Student engagement with resources as observable signifiers of success in practice based learning

Practice-based learning activities with a focus on Science, Technology, Art, Math and Engineering (STEAM) are providing new opportunities for teaching these subjects. However, we lack widely accepted ways of assessing and monitoring these practices to inform educators and learners and enable the provision of effective support. Here, we report the results from a study with 15 teenage students taking part in a 2-day Hack. We present results from analysis of video data recording collaborative working between groups of students. The analysis of the video data is completed using the ERICAP analytical framework (Luckin et al., 2017) based on ecology of resources and interactive, constructive, active and passive engagement concepts. The results illustrate the differences between students' engagement with resources which might be utilized as signifiers of student success in similar learning environments.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

The role of learning theory in multimodal learning analytics

This study presents the outcomes of a semi-systematic literature review on the role of learning theory in multimodal learning analytics (MMLA) research. Based on previous systematic literature reviews in MMLA and an additional new search, 35MMLA works were identified that use theory. The results show that MMLA studies do not always discuss their findings within an established theoretical framework. Most of the theory-driven MMLA studies are positioned in the cognitive and affective domains, and the three most frequently used theories are embodied cognition, cognitive load theory and control–value theory of achievement emotions. Often, the theories are only used to inform the study design, but there is a relationship between the most frequently used theories and the data modalities used to operationalize those theories. Although studies such as these are rare, the findings indicate that MMLA affordances can, indeed, lead to theoretical contributions to learning sciences. In this work, we discuss methods of accelerating theory-driven MMLA research and how this acceleration can extend or even create new theoretical knowledge

An Investigation of an Independent Learning Approach in University Level Chemistry: The Effects on Students' Knowledge, Understanding and Intellectual Attributes

The aim of this study is to provide a preliminary insight into a teaching strategy which deploys independent learning in degree-level chemistry. For this purpose, the impact of the teaching approach applied in a Macromolecules course on students’ knowledge of, understanding about chemical ideas, and intellectual attributes was investigated. To achieve this, diagnostic questions both before and after the teaching, descriptive questionnaires and standardised interviews were used. The sample consisted of first-year undergraduate students who took the Macromolecules course in the Department of Chemistry in one of the top ten universities in the UK. In total, one hundred sixty-seven students took part in the study and interviews were carried out with twenty-four students. The results revealed that the independent learning strategy applied in the Macromolecules course can be effective in improving students’ knowledge of and understanding about chemical ideas, as well as contributing to some of their intellectual attributes. It was also found that when students were left on their own to do independent investigations, without any support, their knowledge of and understanding about chemical ideas from the course content did not change statistically significantly. In addition, whilst there was no statistically significant change in student responses with a sign of misunderstanding for nine out of ten diagnostic questions, in one case there was statistically significant increase in the number of student responses with a sign of misunderstanding. The results of this research study also presented a detailed and personal picture of students’ views of the independent learning approach. Student arguments for their appreciation and disapproval of the strategy were revealed and discussed. The findings of this research study can offer a supplementary resource for teachers at tertiary level to use in situated ways when dealing with similar course contents and similar learning objectives which they encounter during their practice

Solved! Making the case for collaborative problem-solving

This report argues that the ability to solve problems with others is a crucial skill for our young people in the workplace of the future but the current education system does little to support it. Key findings Collaborative problem-solving (CPS) is an increasingly important skill to teach young people in order to prepare them for the future. Despite strong evidence for its impact, CPS is rarely taught in schools but if structured well it can reinforce knowledge and improve attainment. Significant barriers exist for teachers implementing this practice, from behaviour management to curriculum coverage, to task-design. For CPS to gain ground, a concerted shift is needed including teacher training, better resources and system level support. This report is part of Nesta’s ongoing commitment to equipping young people with the skills they need to succeed. It makes a series of recommendations on how organisations and policymakers can help support and embrace the implementation of CPS. Nesta is following this up with a series of small-scale pilots of aligned programmes in order to evaluate impact and explore how CPS can be implemented in a range of practical settings. Policy recommendations Stimulate production of quality collaborative problem-solving (CPS) resources and training, from primary education onwards. Fund existing, aligned programmes to scale and evaluate impact. Educate and involve the out-of-school learning sector and volunteer educators. Develop smarter collaborative problem-solving assessment methods. Help higher education organisations and MOOCs to track what works

