Reflection assessment is a critical component of health professions education that can be used for personalized learning support. However, reflection assessment at scale remains a challenge due to the demanding nature of tasks and the common use of simplified criteria of quality. This study addressed this issue by developing a multi-dimensional automated assessment that uses linguistic models to classify reflections by overall quality (depth) and the presence of six constituent elements denoting quality (description, analysis, feeling, perspective, evaluation, and outcome). 1500 reflections from 369 dental students were manually coded to establish ground truth. Classifiers for each of the six elements were trained and tested based on linguistic features extracted using the LIWC tool applying both single-label and multi-label classification approaches. Classifiers for depth were built both directly from linguistic features and based on the presence of the six elements. Results showed that linguistic modeling can be used to reliably detect the presence of reflection elements and the level of depth. However, the depth classifier showed a heavy reliance on cognitive elements (description, analysis, and evaluation) rather than the others. These findings indicate the feasibility of implementing multidimensional automated assessment in health professions education and the need to reconsider how quality of reflection is conceptualized.
{"title":"How and how well do students reflect?: multi-dimensional automated reflection assessment in health professions education","authors":"Yeonji Jung, A. Wise","doi":"10.1145/3375462.3375528","DOIUrl":"https://doi.org/10.1145/3375462.3375528","url":null,"abstract":"Reflection assessment is a critical component of health professions education that can be used for personalized learning support. However, reflection assessment at scale remains a challenge due to the demanding nature of tasks and the common use of simplified criteria of quality. This study addressed this issue by developing a multi-dimensional automated assessment that uses linguistic models to classify reflections by overall quality (depth) and the presence of six constituent elements denoting quality (description, analysis, feeling, perspective, evaluation, and outcome). 1500 reflections from 369 dental students were manually coded to establish ground truth. Classifiers for each of the six elements were trained and tested based on linguistic features extracted using the LIWC tool applying both single-label and multi-label classification approaches. Classifiers for depth were built both directly from linguistic features and based on the presence of the six elements. Results showed that linguistic modeling can be used to reliably detect the presence of reflection elements and the level of depth. However, the depth classifier showed a heavy reliance on cognitive elements (description, analysis, and evaluation) rather than the others. These findings indicate the feasibility of implementing multidimensional automated assessment in health professions education and the need to reconsider how quality of reflection is conceptualized.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126198540","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}
Learning analytics promises to support adaptive learning in higher education. However, the associated issues around privacy protection, especially their implications for students as data subjects, has been a hurdle to wide-scale adoption. In light of this, we set out to understand student expectations of privacy issues related to learning analytics and to identify gaps between what students desire and what they expect to happen or choose to do in reality when it comes to privacy protection. To this end, an investigation was carried out in a UK higher education institution using a survey (N=674) and six focus groups (26 students). The study highlight a number of key implications for learning analytics research and practice: (1) purpose, access, and anonymity are key benchmarks of ethics and privacy integrity; (2) transparency and communication are key levers for learning analytics adoption; and (3) information asymmetry can impede active participation of students in learning analytics.
