LAK22: 12th International Learning Analytics and Knowledge Conference最新文献

{"title":"Modelling Co-located Team Communication from Voice Detection and Positioning Data in Healthcare Simulation","authors":"Linxuan Zhao, Lixiang Yan, D. Gašević, S. Dix, Hollie Jaggard, Rosie Wotherspoon, Riordan Alfredo, Xinyu Li, Roberto Martínez Maldonado","doi":"10.1145/3506860.3506935","DOIUrl":"https://doi.org/10.1145/3506860.3506935","url":null,"abstract":"In co-located situations, team members use a combination of verbal and visual signals to communicate effectively, among which positional forms play a key role. The spatial patterns adopted by team members in terms of where in the physical space they are standing, and who their body is oriented to, can be key in analysing and increasing the quality of interaction during such face-to-face situations. In this paper, we model the students’ communication based on spatial (positioning) and audio (voice detection) data captured from 92 students working in teams of four in the context of healthcare simulation. We extract non-verbal events (i.e., total speaking time, overlapped speech,and speech responses to team members and teachers) and investigate to what extent they can serve as meaningful indicators of students’ performance according to teachers’ learning intentions. The contribution of this paper to multimodal learning analytics includes: i) a generic method to semi-automatically model communication in a setting where students can freely move in the learning space; and ii) results from a mixed-methods analysis of non-verbal indicators of team communication with respect to teachers’ learning design.","PeriodicalId":185465,"journal":{"name":"LAK22: 12th International Learning Analytics and Knowledge Conference","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122822207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Penny for your Thoughts: Students and Instructors’ Expectations about Learning Analytics in Brazil","authors":"Taciana Pontual Falcão, R. Rodrigues, C. Cechinel, Diego Dermeval, E. H. T. D. Oliveira, Isabela Gasparini, R. Araújo, T. Primo, D. Gašević, R. F. Mello","doi":"10.1145/3506860.3506886","DOIUrl":"https://doi.org/10.1145/3506860.3506886","url":null,"abstract":"Stakeholder engagement is a key aspect for the successful implementation of Learning Analytics (LA) in Higher Education Institutions (HEIs). Studies in Europe and Latin America (LATAM) indicate that, overall, instructors and students have positive views on LA adoption, but there are differences between their ideal expectations and what they consider realistic in the context of their institutions. So far, very little has been found about stakeholders’ views on LA in Brazilian higher education. By replicating the survey conducted in other countries, in seven Brazilian HEIs, we found convergences both with Europe and LATAM, reinforcing the need for local diagnosis and indicating the risk of assuming a ”LATAM identity”. Our findings contribute to building a corpus of knowledge on stakeholders expectations with a contextualised comprehension of the gaps between ideal and predicted scenarios, which can inform institutional policies for LA implementation in Brazil.","PeriodicalId":185465,"journal":{"name":"LAK22: 12th International Learning Analytics and Knowledge Conference","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132463548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Does Analysis of Handwritten Notes Provide Better Insights for Learning Behavior?","authors":"Boyi Li, T. Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada","doi":"10.1145/3506860.3506915","DOIUrl":"https://doi.org/10.1145/3506860.3506915","url":null,"abstract":"Handwritten notes are one important component of students’ learning process, which is used to record what they have learned in class or tease out knowledge after class for reflection and further strengthen the learning effect. It also helps a lot during review. We hope to divide handwritten notes (Japanese) into different parts, such as text, mathematical expressions, charts, etc., and quantify them to evaluate the condition of the notes and compare them among students. At the same time, data on students’ learning behaviors in the course are collected through the online education platform, such as the use time of textbook and attendance, as well as the scores of the online quiz and course grade. In this paper, the analysis of the relationship between the segmentation results of handwritten notes and learning behavior are reported, as well as the research on automatic page segmentation based on deep learning.","PeriodicalId":185465,"journal":{"name":"LAK22: 12th International Learning Analytics and Knowledge Conference","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131512080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semester-level Spacing but Not Procrastination Affected Student Exam Performance","authors":"Iman YeckehZaare, Victoria Mulligan, Grace Ramstad, P. Resnick","doi":"10.1145/3506860.3506907","DOIUrl":"https://doi.org/10.1145/3506860.3506907","url":null,"abstract":"Spacing and procrastination are often thought of as opposites. It is possible, however, for a student to space their studying by doing something every day throughout the semester and still procrastinate by waiting until late in the semester to increase their amount of studying. To analyze the relationship between spacing and procrastination, we examined 674 students’ interactions with a course eBook over four semesters of an introductory programming course. We measured each student’s semester-level spacing as the number of days they interacted with the eBook, and each student’s semester-level procrastination as the average delay from the start of the