Charlotte Larmuseau, Pieter Vanneste, P. Desmet, F. Depaepe
This exploratory study challenges the current practices in cognitive load measurement by using multichannel data to investigate cognitive load affordances during online complex problem solving. Moreover, it is an attempt to investigate how cognitive load is related to strategy use. Accordingly, in the current study a well- and an ill-structured problem were developed in a virtual learning environment. Online support was provided. Participants were 15 students from the teacher training program. This study incorporated subjective measurements of students' cognitive load (i.e., intrinsic, extraneous, germane load and their mental effort) combined with physiological data containing galvanic skin response (GSR) and skin temperature (ST). A first aim was to investigate whether there was a significant difference for the subjective measurements, physiological data and consultation of support between the well-and ill-structured problem. Secondly this study investigated how individual differences of subjective measurements are related to individual differences of physiological data and consultation of support. Results reveal significant differences for intrinsic load, mental effort between a well- and ill-structured problem. Moreover, when investigating individual differences, findings reveal that GSR might be related to mental effort. Additionally, results indicate that cognitive load influences strategy use. Future research with larger sample sizes should verify these findings in order to have more insight into how we can measure cognitive load and how its related to self-directed learning. These insights should allow us to provide adaptive support in virtual learning environments.
{"title":"Multichannel data for understanding cognitive affordances during complex problem solving","authors":"Charlotte Larmuseau, Pieter Vanneste, P. Desmet, F. Depaepe","doi":"10.1145/3303772.3303778","DOIUrl":"https://doi.org/10.1145/3303772.3303778","url":null,"abstract":"This exploratory study challenges the current practices in cognitive load measurement by using multichannel data to investigate cognitive load affordances during online complex problem solving. Moreover, it is an attempt to investigate how cognitive load is related to strategy use. Accordingly, in the current study a well- and an ill-structured problem were developed in a virtual learning environment. Online support was provided. Participants were 15 students from the teacher training program. This study incorporated subjective measurements of students' cognitive load (i.e., intrinsic, extraneous, germane load and their mental effort) combined with physiological data containing galvanic skin response (GSR) and skin temperature (ST). A first aim was to investigate whether there was a significant difference for the subjective measurements, physiological data and consultation of support between the well-and ill-structured problem. Secondly this study investigated how individual differences of subjective measurements are related to individual differences of physiological data and consultation of support. Results reveal significant differences for intrinsic load, mental effort between a well- and ill-structured problem. Moreover, when investigating individual differences, findings reveal that GSR might be related to mental effort. Additionally, results indicate that cognitive load influences strategy use. Future research with larger sample sizes should verify these findings in order to have more insight into how we can measure cognitive load and how its related to self-directed learning. These insights should allow us to provide adaptive support in virtual learning environments.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133380911","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}
Science education reforms in the United States call for an emphasis on teaching of scientific practices, such as inquiry. Previous work examined expert versus novice practices in authentic science inquiry and found although experts have fairly consistent inquiry strategies, novices exist on a continuum. In this paper, we extend our previous qualitative work to quantitatively analyze differences in inquiry practices among novices. Using clustering analysis, we found that non-science majors who performed simple investigations tended to cluster together and biology majors who performed complex investigations also tended to cluster together. We observed two additional clusters that contain both non-science majors and biology majors, but who performed distinct inquiry strategies. This raises some critical questions about how to pedagogically target students within each cluster.