Rapid evidence review of good practical science

There is a clear need for more high-quality studies of practical work that have a tightly-defined focus and a rigorous methodological approach. We are confident that this finding would persist in a more extended review than a Rapid Evidence Assessment (REA), which is necessarily limited in scope. We would not recommend conducting a more in-depth, more traditional systematic review at this stage. There is a wealth of commentary on the purpose and usefulness of practical science, but very few robust studies. A more extensive search encompassing the grey literature would undoubtedly identify more studies, but they are unlikely to add significantly to the current knowledge base. This REA has highlighted the need for more evaluations of practical science in its various guises. There is a requirement for research that is clear in its aims, focus and definitions; has a sound methodology with adequate sample sizes and appropriate outcome measures; and is designed to shed light on the usefulness of practical science work across different contexts and for different purposes. Drawing from the literature, the report identifies five main purposes of practical science.These are to enhance student performance in conceptual understanding; practical skills; non-subject specific intellectual and personal attributes; attitudes towards science; and understanding of how science and scientists work. There is currently a much greater evidence base around practical work improving physical skills and dexterity compared with the other four purposes of practical work defined in this report

Adoption of Artificial Intelligence in Schools: Unveiling Factors Influencing Teachers Engagement

Albeit existing evidence about the impact of AI-based adaptive learning platforms, their scaled adoption in schools is slow at best. In addition, AI tools adopted in schools may not always be the considered and studied products of the research community. Therefore, there have been increasing concerns about identifying factors influencing adoption, and studying the extent to which these factors can be used to predict teachers engagement with adaptive learning platforms. To address this, we developed a reliable instrument to measure more holistic factors influencing teachers adoption of adaptive learning platforms in schools. In addition, we present the results of its implementation with school teachers (n=792) sampled from a large country-level population and use this data to predict teachers real-world engagement with the adaptive learning platform in schools. Our results show that although teachers knowledge, confidence and product quality are all important factors, they are not necessarily the only, may not even be the most important factors influencing the teachers engagement with AI platforms in schools. Not generating any additional workload, in-creasing teacher ownership and trust, generating support mechanisms for help, and assuring that ethical issues are minimised, are also essential for the adoption of AI in schools and may predict teachers engagement with the platform better. We conclude the paper with a discussion on the value of factors identified to increase the real-world adoption and effectiveness of adaptive learning platforms by increasing the dimensions of variability in prediction models and decreasing the implementation variability in practice.Comment: 12 pages,2 tables, 1 figure, International conference of artificial intelligence in educatio

An Instrument for Measuring Teachers’ Trust in AI-Based Educational Technology

Evidence from various domains underlines the key role that human factors, and especially, trust, play in the adoption of technology by practitioners. In the case of Artificial Intelligence (AI) driven learning analytics tools, the issue is even more complex due to practitioners’ AI-specific misconceptions, myths, and fears (i.e., mass unemployment and ethical concerns). In recent years, artificial intelligence has been introduced increasingly into K-12 education. However, little research has been conducted on the trust and attitudes of K-12 teachers regarding the use and adoption of AI-based Educational Technology (EdTech). The present study introduces a new instrument to measure teachers’ trust in AI-based EdTech, provides evidence of its internal structure validity, and uses it to portray secondary-level school teachers’ attitudes toward AI. First, we explain the instrument items creation process based on our preliminary research and review of existing tools in other domains. Second, using Exploratory Factor Analysis we analyze the results from 132 teachers’ input. The results reveal eight factors influencing teachers’ trust in adopting AI-based EdTech: Perceived Benefits of AI-based EdTech, AI-based EdTech’s Lack of Human Characteristics, AI-based EdTech’s Perceived Lack of Transparency, Anxieties Related to Using AI-based EdTech, Self-efficacy in Using AI-based EdTech, Required Shift in Pedagogy to Adopt AI-based EdTech, Preferred Means to Increase Trust in AI-based EdTech, and AI-based EdTech vs Human Advice/Recommendation. Finally, we use the instrument to discuss 132 high-school Biology teachers’ responses to the survey items and to what extent they align with the findings from the literature in relevant domains. The contribution of this research is twofold. First, it introduces a reliable instrument to investigate the role of teachers’ trust in AI-based EdTech and the factors influencing it. Second, the findings from the teachers’ survey can guide creators of teacher professional development courses and policymakers on improving teachers’ trust in, and in turn their willingness to adopt, AI-based EdTech in K-12 education

