Jakub Kuzilek, Z. Zdráhal, Jonas Vaclavek, Viktor Fuglík, J. Skočilas
At present, universities collect study-related data about their students. This information can be used to support students at risk of failing their studies. At the Faculty of Mechanical Engineering (FME), Czech Technical University in Prague (CTU), the group of the first-year students is the most vulnerable. The most critical part of the first year is the winter exam period when students usually divide into those who will pass and fail. One of the most important abilities, students need to learn, is exam planning, and our research aims at the exploration of the exam strategies of successful students. These strategies can be used for improving first-year students retention. The outgoing research on the analysis of exam strategies of the first-year students in the academic year 2017/2018 is reported. From a total of 361 first-year students, successful students have been selected. The successful student is the one who finished all three mandatory exams before the end of the first exam period. From the exam sequences of 153 selected students, a "layered" Markov chain probabilistic model has been constructed. It uncovered the most common exam strategies taken by those students.
{"title":"Exploring exam strategies of successful first year engineering students","authors":"Jakub Kuzilek, Z. Zdráhal, Jonas Vaclavek, Viktor Fuglík, J. Skočilas","doi":"10.1145/3375462.3375469","DOIUrl":"https://doi.org/10.1145/3375462.3375469","url":null,"abstract":"At present, universities collect study-related data about their students. This information can be used to support students at risk of failing their studies. At the Faculty of Mechanical Engineering (FME), Czech Technical University in Prague (CTU), the group of the first-year students is the most vulnerable. The most critical part of the first year is the winter exam period when students usually divide into those who will pass and fail. One of the most important abilities, students need to learn, is exam planning, and our research aims at the exploration of the exam strategies of successful students. These strategies can be used for improving first-year students retention. The outgoing research on the analysis of exam strategies of the first-year students in the academic year 2017/2018 is reported. From a total of 361 first-year students, successful students have been selected. The successful student is the one who finished all three mandatory exams before the end of the first exam period. From the exam sequences of 153 selected students, a \"layered\" Markov chain probabilistic model has been constructed. It uncovered the most common exam strategies taken by those students.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"2 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":"127888696","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}
Empirical evidence of how background music benefits or hinders learning becomes the crux of optimizing music recommendation in educational settings. This study aims to further probe the underlying mechanism through an experiment in naturalistic setting. 30 participants were recruited to join a field experiment which was conducted in their own study places for one week. During the experiment, participants were asked to conduct learning sessions with music in the background and collect music tracks they deemed suitable for learning using a novel mobile-based music discovery application. A set of participant-related, context-related, and music-related data were collected via a pre-experiment questionnaire, surveys popped up in the music app, and the logging system of the music app. Preliminary results reveal correlations between certain music characteristics and learners' task engagement and perceived task performance. This study is expected to provide evidence for understanding cognitive and emotional dimensions of background music during learning, as well as implications for the role of personalization in the selection of background music for facilitating learning.
{"title":"Learning with background music: a field experiment","authors":"Fanjie Li, Xiao Hu, Y. Que","doi":"10.1145/3375462.3375529","DOIUrl":"https://doi.org/10.1145/3375462.3375529","url":null,"abstract":"Empirical evidence of how background music benefits or hinders learning becomes the crux of optimizing music recommendation in educational settings. This study aims to further probe the underlying mechanism through an experiment in naturalistic setting. 30 participants were recruited to join a field experiment which was conducted in their own study places for one week. During the experiment, participants were asked to conduct learning sessions with music in the background and collect music tracks they deemed suitable for learning using a novel mobile-based music discovery application. A set of participant-related, context-related, and music-related data were collected via a pre-experiment questionnaire, surveys popped up in the music app, and the logging system of the music app. Preliminary results reveal correlations between certain music characteristics and learners' task engagement and perceived task performance. This study is expected to provide evidence for understanding cognitive and emotional dimensions of background music during learning, as well as implications for the role of personalization in the selection of background music for facilitating learning.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"27 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":"123763366","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}
Gian Barbosa, Raissa Camelo, Anderson Pinheiro Cavalcanti, P. Miranda, R. F. Mello, Vitomir Kovanovíc, D. Gašević
This paper presents a study that examined automated cross-language classification of online discussion messages for the levels of cognitive presence, a key construct from the widely used Community of Inquiry (CoI) model of online learning. Specifically, we examined the classification of 1,500 Portuguese language discussion messages using a classifier trained on a corpus of the 1,747 English language discussion messages. In the study, a random forest classifier was developed using a small set of 108 validated indicators of psychological processes, linguistic coherence, and online discussion structure. The classifier obtained 67% accuracy and Cohen's κ of 0.32, showing a moderate level of inter-rater agreement above chance and the general viability of the proposed approach. Most importantly, the findings suggest that certain aspects of cognitive presence construct are highly generalizable and transfer across different languages. Finally, the paper also presents a novel method for addressing class imbalance problem using a generic algorithm heuristic technique, which provided substantial improvements over the use of imbalanced dataset. Results and practical implications are further discussed.