{"title":"Clustering Analysis Reveals Authentic Science Inquiry Trajectories Among Undergraduates","authors":"Melanie E. Peffer, David Quigley, M. Mostowfi","doi":"10.1145/3303772.3303831","DOIUrl":"https://doi.org/10.1145/3303772.3303831","url":null,"abstract":"Science education reforms in the United States call for an emphasis on teaching of scientific practices, such as inquiry. Previous work examined expert versus novice practices in authentic science inquiry and found although experts have fairly consistent inquiry strategies, novices exist on a continuum. In this paper, we extend our previous qualitative work to quantitatively analyze differences in inquiry practices among novices. Using clustering analysis, we found that non-science majors who performed simple investigations tended to cluster together and biology majors who performed complex investigations also tended to cluster together. We observed two additional clusters that contain both non-science majors and biology majors, but who performed distinct inquiry strategies. This raises some critical questions about how to pedagogically target students within each cluster.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124449284","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}
Lisa-Angelique Lim, S. Dawson, Srécko Joksimovíc, D. Gašević
Learning Analytics Dashboards (LAD) are becoming an increasingly popular way to provide students with personalised feedback. Despite the number of LADs being developed, significant research gaps exist around the student perspective, especially how students make sense of graphics provided in LADs, and how they intend to act on the feedback provided therein. This study employed a randomized-controlled trial to examine students' sense-making of LADs showing four different frames of reference, and to what extent the impact of LADs was mediated by baseline self-regulation. Using a mix of quantitative and qualitative data analysis, the results revealed rather distinct patterns in students' sense-making across the four LADs. These patterns involved the intersection of visual salience and planned learning actions. However, collectively, across all four LADs a consistent theme emerged around students planned learning actions. This theme was classified as time and study environment management. A key finding of the study is that the use of LADs as a primary feedback process should be personalized and include training and support to aid student sensemaking.
{"title":"Exploring students' sensemaking of learning analytics dashboards: Does frame of reference make a difference?","authors":"Lisa-Angelique Lim, S. Dawson, Srécko Joksimovíc, D. Gašević","doi":"10.1145/3303772.3303804","DOIUrl":"https://doi.org/10.1145/3303772.3303804","url":null,"abstract":"Learning Analytics Dashboards (LAD) are becoming an increasingly popular way to provide students with personalised feedback. Despite the number of LADs being developed, significant research gaps exist around the student perspective, especially how students make sense of graphics provided in LADs, and how they intend to act on the feedback provided therein. This study employed a randomized-controlled trial to examine students' sense-making of LADs showing four different frames of reference, and to what extent the impact of LADs was mediated by baseline self-regulation. Using a mix of quantitative and qualitative data analysis, the results revealed rather distinct patterns in students' sense-making across the four LADs. These patterns involved the intersection of visual salience and planned learning actions. However, collectively, across all four LADs a consistent theme emerged around students planned learning actions. This theme was classified as time and study environment management. A key finding of the study is that the use of LADs as a primary feedback process should be personalized and include training and support to aid student sensemaking.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123639397","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}
W. Matcha, D. Gašević, Nora'ayu Ahmad Uzir, J. Jovanović, A. Pardo
Learning analytics has the potential to detect and explain characteristics of learning strategies through analysis of trace data and communicate the findings via feedback. However, the role of learning analytics-based feedback in selection and regulation of learning strategies is still insufficiently explored and understood. This research aims to examine the sequential and temporal characteristics of learning strategies and investigate their association with feedback. Three years of trace data were collected from online pre-class activities of a flipped classroom, where different types of feedback were employed in each year. Clustering, sequence mining, and process mining were used to detect and interpret learning tactics and strategies. Inferential statistics were used to examine the association of feedback with the learning performance and the detected learning strategies. The results suggest a positive association between the personalised feedback and the effective strategies.
{"title":"Analytics of Learning Strategies: Associations with Academic Performance and Feedback","authors":"W. Matcha, D. Gašević, Nora'ayu Ahmad Uzir, J. Jovanović, A. Pardo","doi":"10.1145/3303772.3303787","DOIUrl":"https://doi.org/10.1145/3303772.3303787","url":null,"abstract":"Learning analytics has the potential to detect and explain characteristics of learning strategies through analysis of trace data and communicate the findings via feedback. However, the role of learning analytics-based feedback in selection and regulation of learning strategies is still insufficiently explored and understood. This research aims to examine the sequential and temporal characteristics of learning strategies and investigate their association with feedback. Three years of trace data were collected from online pre-class activities of a flipped classroom, where different types of feedback were employed in each year. Clustering, sequence mining, and process mining were used to detect and interpret learning tactics and strategies. Inferential statistics were used to examine the association of feedback with the learning performance and the detected learning strategies. The results suggest a positive association between the personalised feedback and the effective strategies.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122178385","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}
It is a common phenomenon for students to listen to background music while studying. However, there are mixed and inconclusive Kindings in the literature, leaving it unclear whether and in which circumstances background music can facilitate or hinder learning. This paper reports a study investigating the effects of Kive different types of background audio (four types of music and one environmental sound) on reading comprehension. An experiment was conducted with 33 graduate students, where a series of cognitive, metacognitive, affective variables and physiological signals were collected and analyzed. Preliminary results show that there were differences on these variables across different music types. This study contributes to the understanding and optimizing of background music for facilitating learning.
