Teaching Analytics (TA) is a new theoretical approach, which combines teaching expertise, visual analytics and design-based research to support teacher’s diagnostic pedagogical ability to use data and evidence to improve the quality of teaching. TA is now gaining prominence because it offers enormous opportunities to the teachers. It also identifies optimal ways in which teaching performance can be enhanced. Further, TA provides a platform for teachers to use data to reflect on teaching outcome. The outcome of TA can be used to engage teachers in a meaningful dialogue to improve the quality of teaching. Arguably, teachers need to develop their teacher data literacy and data inquiry skills to learn about teaching challenges. These skills are dependent on understanding the connection between TA, LA and Learning Design (LD). Additionally, they need to understand how choices in particular pedagogues and the LD can enhance their teaching experience. In other words, teachers need to equip themselves with the knowledge necessary to understand the complexity of teaching and the learning environment. Providing teachers access to analytics associated with their teaching practice and learning outcome can improve the quality of teaching practice. This research aims to explore current TA related discussions in the literature, to provide a generic conception of the meaning and value of TA. The review was intended to inform the establishment of a framework describing the various aspects of TA and to develop a model that can enable us to gain more insights into how TA can help teachers improve teaching practices and learning outcome. The Tripartite model was adopted to carry out a comprehensive, systematic and critical analysis of the literature of TA. To understand the current state-of-the-art relating to TA, and the implications to the future, we reviewed published articles from the year 2012 to 2019. The results of this review have led to the development of a conceptual framework for TA and established the boundaries between TA and LA. From the analysis the literature, we proposed a Teaching Outcome Model (TOM) as a theoretical lens to guide teachers and researchers to engage with data relating to teaching activities, to improve the quality of teaching.

中文翻译：

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教师数据素养的教学分析、价值和工具：一种系统的三方方法

教学分析 (TA) 是一种新的理论方法，它结合了教学专业知识、可视化分析和基于设计的研究，以支持教师使用数据和证据来提高教学质量的诊断教学能力。助教现在越来越受到重视，因为它为教师提供了巨大的机会。它还确定了可以提高教学绩效的最佳方式。此外，助教为教师提供了一个平台，让他们可以使用数据来反思教学成果。TA 的结果可用于让教师参与有意义的对话，以提高教学质量。可以说，教师需要培养他们的教师数据素养和数据查询技能，以了解教学挑战。这些技能取决于对 TA、LA 和学习设计 (LD) 之间联系的理解。此外，他们需要了解特定教师和 LD 的选择如何可以增强他们的教学经验。换言之，教师需要掌握必要的知识，以了解教学的复杂性和学习环境。为教师提供与其教学实践和学习成果相关的分析可以提高教学实践的质量。本研究旨在探索文献中当前与 TA 相关的讨论，为 TA 的含义和价值提供一个通用的概念。该审查旨在为建立描述 TA 各个方面的框架提供信息，并开发一个模型，使我们能够更深入地了解 TA 如何帮助教师改善教学实践和学习成果。采用三方模型对技术援助文献进行全面、系统、批判性的分析。为了了解当前与 TA 相关的最新技术以及对未来的影响，我们回顾了 2012 年至 2019 年发表的文章。这次审查的结果导致了 TA 的概念框架的发展和确定了 TA 和 LA 之间的界限。通过对文献的分析，我们提出了教学成果模型（TOM）作为理论视角，指导教师和研究人员参与与教学活动相关的数据，以提高教学质量。本次审查的结果导致了 TA 概念框架的发展，并确定了 TA 和 LA 之间的界限。通过对文献的分析，我们提出了教学成果模型（TOM）作为理论视角，指导教师和研究人员参与与教学活动相关的数据，以提高教学质量。本次审查的结果导致了 TA 概念框架的发展，并确定了 TA 和 LA 之间的界限。通过对文献的分析，我们提出了教学成果模型（TOM）作为理论视角，指导教师和研究人员参与与教学活动相关的数据，以提高教学质量。