semester for all their eBook interactions. Surprisingly, there was a small, yet positive, correlation between the two measures. Which, then, matters for course performance: studying over more days or studying earlier in the semester? When controlling for total amount of studying, as well as a number of academic and demographic characteristics in an SEM analysis, we find a strong positive effect of spacing but no significant effect of procrastination on final exam scores.","PeriodicalId":185465,"journal":{"name":"LAK22: 12th International Learning Analytics and Knowledge Conference","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132520656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NASC: Network analytics to uncover socio-cognitive discourse of student roles","authors":"Maverick Andre Dionisio Ferreira, R. F. Mello, Vitomir Kovanovíc, André C. A. Nascimento, R. Lins, D. Gašević","doi":"10.1145/3506860.3506978","DOIUrl":"https://doi.org/10.1145/3506860.3506978","url":null,"abstract":"Roles that learners assume during online discussions are an important aspect of educational experience. The roles can be assigned to learners and/or can spontaneously emerge through student-student interaction. While existing research proposed several approaches for analytics of emerging roles, there is limited research in analytic methods that can i) automatically detect emerging roles that can be interpreted in terms of higher-order constructs of collaboration; ii) analyse the extent to which students complied to scripted roles and how emerging roles compare to scripted ones; and iii) track progression of roles in social knowledge progression over time. To address these gaps in the literature, this paper propose a network-analytic approach that combines techniques of cluster analysis and epistemic network analysis. The method was validated in an empirical study discovered emerging roles that were found meaningful in terms of social and cognitive dimensions of the well-known model of communities of inquiry. The study also revealed similarities and differences between emerging and script roles played by learners and identified different progression trajectories in social knowledge construction between emerging and scripted roles. The proposed analytic approach and the study results have implications that can inform teaching practice and development techniques for collaboration analytics.","PeriodicalId":185465,"journal":{"name":"LAK22: 12th International Learning Analytics and Knowledge Conference","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121354765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Teammates Stabilize over Time in How They Evaluate Their Team Experiences","authors":"Rebecca L. Matz, A. Lee, Robin R. Fowler, Caitlin Hayward","doi":"10.1145/3506860.3506891","DOIUrl":"https://doi.org/10.1145/3506860.3506891","url":null,"abstract":"It is difficult for instructors, and even students themselves, to become aware in real-time of inequitable behaviors occurring on student teams. Here, we explored a potential measure for inequitable teamwork drawing on data from a digital pedagogical tool designed to surface and disrupt such team behaviors. Students in a large, undergraduate business course completed seven surveys about team health (called team checks) at regular intervals throughout the term, providing information about team dynamics, contributions, and processes. The ways in which changes in students’ scores from team check to team check compared to the median changes for their team were used to identify the proportions of teams with outlier student scores. The results show that for every team size and team check item, the proportion of teams with outliers at the end of the term was smaller than at the beginning of the semester, indicating stabilization in how teammates evaluated their team experiences. In all but two cases, outlying students were not disproportionately likely to identify with historically marginalized groups based on gender or race/ethnicity. Thus, we did not broadly identify teamwork inequities in this specific context, but the method provides a basis for future studies about inequitable team behavior.","PeriodicalId":185465,"journal":{"name":"LAK22: 12th International Learning Analytics and Knowledge Conference","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117003400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mining Code Submissions to Elucidate Disengagement in a Computer Science MOOC","authors":"Efrat Vinker, Amir Rubinstein","doi":"10.1145/3506860.3506877","DOIUrl":"https://doi.org/10.1145/3506860.3506877","url":null,"abstract":"Despite the growing prevalence of Massive Open Online Courses (MOOCs) in the last decade, using them effectively is still challenging. Particularly, when MOOCs involve teaching programming, learners often struggle with writing code without sufficient support, which may increase frustration, attrition, and eventually dropout. In this study, we assess the pedagogical design of a fresh introductory computer science MOOC. Keeping in mind MOOC “end-user” instructors, our analyses are based merely on features easily accessible from code submissions, and methods that are relatively simple to apply and interpret. Using visual data mining we discover common patterns of behavior, provide insights on content that may require reevaluation and detect critical points of attrition in the course timeline. Additionally, we extract students’ code submission profiles that reflect various aspects of engagement and performance. Consequently, we predict disengagement towards programming using classic machine learning methods. To the best of our knowledge, our definition for attrition in terms of disengagement towards programming is novel as