{"title":"Can Background Music Facilitate Learning?: Preliminary Results on Reading Comprehension","authors":"Xiao Hu, Fanjie Li, Runzhi Kong","doi":"10.1145/3303772.3303839","DOIUrl":"https://doi.org/10.1145/3303772.3303839","url":null,"abstract":"It is a common phenomenon for students to listen to background music while studying. However, there are mixed and inconclusive Kindings in the literature, leaving it unclear whether and in which circumstances background music can facilitate or hinder learning. This paper reports a study investigating the effects of Kive different types of background audio (four types of music and one environmental sound) on reading comprehension. An experiment was conducted with 33 graduate students, where a series of cognitive, metacognitive, affective variables and physiological signals were collected and analyzed. Preliminary results show that there were differences on these variables across different music types. This study contributes to the understanding and optimizing of background music for facilitating learning.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125333273","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}
Evidence suggests that individuals are often willing to exchange personal data for (real or perceived) benefits. Such an exchange may be impacted by their trust in a particular context and their (real or perceived) control over their data. Students remain concerned about the scope and detail of surveillance of their learning behavior, their privacy, their control over what data are collected, the purpose of the collection, and the implications of any analysis. Questions arise as to the extent to which students are aware of the benefits and risks inherent in the exchange of their data, and whether they are willing to exchange personal data for more effective and supported learning experiences. This study reports on the views of entry level students at the Open University (OU) in 2018. The primary aim is to explore differences between stated attitudes to privacy and their online behaviors, and whether these same attitudes extend to their university's uses of their (personal) data. The analysis indicates, inter alia, that there is no obvious relationship between how often students are online or their awareness of/concerns about privacy issues in online contexts and what they actually do to protect themselves. Significantly though, the findings indicate that students overwhelmingly have an inherent trust in their university to use their data appropriately and ethically. Based on the findings, we outline a number of issues for consideration by higher education institutions, such as the need for transparency (of purpose and scope), the provision of some element of student control, and an acknowledgment of the exchange value of information in the nexus of the privacy calculus.
{"title":"Learning analytics at the intersections of student trust, disclosure and benefit","authors":"Sharon Slade, P. Prinsloo, Mohammad Khalil","doi":"10.1145/3303772.3303796","DOIUrl":"https://doi.org/10.1145/3303772.3303796","url":null,"abstract":"Evidence suggests that individuals are often willing to exchange personal data for (real or perceived) benefits. Such an exchange may be impacted by their trust in a particular context and their (real or perceived) control over their data. Students remain concerned about the scope and detail of surveillance of their learning behavior, their privacy, their control over what data are collected, the purpose of the collection, and the implications of any analysis. Questions arise as to the extent to which students are aware of the benefits and risks inherent in the exchange of their data, and whether they are willing to exchange personal data for more effective and supported learning experiences. This study reports on the views of entry level students at the Open University (OU) in 2018. The primary aim is to explore differences between stated attitudes to privacy and their online behaviors, and whether these same attitudes extend to their university's uses of their (personal) data. The analysis indicates, inter alia, that there is no obvious relationship between how often students are online or their awareness of/concerns about privacy issues in online contexts and what they actually do to protect themselves. Significantly though, the findings indicate that students overwhelmingly have an inherent trust in their university to use their data appropriately and ethically. Based on the findings, we outline a number of issues for consideration by higher education institutions, such as the need for transparency (of purpose and scope), the provision of some element of student control, and an acknowledgment of the exchange value of information in the nexus of the privacy calculus.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127795152","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}
With increasing abundance and ubiquity of mobile phones, desktop PCs, and tablets in the last decade, we are seeing students intermixing these modalities to learn and regulate their learning. However, the role of these modalities in educational settings is still largely under-researched. Similarly, little attention has been paid to the research on the extension of learning analytics to analyze the learning processes of students adopting various modalities during a learning activity. Traditionally, research on how modalities affect the way in which activities are completed has mainly relied upon self-reported data or mere counts of access from each modality. We explore the use of technological modalities in regulating learning via learning management systems (LMS) in the context of blended courses. We used data mining techniques to analyze patterns in sequences of actions performed by learners (n = 120) across different modalities in order to identify technological modality profiles of sequences. These profiles were used to detect the technological modality strategies adopted by students. We found a moderate effect size (∈2 = 0.12) of students' adopted strategies on the final course grade. Furthermore, when looking specifically at online discussion engagement and performance, students' adopted technological modality strategies explained a large amount of variance (η2 = 0.68) in their engagement and quality of contributions. The result implications and further research are discussed.
