The effects of a learning analytics dashboard on teachers’ diagnosis and intervention in computer-supported collaborative learning

Bao, Haogang; Li, Yanyan; Su, You; Xing, Shuang; Chen, Nian-Shing; Rosé, Carolyn Penstein

doi:10.1080/1475939x.2021.1902383

Cited by 11 publications

(13 citation statements)

References 42 publications

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“…Behavior when using AI and LA [31,35,39,40,42,44,52] 7 Digital competence [34,38,45,50,53,58] 6 Perception of AI and LA [32,41,48,56,60] 5 Self-regulation and reflection [33,35,47,54,55] 5 Engagement [36,51,57,59] 4 Analysis of educational data [37,43,49] 3…”

Section: Goals and Objectives Reference Number Number Of Studiesmentioning

confidence: 99%

Artificial Intelligence and Learning Analytics in Teacher Education: A Systematic Review

Salas‐Pilco

Xiao

2022

Education Sciences

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In recent years, artificial intelligence (AI) and learning analytics (LA) have been introduced into the field of education, where their use has great potential to enhance the teaching and learning processes. Researchers have focused on applying these technologies to teacher education, as they see the value of technology for educating. Therefore, a systematic review of the literature on AI and LA in teacher education is necessary to understand their impact in the field. Our methodology follows the PRISMA guidelines, and 30 studies related to teacher education were identified. This review analyzes and discusses the several ways in which AI and LA are being integrated in teacher education based on the studies’ goals, participants, data sources, and the tools used to enhance teaching and learning activities. The findings indicate that (a) there is a focus on studying the behaviors, perceptions, and digital competence of pre- and in-service teachers regarding the use of AI and LA in their teaching practices; (b) the main data sources are behavioral data, discourse data, and statistical data; (c) machine learning algorithms are employed in most of the studies; and (d) the ethical clearance is mentioned by few studies. The implications will be valuable for teachers and educational authorities, informing their decisions regarding the effective use of AI and LA technologies to support teacher education.

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Section: Goals and Objectives Reference Number Number Of Studiesmentioning

confidence: 99%

Artificial Intelligence and Learning Analytics in Teacher Education: A Systematic Review

Salas‐Pilco

Xiao

2022

Education Sciences

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“…Based on educational data mining and learning analysis, Zhang Jia-Hua investigated students' login behavior, resource utilization, test, interaction behavior and learning performance, mined users' interest points through data analysis, and designed personalized teaching services [10]. Bao Haogang assessed the effectiveness of a knowledge-based and behavior-based learning analysis dashboard system in supporting teachers to diagnose and intervene in learning difficulties, and improved learning effects by self-evaluating learners in different situations and adjusting their learning strategies according to the evaluation results [11]. De Hoyos Rafael used learning analysis to test the teaching method, so that students can learn professional knowledge in real experimental situations, obtain rich and valuable knowledge through simulation and personal experience, and help them use the knowledge to solve practical problems [12].…”

Section: Progress and Trend Of Classroom Learning Evaluation In Collegesmentioning

confidence: 99%

Evaluation on the Construction of the Diagnosis and Improvement Model of College Classroom Teaching Based on Learning Evaluation Technology

2024

IJNDE

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School is one of the main positions to implement quality education. School teaching is the most important and basic part of the whole education cause, which is related to the development of students in their whole life. However, many colleges have many problems in carrying out teaching activities. College students generally lack the ability of independent thinking and judgment, especially in the aspect of logical thinking. As a result, they tend to fall into a fixed thinking pattern and their own quality cannot be fully developed. Therefore, people must pay attention to and strive to address the issues existing in school education. Only in this way can people ensure that the trained people can comprehensively improve the quality of talent training. Learning analysis is a new teaching mode. This way not only enables learners to grasp knowledge more intuitively, but also applies theoretical knowledge to practical activities, providing students with richer and more vivid teaching content and more flexible and convenient teaching system. This paper discussed some problems in college teaching at the present stage, expounded the construction of classroom teaching mode based on learning analysis technology in detail from the theoretical point of view, put forward new classroom teaching diagnosis methods and improvement strategies, and laid a solid foundation for improving college students' learning achievements and cultivating innovative talents. By comparing the classroom teaching system before and after the improvement based on learning analysis technology, it was proved that the optimized teaching method can greatly mobilize the enthusiasm of students. At the same time, it can improve the problems existing in the conventional teaching mode, enhance the learning effect, and improve the achievement of teaching objectives by about 12.84%. The teaching quality has been significantly improved. This research can not only help colleges and universities to carry out relevant curriculum education, but also promote the progress of modern society and provide important support for training high-quality talents.

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“…In response to this, other forms of representation, such as storytelling, are under exploration [15], while co-designing dashboards with teachers at the centre is proposed as the way forward [19]. An increasing number of studies point to positive effects from teachers' engagement with analytics; this refers to enhanced understanding of learning processes, provision of individualised and timely interventions, better course design [18], better student performance when accessing analytics than without [4], especially for disadvantaged students including those from low socio-economic backgrounds [6], and effective support to collaborative student work [10]. Yet, some teachers are found to be sceptical about the use of analytics in teaching, due to their data-driven nature [19] and due to concerns about the ethical implications of using student data without students being involved in a dialogue about it [20].…”

Section: Pla Dashboards and Teachersmentioning

confidence: 99%

Predictive Learning Analytics and University Teachers: Usage and perceptions three years post implementation

Herodotou

Maguire

Hlosta

et al. 2023

LAK23: 13th International Learning Analytics and Knowledge Conference

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Predictive learning analytics (PLA) dashboards have been used by teachers to identify students at risk of failing their studies and provide proactive support. Yet, very few of them have been deployed at a large scale or had their use studied at a mature level of implementation. In this study, we surveyed 366 distance learning university teachers across four faculties three years after PLA has been made available across university as business as usual. Informed by the Unified Theory of Acceptance and Use of Technology (UTAUT), we present a context-specific version of UTAUT that reflects teachers' perceptions of PLA in distance learning higher education. The adoption and use of PLA was shown to be positively influenced by less experience in teaching, performance expectancy, self-efficacy, positive attitudes, and low anxiety, while negatively influenced by a lack of facilitating conditions and low effort expectancy, indicating that the type of technology and context within which it is used are significant factors determining our understanding of technology usage and adoption. This study provides significant insights as to how to design, apply and implement PLA with teachers in higher education.

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The effects of a learning analytics dashboard on teachers’ diagnosis and intervention in computer-supported collaborative learning

Cited by 11 publications

References 42 publications

Artificial Intelligence and Learning Analytics in Teacher Education: A Systematic Review

Artificial Intelligence and Learning Analytics in Teacher Education: A Systematic Review

Evaluation on the Construction of the Diagnosis and Improvement Model of College Classroom Teaching Based on Learning Evaluation Technology

Predictive Learning Analytics and University Teachers: Usage and perceptions three years post implementation

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