it suits the unique active hands-on nature of programming. To our perception, the results emphasize that more attention and further research should be aimed at the pedagogical design of hands-on experience, such as programming, in online learning systems.","PeriodicalId":185465,"journal":{"name":"LAK22: 12th International Learning Analytics and Knowledge Conference","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114976382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid Human-AI Curriculum Development for Personalised Informal Learning Environments","authors":"Mohammadreza Tavakoli, Abdolali Faraji, M. Molavi, Stefan T. Mol, G'abor Kismih'ok","doi":"10.1145/3506860.3506917","DOIUrl":"https://doi.org/10.1145/3506860.3506917","url":null,"abstract":"Informal learning procedures have been changing extremely fast over the recent decades not only due to the advent of online learning, but also due to changes in what humans need to learn to meet their various life and career goals. Consequently, online, educational platforms are expected to provide personalized, up-to-date curricula to assist learners. Therefore, in this paper, we propose an Artificial Intelligence (AI) and Crowdsourcing based approach to create and update curricula for individual learners. We show the design of this curriculum development system prototype, in which contributors receive AI-based recommendations to be able to define and update high-level learning goals, skills, and learning topics together with associated learning content. This curriculum development system was also integrated into our personalized online learning platform. To evaluate our prototype we compared experts’ opinion with our system’s recommendations, and resulted in 89%, 79%, and 93% F1-scores when recommending skills, learning topics, and educational materials respectively. Also, we interviewed eight senior level experts from educational institutions and career consulting organizations. Interviewees agreed that our curriculum development method has high potential to support authoring activities in dynamic, personalized learning environments.","PeriodicalId":185465,"journal":{"name":"LAK22: 12th International Learning Analytics and Knowledge Conference","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124128469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Empowering Teachers with AI: Co-Designing a Learning Analytics Tool for Personalized Instruction in the Science Classroom","authors":"Tanya Nazaretsky, Carmel Bar, Michal Walter, Giora Alexandron","doi":"10.1145/3506860.3506861","DOIUrl":"https://doi.org/10.1145/3506860.3506861","url":null,"abstract":"AI-powered educational technology that is designed to support teachers in providing personalized instruction can enhance their ability to address the needs of individual students, hopefully leading to better learning gains. This paper presents results from a participatory research aimed at co-designing with science teachers a learning analytics tool that will assist them in implementing a personalized pedagogy in blended learning contexts. The development process included three stages. In the first, we interviewed a group of teachers to identify where and how personalized instruction may be integrated into their teaching practices. This yielded a clustering-based personalization strategy. Next, we designed a mock-up of a learning analytics tool that supports this strategy and worked with another group of teachers to define an ‘explainable learning analytics’ scheme that explains each cluster in a way that is both pedagogically meaningful and can be generated automatically. Third, we developed an AI algorithm that supports this ‘explainable clusters’ pedagogy and conducted a controlled experiment that evaluated its contribution to teachers’ ability to plan personalized learning sequences. The planned sequences were evaluated in a blinded fashion by an expert, and the results demonstrated that the experimental group – teachers who received the clusters with the explanations – designed sequences that addressed the difficulties exhibited by different groups of students better than those designed by teachers who received the clusters without explanations. The main contribution of this study is twofold. First, it presents an effective personalization approach that fits blended learning in the science classroom, which combines a real-time clustering algorithm with an explainable-AI scheme that can automatically build pedagogically meaningful explanations from item-level meta-data (Q Matrix). Second, it demonstrates how such an end-to-end learning analytics solution can be built with teachers through a co-design process and highlights the types of knowledge that teachers add to system-provided analytics in order to apply them to their local context. As a practical contribution, this process informed the design of a new learning analytics tool that was integrated into a free online learning platform that is being used by more than 1000 science teachers.","PeriodicalId":185465,"journal":{"name":"LAK22: 12th International Learning Analytics and Knowledge Conference","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127393530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Instrument for Measuring Teachers’ Trust in AI-Based Educational Technology","authors":"Tanya Nazaretsky, M. Cukurova, Giora Alexandron","doi":"10.1145/3506860.3506866","DOIUrl":"https://doi.org/10.1145/3506860.3506866","url":null,"abstract":"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.","PeriodicalId":185465,"journal":{"name":"LAK22: 12th International Learning Analytics and Knowledge Conference","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123267921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}