{"title":"On multi-device use: Using technological modality profiles to explain differences in students' learning","authors":"Varshita Sher, M. Hatala, D. Gašević","doi":"10.1145/3303772.3303790","DOIUrl":"https://doi.org/10.1145/3303772.3303790","url":null,"abstract":"With increasing abundance and ubiquity of mobile phones, desktop PCs, and tablets in the last decade, we are seeing students intermixing these modalities to learn and regulate their learning. However, the role of these modalities in educational settings is still largely under-researched. Similarly, little attention has been paid to the research on the extension of learning analytics to analyze the learning processes of students adopting various modalities during a learning activity. Traditionally, research on how modalities affect the way in which activities are completed has mainly relied upon self-reported data or mere counts of access from each modality. We explore the use of technological modalities in regulating learning via learning management systems (LMS) in the context of blended courses. We used data mining techniques to analyze patterns in sequences of actions performed by learners (n = 120) across different modalities in order to identify technological modality profiles of sequences. These profiles were used to detect the technological modality strategies adopted by students. We found a moderate effect size (∈2 = 0.12) of students' adopted strategies on the final course grade. Furthermore, when looking specifically at online discussion engagement and performance, students' adopted technological modality strategies explained a large amount of variance (η2 = 0.68) in their engagement and quality of contributions. The result implications and further research are discussed.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114906818","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}
Privacy concerns may lead people to opt-in or opt-out of having their educational data collected. These decisions may impact the performance of educational predictive models. To understand this, we conducted a survey to determine the propensity of students to withhold or grant access to their data for the purposes of training predictive models. We simulated the effects of opt-out on the accuracy of educational predictive models by dropping a random sample of data over a range of increments, and then contextualize our findings using the survey results. We find that grade predictive models are fairly robust and that kappa scores do not decrease unless there is signiicant opt-out, but when there is, the deteriorating performance disproportionately affects certain subpopulations.
{"title":"The Impact of Student Opt-Out on Educational Predictive Models","authors":"Warren Li, Christopher A. Brooks, F. Schaub","doi":"10.1145/3303772.3303809","DOIUrl":"https://doi.org/10.1145/3303772.3303809","url":null,"abstract":"Privacy concerns may lead people to opt-in or opt-out of having their educational data collected. These decisions may impact the performance of educational predictive models. To understand this, we conducted a survey to determine the propensity of students to withhold or grant access to their data for the purposes of training predictive models. We simulated the effects of opt-out on the accuracy of educational predictive models by dropping a random sample of data over a range of increments, and then contextualize our findings using the survey results. We find that grade predictive models are fairly robust and that kappa scores do not decrease unless there is signiicant opt-out, but when there is, the deteriorating performance disproportionately affects certain subpopulations.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129004093","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}
R. Manrique, B. Nunes, O. Mariño, M. Casanova, Terhi Nurmikko-Fuller
Identifying and monitoring students who are likely to dropout is a vital issue for universities. Early detection allows institutions to intervene, addressing problems and retaining students. Prior research into the early detection of at-risk students has opted for the use of predictive models, but a comprehensive assessment of the suitability of different algorithms and approaches is complicated by the large number of variable features that constitute a student's educational experience. Predictive models vary in terms of their amplitude, temporality and the learning algorithms employed. While amplitude refers to the ability of the model to operate on multiple degrees, temporality is often considered due to the natural temporal aspect of the data. In the absence of a comparative framework of learning algorithms, the aim of this paper has been to provide such an analysis, based on a proposed classification of strategies for predicting dropouts in Higher Education Institutions. Three different student representations are implemented (namely Global Feature-Based, Local Feature-Based, and Time Series) in conjunction with the appropriate learning algorithms for each of them. A description of each approach, as well as its implementation process, are presented in this paper as technical contributions. An experiment based on a dataset of student information from two degrees, namely Business Administration and Architecture, acquired through an automated management system from a university in Brazil is used. Our findings can be summarized as: (i) of the three proposed student representations, the Local Feature-Based was the most suitable approach for predicting dropout. In addition to providing high quality results, the Local Feature-Based representations are simple to build, and the construction of the model is less expensive when compared to more complex ones; (ii) as a conclusion of the results obtained via Local Feature-Based, dropout can be said to be accurately predicted using grades of a few core courses, so there is no need for a complex features extraction process; (iii) considering temporal aspects of the data does not seem to contribute to the prediction performance although it increases computational costs as the model complexity increases.