{"title":"The privacy paradox and its implications for learning analytics","authors":"Yi-Shan Tsai, A. Whitelock-Wainwright, D. Gašević","doi":"10.1145/3375462.3375536","DOIUrl":"https://doi.org/10.1145/3375462.3375536","url":null,"abstract":"Learning analytics promises to support adaptive learning in higher education. However, the associated issues around privacy protection, especially their implications for students as data subjects, has been a hurdle to wide-scale adoption. In light of this, we set out to understand student expectations of privacy issues related to learning analytics and to identify gaps between what students desire and what they expect to happen or choose to do in reality when it comes to privacy protection. To this end, an investigation was carried out in a UK higher education institution using a survey (N=674) and six focus groups (26 students). The study highlight a number of key implications for learning analytics research and practice: (1) purpose, access, and anonymity are key benchmarks of ethics and privacy integrity; (2) transparency and communication are key levers for learning analytics adoption; and (3) information asymmetry can impede active participation of students in learning analytics.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125258294","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}
Namrata Srivastava, Sadia Nawaz, J. Lodge, Eduardo Velloso, S. Erfani, J. Bailey
Video is becoming a dominant medium for the delivery of educational material. Despite the widespread use of video for learning, there is still a lack of understanding about how best to help people learn in this medium. This study demonstrates the use of thermal camera as compared to traditional self-reported methods for assessing learners' cognitive load while watching video lectures of different styles. We evaluated our approach in a study with 78 university students viewing two variants of short video lectures on two different topics. To incorporate subjective measures, the students reported on mental effort, interest, prior knowledge, confidence, and challenge. Moreover, through a physical slider device, the students could continuously report on their perceived level of difficulty. Lastly, we used thermal sensor as an additional indicator of students' level of difficulty and associated cognitive load. This was achieved through, continuous real-time monitoring of students by using a thermal imaging camera. This study aims to address the following: firstly, to analyze if video styles differ in terms of the associated cognitive load. Secondly, to assess the effects of cognitive load on learning outcomes; could an increase in the cognitive load be associated with poorer learning outcomes? Third, to see if there is a match between students' perceived difficulty levels and a biological indicator. The results suggest that thermal imaging could be an effective tool to assess learners' cognitive load, and an increased cognitive load could lead to poorer performance. Moreover, in terms of the lecture styles, the animated video lectures appear to be a better tool than the text-only lectures (in the content areas tested here). The results of this study may guide future works on effective video designs, especially those that consider the cognitive load.
{"title":"Exploring the usage of thermal imaging for understanding video lecture designs and students' experiences","authors":"Namrata Srivastava, Sadia Nawaz, J. Lodge, Eduardo Velloso, S. Erfani, J. Bailey","doi":"10.1145/3375462.3375514","DOIUrl":"https://doi.org/10.1145/3375462.3375514","url":null,"abstract":"Video is becoming a dominant medium for the delivery of educational material. Despite the widespread use of video for learning, there is still a lack of understanding about how best to help people learn in this medium. This study demonstrates the use of thermal camera as compared to traditional self-reported methods for assessing learners' cognitive load while watching video lectures of different styles. We evaluated our approach in a study with 78 university students viewing two variants of short video lectures on two different topics. To incorporate subjective measures, the students reported on mental effort, interest, prior knowledge, confidence, and challenge. Moreover, through a physical slider device, the students could continuously report on their perceived level of difficulty. Lastly, we used thermal sensor as an additional indicator of students' level of difficulty and associated cognitive load. This was achieved through, continuous real-time monitoring of students by using a thermal imaging camera. This study aims to address the following: firstly, to analyze if video styles differ in terms of the associated cognitive load. Secondly, to assess the effects of cognitive load on learning outcomes; could an increase in the cognitive load be associated with poorer learning outcomes? Third, to see if there is a match between students' perceived difficulty levels and a biological indicator. The results suggest that thermal imaging could be an effective tool to assess learners' cognitive load, and an increased cognitive load could lead to poorer performance. Moreover, in terms of the lecture styles, the animated video lectures appear to be a better tool than the text-only lectures (in the content areas tested here). The results of this study may guide future works on effective video designs, especially those that consider the cognitive load.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117069064","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}
M. Cukurova, Qi Zhou, Daniel Spikol, Lorenzo Landolfi
In this study, we describe the results of our research to model collaborative problem-solving (CPS) competence based on analytics generated from video data. We have collected ~500 mins video data from 15 groups of 3 students working to solve design problems collaboratively. Initially, with the help of OpenPose, we automatically generated frequency metrics such as the number of the face-in-the-screen; and distance metrics such as the distance between bodies. Based on these metrics, we built decision trees to predict students' listening, watching, making, and speaking behaviours as well as predicting the students' CPS competence. Our results provide useful decision rules mined from analytics of video data which can be used to inform teacher dashboards. Although, the accuracy and recall values of the models built are inferior to previous machine learning work that utilizes multimodal data, the transparent nature of the decision trees provides opportunities for explainable analytics for teachers and learners. This can lead to more agency of teachers and learners, therefore can lead to easier adoption. We conclude the paper with a discussion on the value and limitations of our approach.
