To address obstacles of adopting lesson study at scale, this study investigated how a technology-assisted lesson study (TALS) approach could remove the obstacle of scheduling while retaining positive effects of traditional lesson study (LS). The TALS approach involves embedding lesson study within teachers’ normal schedules, videotaping the research lessons using Swivl, and asychronously reviewing annotated videos of research lessons before debriefings facilitated by a mathematics specialist through Zoom. A TALS with two third-grade teachers was conducted. Analysis of the data, including lesson plans, research lesson videos, debriefing session videos and interviews with the teachers and the specialist, revealed that, as a traditional lesson study typically does, the research lesson was improved significantly. The participating teachers learned how to implement reform-oriented mathematics teaching through making critical alignments in sharpening learning goals, improving task design, and better orchestrating student work. Participating teachers and the specialist highlighted that the TALS provides teachers the opportunity to conduct LS without missing their own classes, examine student thinking in depth, and review and discus lessons critically. The unique contribution of the study is discussed.
The Game Play and Design Framework is a project-based instructional method to engage teachers and students with mathematics content by utilizing technology as a vehicle for game play and creation. In the authors’ prior work, they created a technology tool and game editing platform, the Wearable Learning Cloud Platform (WLCP), which enables teachers and students to play, create, and experience technology-augmented learning activities. This paper describes a 14-week Game Play and Design professional development program in which middle school teachers played, designed, tested, and implemented mathematics games in the classroom with their own students. Examples are included of teacher-created games, feedback from the students’ experience designing games, and evidence of student learning gains from playing teacher-created games. This work provides a pedagogical approach for educators and students that utilizes the benefits of mobile technologies and collaborative learning through games to develop students’ higher-level thinking in STEM classrooms.
National standards and frameworks for mathematics, computer science, and technology emphasize the importance of teaching all children computational thinking (CT) skills. These skills are important for preparing citizens that are literate in science, technology, engineering, and mathematics and for participation in a society that is rapidly changing with emerging technologies. This paper describes a 72-hour summer institute for grades 6-8 middle school mathematics teachers (n = 22) with a comprehensive approach to professional development, including training in computer programming with Bootstrap Algebra and Lego® Mindstorms® robotics, mathematics content sessions, and mathematics pedagogy sessions. Results of an assessment used to measure content knowledge and CT skills as well as the Technological Pedagogical Content Knowledge survey yielded statistically significant increases. Participant reflections revealed they valued opportunities for collaboration within grade-level professional learning communities and integration of CT strategies through both programming and robotics. Based upon participant feedback we recommend choosing either the use of Bootstrap Algebra or Lego Mindstorms within shorter timeframes to better prepare teachers for classroom implementation. These middle school teachers were receptive to mathematics-specific content sessions focused on developing conceptual understanding of mathematics they teach as well as grade-level appropriate manipulatives.
This study examined the characteristics of technology-enhanced statistical tasks written by 75 preservice mathematics teachers who used the ESTEEM project’s curriculum materials. In particular, it investigated the extent to which the tasks incorporated three key aspects related to best practices for teaching statistics: (a) analysis of large, multivariate, real datasets; (b) continual connection to context; and (c) engagement in the statistical investigation cycle. Regarding Key Aspect 1, the results showed that the tasks involved analysis of large (usually between 30 and 200 cases) datasets, with an average of 15 attributes provided per case. The vast majority of the datasets were also real, either from outside sources or collected by the students when the task would be implemented. Concerning Key Aspect 2, most tasks called for students to connect their work to the context from which the data was generated numerous times including during their orientation to the task, their reading of graphs made to display the data, and their interpretation of their analysis. Engagement in multiple phases of the statistical investigation cycle (Key Aspect 3) was asked for in the tasks as well. Hence, the ESTEEM project’s curriculum materials hold promise for supporting new teachers to plan meaningful technology-enhanced statistical tasks.