{"title":"An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree Dropout","authors":"R. Manrique, B. Nunes, O. Mariño, M. Casanova, Terhi Nurmikko-Fuller","doi":"10.1145/3303772.3303800","DOIUrl":"https://doi.org/10.1145/3303772.3303800","url":null,"abstract":"Identifying and monitoring students who are likely to dropout is a vital issue for universities. Early detection allows institutions to intervene, addressing problems and retaining students. Prior research into the early detection of at-risk students has opted for the use of predictive models, but a comprehensive assessment of the suitability of different algorithms and approaches is complicated by the large number of variable features that constitute a student's educational experience. Predictive models vary in terms of their amplitude, temporality and the learning algorithms employed. While amplitude refers to the ability of the model to operate on multiple degrees, temporality is often considered due to the natural temporal aspect of the data. In the absence of a comparative framework of learning algorithms, the aim of this paper has been to provide such an analysis, based on a proposed classification of strategies for predicting dropouts in Higher Education Institutions. Three different student representations are implemented (namely Global Feature-Based, Local Feature-Based, and Time Series) in conjunction with the appropriate learning algorithms for each of them. A description of each approach, as well as its implementation process, are presented in this paper as technical contributions. An experiment based on a dataset of student information from two degrees, namely Business Administration and Architecture, acquired through an automated management system from a university in Brazil is used. Our findings can be summarized as: (i) of the three proposed student representations, the Local Feature-Based was the most suitable approach for predicting dropout. In addition to providing high quality results, the Local Feature-Based representations are simple to build, and the construction of the model is less expensive when compared to more complex ones; (ii) as a conclusion of the results obtained via Local Feature-Based, dropout can be said to be accurately predicted using grades of a few core courses, so there is no need for a complex features extraction process; (iii) considering temporal aspects of the data does not seem to contribute to the prediction performance although it increases computational costs as the model complexity increases.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114834724","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}
Gaps between knowledge sources are interesting to various stakeholders: they might indicate potential misconceptions awaiting correction, complex or novel knowledge that requires careful delivery or studying. Motivated by these underlying values, this study explores the knowledge gap phenomenon in the context of student textual responses. In the method proposed in this study, discourses are first mapped into structured knowledge spaces where gaps between correct/incorrect responses and assessed knowledge are measured by network-based metrics. Empirical results demonstrate the effectiveness of the proposed method in measuring gaps in student responses. The networked representation of texts proposed in this study is novel in quantitatively framing gaps of knowledge. It also offers a set of validated metrics for analyzing student responses in research and practice.
{"title":"Measuring Knowledge Gaps in Student Responses by Mining Networked Representations of Texts","authors":"Chen Qiao, Xiao Hu","doi":"10.1145/3303772.3303822","DOIUrl":"https://doi.org/10.1145/3303772.3303822","url":null,"abstract":"Gaps between knowledge sources are interesting to various stakeholders: they might indicate potential misconceptions awaiting correction, complex or novel knowledge that requires careful delivery or studying. Motivated by these underlying values, this study explores the knowledge gap phenomenon in the context of student textual responses. In the method proposed in this study, discourses are first mapped into structured knowledge spaces where gaps between correct/incorrect responses and assessed knowledge are measured by network-based metrics. Empirical results demonstrate the effectiveness of the proposed method in measuring gaps in student responses. The networked representation of texts proposed in this study is novel in quantitatively framing gaps of knowledge. It also offers a set of validated metrics for analyzing student responses in research and practice.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126134295","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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