{"title":"Modelling collaborative problem-solving competence with transparent learning analytics: is video data enough?","authors":"M. Cukurova, Qi Zhou, Daniel Spikol, Lorenzo Landolfi","doi":"10.1145/3375462.3375484","DOIUrl":"https://doi.org/10.1145/3375462.3375484","url":null,"abstract":"In this study, we describe the results of our research to model collaborative problem-solving (CPS) competence based on analytics generated from video data. We have collected ~500 mins video data from 15 groups of 3 students working to solve design problems collaboratively. Initially, with the help of OpenPose, we automatically generated frequency metrics such as the number of the face-in-the-screen; and distance metrics such as the distance between bodies. Based on these metrics, we built decision trees to predict students' listening, watching, making, and speaking behaviours as well as predicting the students' CPS competence. Our results provide useful decision rules mined from analytics of video data which can be used to inform teacher dashboards. Although, the accuracy and recall values of the models built are inferior to previous machine learning work that utilizes multimodal data, the transparent nature of the decision trees provides opportunities for explainable analytics for teachers and learners. This can lead to more agency of teachers and learners, therefore can lead to easier adoption. We conclude the paper with a discussion on the value and limitations of our approach.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131507794","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}
Yanghee Kim, S. Butail, Michael Tscholl, Lichuan Liu, Yunlong Wang
This study explored data analytic approaches to assessing young children's engagement in robot-mediated collaborative interaction. To develop our analytic models, we took a case-study approach and looked closely into four children's behaviors during three conversational sessions. Grounded in engagement theory, three sources of multimodal behavioral data (utterances, kinesics, and vocie) were coded through human annotation and automatic speech recognition and analysis. Then, information-theoretic methods were used to uncover nonlinear dependencies (called mutual information) among the multimodal behaviors of each child. From this, we derived a model to compute a compound variable of engagement. This computation produced engagement trends of each child, the engagement relationship between two children in a pair, and the engagement relationship with the robot over time. The computed trends corresponded well with the data from human observations. This approach has implications for quantifying engagement from rich and natural multimodal behaviors.
{"title":"An exploratory approach to measuring collaborative engagement in child robot interaction","authors":"Yanghee Kim, S. Butail, Michael Tscholl, Lichuan Liu, Yunlong Wang","doi":"10.1145/3375462.3375522","DOIUrl":"https://doi.org/10.1145/3375462.3375522","url":null,"abstract":"This study explored data analytic approaches to assessing young children's engagement in robot-mediated collaborative interaction. To develop our analytic models, we took a case-study approach and looked closely into four children's behaviors during three conversational sessions. Grounded in engagement theory, three sources of multimodal behavioral data (utterances, kinesics, and vocie) were coded through human annotation and automatic speech recognition and analysis. Then, information-theoretic methods were used to uncover nonlinear dependencies (called mutual information) among the multimodal behaviors of each child. From this, we derived a model to compute a compound variable of engagement. This computation produced engagement trends of each child, the engagement relationship between two children in a pair, and the engagement relationship with the robot over time. The computed trends corresponded well with the data from human observations. This approach has implications for quantifying engagement from rich and natural multimodal behaviors.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121931170","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}
Leon Fadljevic, Katharina Maitz, Dominik Kowald, Viktoria Pammer-Schindler, B. Gasteiger-Klicpera
This paper describes the analysis of temporal behavior of 11--15 year old students in a heavily instructionally designed adaptive e-learning environment. The e-learning system is designed to support student's acquisition of health literacy. The system adapts text difficulty depending on students' reading competence, grouping students into four competence levels. Content for the four levels of reading competence was created by clinical psychologists, pedagogues and medicine students. The e-learning system consists of an initial reading competence assessment, texts about health issues, and learning tasks related to these texts. The research question we investigate in this work is whether temporal behavior is a differentiator between students despite the system's adaptation to students' reading competence, and despite students having comparatively little freedom of action within the system. Further, we also investigated the correlation of temporal behaviour with performance. Unsupervised clustering clearly separates students into slow and fast students with respect to the time they take to complete tasks. Furthermore, topic completion time is linearly correlated with performance in the tasks. This means that we interpret working slowly in this case as diligence, which leads to more correct answers, even though the level of text difficulty matches student's reading competence. This result also points to the design opportunity to integrate advice on overarching learning strategies, such as working diligently instead of rushing through, into the student's overall learning activity. This can be done either by teachers, or via additional adaptive learning guidance within the system.