{"title":"Towards automatic cross-language classification of cognitive presence in online discussions","authors":"Gian Barbosa, Raissa Camelo, Anderson Pinheiro Cavalcanti, P. Miranda, R. F. Mello, Vitomir Kovanovíc, D. Gašević","doi":"10.1145/3375462.3375496","DOIUrl":"https://doi.org/10.1145/3375462.3375496","url":null,"abstract":"This paper presents a study that examined automated cross-language classification of online discussion messages for the levels of cognitive presence, a key construct from the widely used Community of Inquiry (CoI) model of online learning. Specifically, we examined the classification of 1,500 Portuguese language discussion messages using a classifier trained on a corpus of the 1,747 English language discussion messages. In the study, a random forest classifier was developed using a small set of 108 validated indicators of psychological processes, linguistic coherence, and online discussion structure. The classifier obtained 67% accuracy and Cohen's κ of 0.32, showing a moderate level of inter-rater agreement above chance and the general viability of the proposed approach. Most importantly, the findings suggest that certain aspects of cognitive presence construct are highly generalizable and transfer across different languages. Finally, the paper also presents a novel method for addressing class imbalance problem using a generic algorithm heuristic technique, which provided substantial improvements over the use of imbalanced dataset. Results and practical implications are further discussed.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"23 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":"115410780","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 this work we intend to develop cognitive modules for learning analytics solutions used in inquiry learning environments that can monitor and assess mental abilities involved in self-directed learning activities. We realize this idea by drawing on models from mathematical psychology, which specify assumptions about the human mind algorithmically and thereby automate a theory-driven data analysis. We report a study to exemplify this approach in which N=105 15-year-old high school students perform a self-determined navigation in a taxonomy of dinosaur concepts. We analyze their search and learning traces through the lens of a connectionist network model of working memory (WM). The results are encouraging in three ways. First, the model predicts students' average progress (as well as difficulties) in forming new concepts at high accuracy. Second, a simple (1-parameter) extension, which we derive from a meta-cognitive learning framework, is sufficient to also predict aggregated search patterns. Third, our initial attempt to fit the model to individual data offers some promising results: estimates of a free parameter correlate significantly with a measure of WM capacity. Together, we believe that these results help demonstrate a novel and promising way towards extending learner models by cognitive variables. We also discuss current limitations in the light of our future work on cognitive-computational scaffolding techniques in inquiry learning scenarios.
{"title":"How working memory capacity limits success in self-directed learning: a cognitive model of search and concept formation","authors":"Paul Seitlinger, Abida Bibi, Õnne Uus, Tobias Ley","doi":"10.1145/3375462.3375480","DOIUrl":"https://doi.org/10.1145/3375462.3375480","url":null,"abstract":"With this work we intend to develop cognitive modules for learning analytics solutions used in inquiry learning environments that can monitor and assess mental abilities involved in self-directed learning activities. We realize this idea by drawing on models from mathematical psychology, which specify assumptions about the human mind algorithmically and thereby automate a theory-driven data analysis. We report a study to exemplify this approach in which N=105 15-year-old high school students perform a self-determined navigation in a taxonomy of dinosaur concepts. We analyze their search and learning traces through the lens of a connectionist network model of working memory (WM). The results are encouraging in three ways. First, the model predicts students' average progress (as well as difficulties) in forming new concepts at high accuracy. Second, a simple (1-parameter) extension, which we derive from a meta-cognitive learning framework, is sufficient to also predict aggregated search patterns. Third, our initial attempt to fit the model to individual data offers some promising results: estimates of a free parameter correlate significantly with a measure of WM capacity. Together, we believe that these results help demonstrate a novel and promising way towards extending learner models by cognitive variables. We also discuss current limitations in the light of our future work on cognitive-computational scaffolding techniques in inquiry learning scenarios.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":" 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113953352","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}
S. V. Goidsenhoven, D. Bogdanova, Galina Deeva, S. V. Broucke, Jochen De Weerdt, M. Snoeck
Blended learning is gaining ground in contemporary education. However, studies on predictive learning analytics in the context of blended learning remain relatively scarce compared to Massive Open Online Courses (MOOCs), where such applications have gained a strong foothold. Data sets obtained from blended learning environments suffer from a high dimensionality and typically expose a limited number of instances, which makes predictive analysis a challenging task. In this work, we explore the log data of a master-level blended course to predict the students' grades based entirely on the data obtained from an online module (a small private online course), using and comparing logistic regression and random forest-based predictive models. The results of the analysis show that, despite the limited data, success vs. fail predictions can be made as early as in the middle of the course. This could be used in the future for timely interventions, both for failure prevention as well as for reinforcing positive learning behaviours of students.
