A Strategy for Incorporating Learning Analytics into the Design and Evaluation of a K-12 Science Curriculum

Abstract

Abstract:In this paper, we discuss a scalable approach for integrating learning analytics into an online K-12 science curriculum. A description of the curriculum and the underlying pedagogical framework is followed by a discussion of the challenges to be tackled as part of this integration. We include examples of data visualization based on teacher usage data along with a methodology for examining an inquiry-based science program. With more than one million students and fifty thousand teachers using the curriculum, a massive and rich dataset is continuously updated. This repository depicts teacher and student usage, and offers exciting opportunities to leverage data to improve both teaching and learning. In this paper, we use data from a medium-sized school district, comprising 53 schools, 1,026 teachers, and nearly one-third of a million curriculum visits during the 2012-2013 school year. This growing dataset also poses technical challenges such as data storage, complex aggregation, and analyses with broader implications for pedagogy, big data, and learning.

Using educational data from teaching and learning to inform teachers’ reflective educational design in inquiry-based STEM education

“…Another research strand that could benefit from this work refers to works that have been striving to use diverse educational data in order to help teachers track and improve their own performance when delivering their practice (i.e., moving beyond reflection on design, to reflection on delivery). A core aspect in these works is a detailed analysis of the educational design, in order to use educational data from the delivery for self-'monitoring' the performance of teachers and support their reflection (e.g., Monroy et al, 2014;van Leeuwen et al 2014;Rodríguez-Triana et al, 2015). Therefore, the ideas behind the proposed method in this paper are consistent with this strand of research and could introduce new perspectives of self-'monitoring', for example, on how to optimize the level and type of guidance that teachers provide to students on-thefly.…”

Direction of collaborative problem solving-based STEM learning by learning analytics approach

“…According to Dunbar, Dingel, and Prat-Resina (2014), it is important to incorporate the educational data and analysis relevant to student learning into course and curriculum design, and they also indicated a need for methods and tools that curriculum designers can use to explore data on instructional practices. In science education, the analytics and mining of educational data are useful in evaluating and improving educational design (Monroy, Rangel, & Whitaker, 2014). During the past decades, researches put efforts to explore the potential of analytics and data mining techniques and methodologies, to extract valuable and actionable information from large datasets.…”

Toward a New Approach to the Evaluation of a Digital Curriculum Using Learning Analytics

“…User data. Currently, the architecture supporting the online science curriculum allows only for the tracking of page visits, so all of the data we collected consisted of instances when a teacher accessed a particular page of the curriculum, regardless of what the teacher does or how long the teacher is on the page (Monroy et al, , 2014. This architecture, therefore, imposes important limits on the analyses: We cannot be sure exactly what kind of activity or what level of engagement the data are measuring, so the validity of the data can be questioned.…”