{"title":"Slow is good: the effect of diligence on student performance in the case of an adaptive learning system for health literacy","authors":"Leon Fadljevic, Katharina Maitz, Dominik Kowald, Viktoria Pammer-Schindler, B. Gasteiger-Klicpera","doi":"10.1145/3375462.3375502","DOIUrl":"https://doi.org/10.1145/3375462.3375502","url":null,"abstract":"This paper describes the analysis of temporal behavior of 11--15 year old students in a heavily instructionally designed adaptive e-learning environment. The e-learning system is designed to support student's acquisition of health literacy. The system adapts text difficulty depending on students' reading competence, grouping students into four competence levels. Content for the four levels of reading competence was created by clinical psychologists, pedagogues and medicine students. The e-learning system consists of an initial reading competence assessment, texts about health issues, and learning tasks related to these texts. The research question we investigate in this work is whether temporal behavior is a differentiator between students despite the system's adaptation to students' reading competence, and despite students having comparatively little freedom of action within the system. Further, we also investigated the correlation of temporal behaviour with performance. Unsupervised clustering clearly separates students into slow and fast students with respect to the time they take to complete tasks. Furthermore, topic completion time is linearly correlated with performance in the tasks. This means that we interpret working slowly in this case as diligence, which leads to more correct answers, even though the level of text difficulty matches student's reading competence. This result also points to the design opportunity to integrate advice on overarching learning strategies, such as working diligently instead of rushing through, into the student's overall learning activity. This can be done either by teachers, or via additional adaptive learning guidance within the system.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123436170","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}
Self-regulated learning (SRL) can predict academic performance. Yet, it is difficult for learners. The ability to self-regulate learning becomes even more important in emerging online learning settings. To support learners in developing their SRL, learning analytics (LA), which can improve learning practice by transforming the ways we support learning, is critical. This scoping review is based on the analysis of 54 papers on LA empirical research for SRL in online learning contexts published between 2011 and 2019. The research question is: What is the current state of the applications of learning analytics to measure and support students' SRL in online learning environments? The focus is on SRL phases, methods, forms of SRL support, evidence for LA and types of online learning settings. Zimmerman's model (2002) was used to examine SRL phases. The evidence about LA was examined in relation to four propositions: whether LA i) improve learning outcomes, ii) improve learning support and teaching, iii) are deployed widely, and iv) used ethically. Results showed most studies focused on SRL parts from the forethought and performance phase but much less focus on reflection. We found little evidence for LA that showed i) improvements in learning outcomes (20%), ii) improvements in learning support and teaching (22%). LA was also found iii) not used widely and iv) few studies (15%) approached research ethically. Overall, the findings show LA research was conducted mainly to measure rather than to support SRL. Thus, there is a critical need to exploit the LA support mechanisms further in order to ultimately use them to foster student SRL in online learning environments.