{"title":"Predicting student success in a blended learning environment","authors":"S. V. Goidsenhoven, D. Bogdanova, Galina Deeva, S. V. Broucke, Jochen De Weerdt, M. Snoeck","doi":"10.1145/3375462.3375494","DOIUrl":"https://doi.org/10.1145/3375462.3375494","url":null,"abstract":"Blended learning is gaining ground in contemporary education. However, studies on predictive learning analytics in the context of blended learning remain relatively scarce compared to Massive Open Online Courses (MOOCs), where such applications have gained a strong foothold. Data sets obtained from blended learning environments suffer from a high dimensionality and typically expose a limited number of instances, which makes predictive analysis a challenging task. In this work, we explore the log data of a master-level blended course to predict the students' grades based entirely on the data obtained from an online module (a small private online course), using and comparing logistic regression and random forest-based predictive models. The results of the analysis show that, despite the limited data, success vs. fail predictions can be made as early as in the middle of the course. This could be used in the future for timely interventions, both for failure prevention as well as for reinforcing positive learning behaviours of students.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"1 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":"130108478","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}
A. Duff, Andrew Zamecnik, A. Pardo, Elizabeth Smith
Students enrol into STEM programs with varying degrees of confidence with citing and referencing texts in their written work. Students often have an inclination to choose numbers over written language throughout schooling which means less opportunity to practice referencing and citation. This is compounded by large numbers of students for whom English is an additional language or who articulate from different cultural ways-of-doing. The Search, Evaluate, Integrate, Reference and Act Ethically (SEIRA) modules were developed to provide discipline-relevance to a confounding task. Data Analysis looking at the student engagement with the SEIRA site and subsequent student success provides an indication of the value of this approach to developing academic literacy across the STEM disciplines.
{"title":"The SEIRA approach: course embedded activities to promote academic integrity and literacies in first year engineering","authors":"A. Duff, Andrew Zamecnik, A. Pardo, Elizabeth Smith","doi":"10.1145/3375462.3375497","DOIUrl":"https://doi.org/10.1145/3375462.3375497","url":null,"abstract":"Students enrol into STEM programs with varying degrees of confidence with citing and referencing texts in their written work. Students often have an inclination to choose numbers over written language throughout schooling which means less opportunity to practice referencing and citation. This is compounded by large numbers of students for whom English is an additional language or who articulate from different cultural ways-of-doing. The Search, Evaluate, Integrate, Reference and Act Ethically (SEIRA) modules were developed to provide discipline-relevance to a confounding task. Data Analysis looking at the student engagement with the SEIRA site and subsequent student success provides an indication of the value of this approach to developing academic literacy across the STEM disciplines.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"41 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":"116809575","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}
In this paper, we perform a predictive analysis of a curriculum-integrated math game, ST Math, to suggest a partial ordering for the game's curriculum sequence. We analyzed the sequence of ST Math objectives played by elementary school students in 5 U.S. districts and grouped each objective into difficult and easy categories according to how many retries were needed for students to master an objective. We observed that retries on some objectives were high in one district and low in another district where the objectives are played in a different order. Motivated by this observation, we investigated what makes an effective curriculum sequence. To infer a new partially-ordered sequence, we performed an expanded replication study of a novel predictive analysis by a prior study to find predictive relationships between 15 objectives played in different sequences by 3,328 students from 5 districts. Based on the predictive abilities of objectives in these districts, we found 17 suggested objective orderings. After deriving these orderings, we confirmed the validity of the order by evaluating the impact of the suggested sequence on changes in rates of retries and corresponding performance. We observed that when the objectives were played in the suggested sequence, we record a drastic reduction in retries, implying that these objectives are easier for students. This indicates that objectives that come earlier can provide prerequisite knowledge for later objectives. We believe that data-informed sequences, such as the ones we suggest, may improve efficiency of instruction and increase content learning and performance.
