as the central scientific method of mathematics (Selden, A. & Selden, 2008). Lecturers write proofs on the blackboard, students attempt to demonstrate their understanding and skills by proving theorems on their own or in collaboration with others. However, there is often little systematic discussion in these courses on how new mathematical conjectures can be generated and on how proofs are constructed (Alcock, 2010). Students’ experiences with conjecturing and proving in schools or in university mathematics courses often lead them to “consider proof as a static product rather than a negotiated process that can help students justify and make sense of mathematical ideas” (Otten, Bleiler-Baxter, & Engledowl, 2017, p. 112). Yet, several authors (e.g., Epp, 2003; Savic, 2015a; Selden, A. & Selden, 2008) have hypothesized that often only little time can be devoted to illustrate students which strategies and processes may help to step through the proof construction process and to recover from proving impasses. Furthermore, the knowledge about what characterizes proof processes that lead to a successful outcome (i.e., an acceptable mathematical proof [according to local acceptance criteria]) is rare.
To approach this issue, an extensive systematic literature search was conducted to summarize common claims and empirical findings about promising conjecturing and proving processes. 126 articles that focussed on conjecturing and proving were clustered using a topic modeling method. The algorithm identified 17 different topics. The most representative papers for each topic, in total 45 papers, were qualitatively analysed with regard to their research perspectives on which they were based and their claims and findings about the processes that are needed to successfully generate conjectures and construct proofs. This combination of statistical clustering and qualitative analyses allowed a systematic categorization of claims and empirical findings about successful conjecturing and proving processes in the literature. Based on this review, a set of characteristics of conjecturing and proving processes, that are assumed or reported to be crucial for success, is proposed.
For the further analysis of such process characteristics, we started from a model differentiating students’ prerequisites they bring to bear on the proving situation, the conjecturing and proving processes they engage in, and the quality of the resulting product. The main question of the empirical work in this dissertation was, which process characteristics influence the quality of the final product (the formulated conjecture and constructed proof), and in which way they mediate the impact of students’ prerequisites on this product. Specifically, we distinguished between individual-mathematical and social-discursive process characteristics of conjecturing and proving. These process characteristics were extracted from prior research in mathematics education or in educational psychology or in the Learning Sciences.
The central aim of this dissertation was to develop an instrument for assessing (prospective undergraduate) mathematics students’ conjecturing and proving processes in collaborative situations. A high-inference rating scheme with seven scales, based on theoretical considerations and on rating guidelines adapted from educational research was designed. The rating scheme was evaluated in a study with N=98 prospective undergraduate students working in dyads on an open-ended conjecturing and proving task. The results of the empirical study with regard to the basic analyses showed that collaborative conjecturing and proving processes could be rated with sufficient reliability and that the structure of the data corresponded to the underlying theoretical assumption that two dimensions, one related to individual-mathematical and one related to social-discursive process characteristics can be distinguished. The in-depth analyses pointed out that individual-mathematical process characteristics were predictive for the quality of the resulting product and mediated the relation between prerequisites (students’ prior knowledge on proof) and the quality of the product.
In this way, the dissertation contributes to the scientific debate on how to assess (mathematical argumentation) skills (e.g., Blömeke, Gustafsson, & Shavelson, 2015; Koeppen, Hartig, Klieme, & Leutner, 2008) and provides theoretical and empirical insights on individual-mathematical and social-discursive process characteristics that describe the quality of collaborative conjecturing and proving processes