{"title":"Self-regulated learning and learning analytics in online learning environments: a review of empirical research","authors":"Olga Viberg, Mohammad Khalil, M. Baars","doi":"10.1145/3375462.3375483","DOIUrl":"https://doi.org/10.1145/3375462.3375483","url":null,"abstract":"Self-regulated learning (SRL) can predict academic performance. Yet, it is difficult for learners. The ability to self-regulate learning becomes even more important in emerging online learning settings. To support learners in developing their SRL, learning analytics (LA), which can improve learning practice by transforming the ways we support learning, is critical. This scoping review is based on the analysis of 54 papers on LA empirical research for SRL in online learning contexts published between 2011 and 2019. The research question is: What is the current state of the applications of learning analytics to measure and support students' SRL in online learning environments? The focus is on SRL phases, methods, forms of SRL support, evidence for LA and types of online learning settings. Zimmerman's model (2002) was used to examine SRL phases. The evidence about LA was examined in relation to four propositions: whether LA i) improve learning outcomes, ii) improve learning support and teaching, iii) are deployed widely, and iv) used ethically. Results showed most studies focused on SRL parts from the forethought and performance phase but much less focus on reflection. We found little evidence for LA that showed i) improvements in learning outcomes (20%), ii) improvements in learning support and teaching (22%). LA was also found iii) not used widely and iv) few studies (15%) approached research ethically. Overall, the findings show LA research was conducted mainly to measure rather than to support SRL. Thus, there is a critical need to exploit the LA support mechanisms further in order to ultimately use them to foster student SRL in online learning environments.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124450584","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}
To master a skill, students generally practice the content of the skill in a blocking manner. While practicing in a blocked fashion, students know the context of the problems and also which strategy is needed to arrive at a solution. However, in real life standardized tests, where problems from various skills are grouped together, students often find it challenging to identify the correct strategy to solve the problems. This is because, during learning, students often practice the content in isolation. It hinders their ability to discriminate among the contexts of the problem. In this work, using tutor funtoot, we present students working on the topic Addition Word Problems with a subtraction word problem and investigate how they perform in the out-of-context subtraction word problem. We find that students' performance in the topic Addition Word Problems is a strong predictor of their performance in this out-of-context problem. Our results suggest that it is a stronger predictor for higher grades (4th and 5th) compared to the lower (2nd and 3rd) grades.
{"title":"Decoding the performance in an out-of-context problem during blocked practice","authors":"Sweety Agrawal, Amar Lalwani","doi":"10.1145/3375462.3375542","DOIUrl":"https://doi.org/10.1145/3375462.3375542","url":null,"abstract":"To master a skill, students generally practice the content of the skill in a blocking manner. While practicing in a blocked fashion, students know the context of the problems and also which strategy is needed to arrive at a solution. However, in real life standardized tests, where problems from various skills are grouped together, students often find it challenging to identify the correct strategy to solve the problems. This is because, during learning, students often practice the content in isolation. It hinders their ability to discriminate among the contexts of the problem. In this work, using tutor funtoot, we present students working on the topic Addition Word Problems with a subtraction word problem and investigate how they perform in the out-of-context subtraction word problem. We find that students' performance in the topic Addition Word Problems is a strong predictor of their performance in this out-of-context problem. Our results suggest that it is a stronger predictor for higher grades (4th and 5th) compared to the lower (2nd and 3rd) grades.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125120402","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}
Teachers play an essential role during collaborative learning. To provide effective support, teachers have to be constantly aware of students' activities and make fast decisions about which group to offer support, without disrupting students' collaborative process. Teacher dashboards are visual displays that provide analytics about learners to help teachers increase their awareness of the situation. However, if teachers are not able to efficiently and effectively distill information from the dashboard, the dashboard can become an obstacle instead of an aid. In the present study, we compared dashboards that provide information (mirroring) to dashboards that provide information and alert the teacher to groups that are in need of support (advising). Teachers were shown standardized, fictitious collaborative situations on one of the types of dashboards and were asked to detect the group that was in need of support. The results showed that teachers in the advising condition more often detected the problematic group, needed less effort to do so, and were more confident of their decisions. The teacher-dashboard interaction patterns showed that teachers in the advising condition generally started by checking the given alert, but also that they tried to look at as much information about other groups as they could. In the mirroring condition, teachers generally started by examining information from class overviews, but did not always have time to check information for individual groups. These findings are discussed in light of the role of a teacher dashboard in teachers' decision making in the context of student collaboration.
