Alternate Reality Games as an Informal Learning Tool for Generating STEM Engagement among Underrepresented Youth: a Qualitative Evaluation of the Source

This project developed and studied The Source, an alternate reality game (ARG) designed to foster interest and knowledge related to science, technology, engineering, and math (STEM) among youth from populations underrepresented in STEM fields. ARGs are multiplayer games that engage participants across several media such as shared websites, social media, personal communications, and real-world settings to complete activities and collaborate with team members. The Source was a five-week summer program with 144 participants from Chicago aged 13 to 18 years. The Source incorporated six socio-contextual factors derived from three frameworks: Chang’s (ERIC Digest, 2002) recommendations for engaging underrepresented populations in STEM careers, Lave and Wenger’s (Cambridge University Press, 1991) situated learning model, and Barron’s (Human Development, 49(4); 193-224, 2006) learning ecology perspective. These factors aligned with the program’s aims of promoting (1) social community and peer support, (2) collaboration and teamwork, (3) real-world relevance and investigative learning, (4) mentoring and exposure to STEM professionals, (5) hands-on activities to foster transferable skill building, and (6) interface with technology. This paper presents results from 10 focus groups and 10 individual interviews conducted with a subset of the 144 youth participants who completed the game. It describes how these six factors were realized through The Source and uses them as a lens for considering how The Source functioned pedagogically. Qualitative findings describe youth’s perception of The Source’s potential influence on STEM interest, engagement, and identity formation. Despite limitations, study results indicate that underrepresented youth can engage in an immersive, narrative, and game-based experience as a potential mechanism for piquing and developing STEM interest and skills, particularly among underrepresented youth.     

Candidate working set strategy based SMO algorithm in support vector machine

Sequential minimal optimization (SMO) is quite an efficient algorithm for training the support vector machine. The most important step of this algorithm is the selection of the working set, which greatly affects the training speed. The feasible direction strategy for the working set selection can decrease the objective function, however, may augment to the total calculation for selecting the working set in each of the iteration. In this paper, a new candidate working set (CWS) Strategy is presented considering the cost on the working set selection and cache performance. This new strategy can select several greatest violating samples from Cache as the iterative working sets for the next several optimizing steps, which can improve the efficiency of the kernel cache usage and reduce the computational cost related to the working set selection. The results of the theory analysis and experiments demonstrate that the proposed method can reduce the training time, especially on the large-scale datasets.     

Collaborative Practitioner Research: A Case of Lesson Study in a Small Jewish School

ABSTRACT

Lesson study is a form of professional development where a group of teachers identifies a problem of practice on which they would like to make progress in their teaching. Over an extended period of time, the teachers study the topic and plan a lesson together. One member then teaches the lesson while the others observe; the group reflects afterwards on student learning. The cycle repeats, building teachers’ professional knowledge and their shared views of pedagogy over time. In this article, we argue that lesson study is a collaborative form of practitioner research and we show how this is so by sharing an example of a lesson study cycle conducted in a synagogue school.