{"title":"Data-informed curriculum sequences for a curriculum-integrated game","authors":"Ruth Okoilu Akintunde, Preya Shabrina, Veronica Catété, T. Barnes, Collin Lynch, Teomara Rutherford","doi":"10.1145/3375462.3375530","DOIUrl":"https://doi.org/10.1145/3375462.3375530","url":null,"abstract":"In this paper, we perform a predictive analysis of a curriculum-integrated math game, ST Math, to suggest a partial ordering for the game's curriculum sequence. We analyzed the sequence of ST Math objectives played by elementary school students in 5 U.S. districts and grouped each objective into difficult and easy categories according to how many retries were needed for students to master an objective. We observed that retries on some objectives were high in one district and low in another district where the objectives are played in a different order. Motivated by this observation, we investigated what makes an effective curriculum sequence. To infer a new partially-ordered sequence, we performed an expanded replication study of a novel predictive analysis by a prior study to find predictive relationships between 15 objectives played in different sequences by 3,328 students from 5 districts. Based on the predictive abilities of objectives in these districts, we found 17 suggested objective orderings. After deriving these orderings, we confirmed the validity of the order by evaluating the impact of the suggested sequence on changes in rates of retries and corresponding performance. We observed that when the objectives were played in the suggested sequence, we record a drastic reduction in retries, implying that these objectives are easier for students. This indicates that objectives that come earlier can provide prerequisite knowledge for later objectives. We believe that data-informed sequences, such as the ones we suggest, may improve efficiency of instruction and increase content learning and performance.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"102 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":"133453681","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}
Hamideh Iraj, Anthea Fudge, M. Faulkner, A. Pardo, Vitomir Kovanovíc
Feedback is a major factor of student success within higher education learning. However, recent changes - such as increased class sizes and socio-economic diversity of the student population - challenged the provision of effective student feedback. Although the use of educational technology for personalised feedback to diverse students has gained traction, the feedback gap still exists: educators wonder which students respond to feedback and which do not. In this study, a set of trackable Call to Action (CTA) links was embedded in two sets of feedback messages focusing on students' time management, with the goal of (1) examining the association between feedback engagement and course success and (2), to predict students' reaction to provided feedback. We also conducted two focus groups to further examine students' perception of provided feedback messages. Our results revealed that early engagement with the feedback was associated with higher chances of succeeding in the course. Likewise, previous engagement with feedback was highly predictive of students' engagement in the future, and also that certain student sub-populations, (e.g., female students), were more likely to engage than others. Such insight enables instructors to ask "why" questions, improve feedback processes and narrow the feedback gap. Practical implications of our findings are further discussed.
{"title":"Understanding students' engagement with personalised feedback messages","authors":"Hamideh Iraj, Anthea Fudge, M. Faulkner, A. Pardo, Vitomir Kovanovíc","doi":"10.1145/3375462.3375527","DOIUrl":"https://doi.org/10.1145/3375462.3375527","url":null,"abstract":"Feedback is a major factor of student success within higher education learning. However, recent changes - such as increased class sizes and socio-economic diversity of the student population - challenged the provision of effective student feedback. Although the use of educational technology for personalised feedback to diverse students has gained traction, the feedback gap still exists: educators wonder which students respond to feedback and which do not. In this study, a set of trackable Call to Action (CTA) links was embedded in two sets of feedback messages focusing on students' time management, with the goal of (1) examining the association between feedback engagement and course success and (2), to predict students' reaction to provided feedback. We also conducted two focus groups to further examine students' perception of provided feedback messages. Our results revealed that early engagement with the feedback was associated with higher chances of succeeding in the course. Likewise, previous engagement with feedback was highly predictive of students' engagement in the future, and also that certain student sub-populations, (e.g., female students), were more likely to engage than others. Such insight enables instructors to ask \"why\" questions, improve feedback processes and narrow the feedback gap. Practical implications of our findings are further discussed.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"15 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":"122554558","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}
Melanie E. Peffer, David Quigley, Liza Brusman, Jennifer Avena, J. Knight
Problem solving, particularly in disciplines such as genetics, is an essential but difficult competency for students to master. Prior work indicated that trace data can be leveraged to measure the invisible cognitive processes that undergird learning activities such as problem solving. Building on prior work and given the importance and difficulties associated with genetics problem solving, we used unsupervised statistical methods (k-means clustering and feature selection) to characterize the patterns of processes students use during genetics problem solving and the relationship to proximal and distal outcomes. At the level of the individual problem, we found that conclusion processes, such as making claims and eliminating possible solutions, was an important interim step and associated with getting a particular problem correct. Surprisingly, we noted that a different set of processes was associated with course outcomes. Students who performed multiple metacognitive steps (e.g. monitoring, checking, planning) in a row or who engaged in execution steps (e.g. using information, drawing a picture, restating the process) as part of problem solving during the semester performed better on final assessments. We found a third set of practices, making consecutive conclusion processes, metacognitive processes preceding reasoning and reasoning preceding conclusions to be important for success at both the problem level and on final assessments. This suggests that different problem-solving processes are associated with success on different course benchmarks. This work raises provocative questions regarding best practices for teaching problem solving in genetics classrooms.
