Teaching analytics, value and tools for teacher data literacy: a systematic and tripartite approach

Abstract

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.

Training of modern specialists based on V.I. Vernadsky’s ideas of noosphere education

“…The key idea is to prepare thinking young people with creative ideas based on different types of activity and leadership. The key approach in the organization of the educational process is the environmental approach aimed at forming a creative educational environment [11].…”

Collaboration Analytics Need More Comprehensive Models and Methods: An Opinion Paper

“…For instance, implicit "data harvesting" can make students and teachers uncomfortable, since the educational field often handles sensitive personal data (Ferguson & Buckingham Shum, 2012, p. 9). Also, data ownership issues can arise when the same data sources are shared with stakeholders with different access rights (Ndukwe & Daniel, 2020). Furthermore, being "nudged" toward certain conduct can demotivate students and teachers (Knox, Williamson, & Bayne, 2020, p. 42).…”

Supporting the development of critical data literacies in higher education: building blocks for fair data cultures in society

“…As for the study of Ndukwe and Daniel ( 2020 ), a systematic review of the literature is used to develop a reflection on the way teachers can appropriate the power of teaching analytics (TA). The authors insist on the need to connect the dots of teaching analytics, learning analytics and learning design, as forms of representation (usually embedding visual representations) to improve the quality of teaching.…”

“…The other two works consider the actionability of analytics and the actors’ skills and knowledge of them to keep improving their practices. Specifically, Ndukwe and Daniel ( 2020 ) underline the impact of analytics as enablers of teachers’ reflection on the quality of their teaching, and Yang and Li ( 2020 ) explore the way HE stakeholders’ data literacy contributes to students’ educational success in a holistic way.…”

“…While there is no “one-to-one” relationship between the selected papers and these perspectives, the former sampled the need for data literacy among university staff to produce a common vision of quality in HE, taking into consideration the huge use of metrics in such an endeavour (Yang and Li 2020 ); they also addressed the complexities of privacy and data usage in the design of learning analytics (Cerro Martínez et al 2020 ) and the criticalities of extracting text as data to characterize and analyse polemic constructs such as gender issues in students’ evaluation of practice (Okoye et al 2020 ); and finally, they also explored a conceptual model for addressing educators’ data literacy to enable them to engage with teaching analytics through an informed and mindful approach (Ndukwe and Daniel 2020 ).…”