Is it time we get real? A systematic review of the potential of data-driven technologies to address teachers' implicit biases

Data-driven technologies for education, such as artificial intelligence in education (AIEd) systems, learning analytics dashboards, open learner models, and other applications, are often created with an aspiration to help teachers make better, evidence-informed decisions in the classroom. Addressing gender, racial, and other biases inherent to data and algorithms in such applications is seen as a way to increase the responsibility of these systems and has been the focus of much of the research in the field, including systematic reviews. However, implicit biases can also be held by teachers. To the best of our knowledge, this systematic literature review is the first of its kind to investigate what kinds of teacher biases have been impacted by data-driven technologies, how or if these technologies were designed to challenge these biases, and which strategies were most effective at promoting equitable teaching behaviors and decision making. Following PRISMA guidelines, a search of five databases returned n = 359 records of which only n = 2 studies by a single research team were identified as relevant. The findings show that there is minimal evidence that data-driven technologies have been evaluated in their capacity for supporting teachers to make less biased decisions or promote equitable teaching behaviors, even though this capacity is often used as one of the core arguments for the use of data-driven technologies in education. By examining these two studies in conjunction with related studies that did not meet the eligibility criteria during the full-text review, we reveal the approaches that could play an effective role in mitigating teachers' biases, as well as ones that may perpetuate biases. We conclude by summarizing directions for future research that should seek to directly confront teachers' biases through explicit design strategies within teacher tools, to ensure that the impact of biases of both technology (including data, algorithms, models etc.) and teachers are minimized. We propose an extended framework to support future research and design in this area, through motivational, cognitive, and technological debiasing strategies

A Transparency Index Framework for AI in Education

Numerous AI ethics checklists and frameworks have been proposed focusing on different dimensions of ethical AI such as fairness, explainability, and safety. Yet, no such work has been done on developing transparent AI systems for real-world educational scenarios. This paper presents a Transparency Index framework that has been iteratively co-designed with different stakeholders of AI in education, including educators, ed-tech experts, and AI practitioners. We map the requirements of transparency for different categories of stakeholders of AI in education and demonstrate that transparency considerations are embedded in the entire AI development process from the data collection stage until the AI system is deployed in the real world and iteratively improved. We also demonstrate how transparency enables the implementation of other ethical AI dimensions in Education like interpretability, accountability, and safety. In conclusion, we discuss the directions for future research in this newly emerging field. The main contribution of this study is that it highlights the importance of transparency in developing AI-powered educational technologies and proposes an index framework for its conceptualization for AI in education

Investigating Collaboration as a Process with Theory-driven Learning Analytics

Although collaboration is considered a key 21st-century skill, oftentimes it is only assessed through the outcome measures of individual learners. In this paper, we draw upon collaborative cognitive load theory (CCLT) to explain the process of collaboration in learning from the point of view of the collective, rather than an individual learner. Using CCLT we suggest a new method of measuring the process of collaboration regardless of its outcome measures. Our approach — Collaborative Learning as a Process (CLaP) — uses social network analysis to evaluate the balance between interactivity gains and coordination costs of learner communities. Here, we demonstrate the approach using real-world data derived from the digital tracks of two online discussion communities. We argue that our conceptual approach can enable instructors and learners to unlock the black box of collaboration in learning. It has the potential to support the development of learner skills that go beyond cognition. We conclude the paper with the results of our investigation of the value of the approach to the online module instructor