{"title":"Trace data from student solutions to genetics problems reveals variance in the processes related to different course outcomes","authors":"Melanie E. Peffer, David Quigley, Liza Brusman, Jennifer Avena, J. Knight","doi":"10.1145/3375462.3375503","DOIUrl":"https://doi.org/10.1145/3375462.3375503","url":null,"abstract":"Problem solving, particularly in disciplines such as genetics, is an essential but difficult competency for students to master. Prior work indicated that trace data can be leveraged to measure the invisible cognitive processes that undergird learning activities such as problem solving. Building on prior work and given the importance and difficulties associated with genetics problem solving, we used unsupervised statistical methods (k-means clustering and feature selection) to characterize the patterns of processes students use during genetics problem solving and the relationship to proximal and distal outcomes. At the level of the individual problem, we found that conclusion processes, such as making claims and eliminating possible solutions, was an important interim step and associated with getting a particular problem correct. Surprisingly, we noted that a different set of processes was associated with course outcomes. Students who performed multiple metacognitive steps (e.g. monitoring, checking, planning) in a row or who engaged in execution steps (e.g. using information, drawing a picture, restating the process) as part of problem solving during the semester performed better on final assessments. We found a third set of practices, making consecutive conclusion processes, metacognitive processes preceding reasoning and reasoning preceding conclusions to be important for success at both the problem level and on final assessments. This suggests that different problem-solving processes are associated with success on different course benchmarks. This work raises provocative questions regarding best practices for teaching problem solving in genetics classrooms.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"26 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":"121453236","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}
Louis Lecailliez, B. Flanagan, Mei-Rong Alice Chen, H. Ogata
Reading, be it intensive or extensive, is one of the key skills required to master English as a foreign language (EFL) learner. Computerized e-book systems provide convenient access to learning materials inside and outside class. Students may regularly check the meaning of a word or expression using a separate tool to progress on their reading, which is not only disruptive but can lead to other learning problems. An example of a particular issue faced in EFL is when a student learns an inappropriate meaning of a polysemous word for the context in which it is presented. This is also a problem for teachers as they often need to investigate the cause. In this paper, we propose a smart dictionary integrated into an e-book reading platform. It allows the learner to search and note word definitions directly with the purpose of reducing context switching and improve vocabulary retention. Finally, we propose that learner interactions with the system can be analyzed to support EFL teachers in identifying possible problems that arise through dictionary use while reading.
{"title":"Smart dictionary for e-book reading analytics","authors":"Louis Lecailliez, B. Flanagan, Mei-Rong Alice Chen, H. Ogata","doi":"10.1145/3375462.3375499","DOIUrl":"https://doi.org/10.1145/3375462.3375499","url":null,"abstract":"Reading, be it intensive or extensive, is one of the key skills required to master English as a foreign language (EFL) learner. Computerized e-book systems provide convenient access to learning materials inside and outside class. Students may regularly check the meaning of a word or expression using a separate tool to progress on their reading, which is not only disruptive but can lead to other learning problems. An example of a particular issue faced in EFL is when a student learns an inappropriate meaning of a polysemous word for the context in which it is presented. This is also a problem for teachers as they often need to investigate the cause. In this paper, we propose a smart dictionary integrated into an e-book reading platform. It allows the learner to search and note word definitions directly with the purpose of reducing context switching and improve vocabulary retention. Finally, we propose that learner interactions with the system can be analyzed to support EFL teachers in identifying possible problems that arise through dictionary use while reading.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"108 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":"122031592","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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