{"title":"Comparing teachers' use of mirroring and advising dashboards","authors":"A. V. Leeuwen, N. Rummel","doi":"10.1145/3375462.3375471","DOIUrl":"https://doi.org/10.1145/3375462.3375471","url":null,"abstract":"Teachers play an essential role during collaborative learning. To provide effective support, teachers have to be constantly aware of students' activities and make fast decisions about which group to offer support, without disrupting students' collaborative process. Teacher dashboards are visual displays that provide analytics about learners to help teachers increase their awareness of the situation. However, if teachers are not able to efficiently and effectively distill information from the dashboard, the dashboard can become an obstacle instead of an aid. In the present study, we compared dashboards that provide information (mirroring) to dashboards that provide information and alert the teacher to groups that are in need of support (advising). Teachers were shown standardized, fictitious collaborative situations on one of the types of dashboards and were asked to detect the group that was in need of support. The results showed that teachers in the advising condition more often detected the problematic group, needed less effort to do so, and were more confident of their decisions. The teacher-dashboard interaction patterns showed that teachers in the advising condition generally started by checking the given alert, but also that they tried to look at as much information about other groups as they could. In the mirroring condition, teachers generally started by examining information from class overviews, but did not always have time to check information for individual groups. These findings are discussed in light of the role of a teacher dashboard in teachers' decision making in the context of student collaboration.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130226460","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}
There has been significant efforts in studying collaborative and social learning using aggregate networks. Such efforts have demonstrated the worth of the approach by providing insights about the interactions, student and teacher roles, and predictability of performance. However, using an aggregated network discounts the fine resolution of temporal interactions. By doing so, we might overlook the regularities/irregularities of students' interactions, the process of learning regulation, and how and when different actors influence each other. Thus, compressing a complex temporal process such as learning may be oversimplifying and reductionist. Through a temporal network analysis of 54 students interactions (in total 3134 interactions) in an online medical education course, this study contributes with a methodological approach to building, visualizing and quantitatively analyzing temporal networks, that could help educational practitioners understand important temporal aspects of collaborative learning that might need attention and action. Furthermore, the analysis conducted emphasize the importance of considering the time characteristics of the data that should be used when attempting to, for instance, implement early predictions of performance and early detection of students and groups that need support and attention.
{"title":"High resolution temporal network analysis to understand and improve collaborative learning","authors":"Mohammed Saqr, Jalal Nouri","doi":"10.1145/3375462.3375501","DOIUrl":"https://doi.org/10.1145/3375462.3375501","url":null,"abstract":"There has been significant efforts in studying collaborative and social learning using aggregate networks. Such efforts have demonstrated the worth of the approach by providing insights about the interactions, student and teacher roles, and predictability of performance. However, using an aggregated network discounts the fine resolution of temporal interactions. By doing so, we might overlook the regularities/irregularities of students' interactions, the process of learning regulation, and how and when different actors influence each other. Thus, compressing a complex temporal process such as learning may be oversimplifying and reductionist. Through a temporal network analysis of 54 students interactions (in total 3134 interactions) in an online medical education course, this study contributes with a methodological approach to building, visualizing and quantitatively analyzing temporal networks, that could help educational practitioners understand important temporal aspects of collaborative learning that might need attention and action. Furthermore, the analysis conducted emphasize the importance of considering the time characteristics of the data that should be used when attempting to, for instance, implement early predictions of performance and early detection of students and groups that need support and attention.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130550318","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}
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