Proceedings of the 9th International Conference on Learning Analytics & Knowledge最新文献_第6页

Where You Are, Not What You See: The Impact of Learning Environment on Mind Wandering and Material Retention 你在哪里，而不是你看到什么:学习环境对走神和记忆的影响

Proceedings of the 9th International Conference on Learning Analytics & Knowledge Pub Date : 2019-03-04 DOI: 10.1145/3303772.3303824

Trish L. Varao-Sousa, Caitlin Mills, A. Kingstone

引用次数: 2

Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with Skills 面向知识追踪的知识查询网络:知识与技能的相互作用

Proceedings of the 9th International Conference on Learning Analytics & Knowledge Pub Date : 2019-03-04 DOI: 10.1145/3303772.3303786

Jinseok Lee, D. Yeung

{"title":"Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with Skills","authors":"Jinseok Lee, D. Yeung","doi":"10.1145/3303772.3303786","DOIUrl":"https://doi.org/10.1145/3303772.3303786","url":null,"abstract":"Knowledge Tracing (KT) is to trace the knowledge of students as they solve a sequence of problems represented by their related skills. This involves abstract concepts of students' states of knowledge and the interactions between those states and skills. Therefore, a KT model is designed to predict whether students will give correct answers and to describe such abstract concepts. However, existing methods either give relatively low prediction accuracy or fail to explain those concepts intuitively. In this paper, we propose a new model called Knowledge Query Network (KQN) to solve these problems. KQN uses neural networks to encode student learning activities into knowledge state and skill vectors, and models the interactions between the two types of vectors with the dot product. Through this, we introduce a novel concept called probabilistic skill similarity that relates the pairwise cosine and Euclidean distances between skill vectors to the odds ratios of the corresponding skills, which makes KQN interpretable and intuitive. On four public datasets, we have carried out experiments to show the following: 1. KQN outperforms all the existing KT models based on prediction accuracy. 2. The interaction between the knowledge state and skills can be visualized for interpretation. 3. Based on probabilistic skill similarity, a skill domain can be analyzed with clustering using the distances between the skill vectors of KQN. 4. For different values of the vector space dimensionality, KQN consistently exhibits high prediction accuracy and a strong positive correlation between the distance matrices of the skill vectors.","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":"128793391","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}

引用次数: 35

user2code2vec

Proceedings of the 9th International Conference on Learning Analytics & Knowledge Pub Date : 2019-03-04 DOI: 10.1145/3303772.3303813

David Azcona, Piyush Arora, I-Han Hsiao, Alan F. Smeaton

{"title":"user2code2vec","authors":"David Azcona, Piyush Arora, I-Han Hsiao, Alan F. Smeaton","doi":"10.1145/3303772.3303813","DOIUrl":"https://doi.org/10.1145/3303772.3303813","url":null,"abstract":"In this work, we propose a new methodology to profile individual students of computer science based on their programming design using a technique called embeddings. We investigate different approaches to analyze user source code submissions in the Python language. We compare the performances of different source code vectorization techniques to predict the correctness of a code submission. In addition, we propose a new mechanism to represent students based on their code submissions for a given set of laboratory tasks on a particular course. This way, we can make deeper recommendations for programming solutions and pathways to support student learning and progression in computer programming modules effectively at a Higher Education Institution. Recent work using Deep Learning tends to work better when more and more data is provided. However, in Learning Analytics, the number of students in a course is an unavoidable limit. Thus we cannot simply generate more data as is done in other domains such as FinTech or Social Network Analysis. Our findings indicate there is a need to learn and develop better mechanisms to extract and learn effective data features from students so as to analyze the students' progression and performance effectively.","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":"121073046","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}

引用次数: 4

Integrated Closed-loop Learning Analytics Scheme in a First Year Experience Course 一年级体验课程的综合闭环学习分析方案

Proceedings of the 9th International Conference on Learning Analytics & Knowledge Pub Date : 2019-03-04 DOI: 10.1145/3303772.3303803

Munira Syed, Trunojoyo Anggara, Alison Lanski, Xiaojing Duan, G. Ambrose, N. Chawla

{"title":"Integrated Closed-loop Learning Analytics Scheme in a First Year Experience Course","authors":"Munira Syed, Trunojoyo Anggara, Alison Lanski, Xiaojing Duan, G. Ambrose, N. Chawla","doi":"10.1145/3303772.3303803","DOIUrl":"https://doi.org/10.1145/3303772.3303803","url":null,"abstract":"Identifying non-thriving students and intervening to boost them are two processes that recent literature suggests should be more tightly integrated. We perform this integration over six semesters in a First Year Experience (FYE) course with the aim of boosting student success, by using an integrated closed-loop learning analytics scheme that consists of multiple steps broken into three main phases, as follows: Architecting for Collection (steps: design, build, capture), Analyzing for Action (steps: identify, notify, boost), and Assessing for Improvement (steps: evaluate, report). We close the loop by allowing later steps to inform earlier ones in real-time during a semester and iteratively year to year, thereby improving the course from data-driven insights. This process depends on the purposeful design of an integrated learning environment that facilitates data collection, storage, and analysis. Methods for evaluating the effectiveness of our analytics-based student interventions show that our criterion for identifying non-thriving students was satisfactory and that non-thriving students demonstrated more substantial changes from mid-term to final course grades than already-thriving students. Lastly, we make a case for using early performance in the FYE as an indicator of overall performance and retention of first-year students.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"5 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":"121398420","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}

引用次数: 13

Read Between the Lines: An Annotation Tool for Multimodal Data for Learning 阅读字里行间:用于学习的多模态数据注释工具

Proceedings of the 9th International Conference on Learning Analytics & Knowledge Pub Date : 2019-03-04 DOI: 10.1145/3303772.3303776

D. D. Mitri, J. Schneider, R. Klemke, M. Specht, H. Drachsler

引用次数: 36

Utilizing Learning Analytics to Map Students' Self-Reported Study Strategies to Click Behaviors in STEM Courses 利用学习分析绘制学生自报告学习策略在STEM课程中的点击行为

Proceedings of the 9th International Conference on Learning Analytics & Knowledge Pub Date : 2019-03-04 DOI: 10.1145/3303772.3303841

F. Rodriguez, Renzhe Yu, Jihyun Park, Mariela J. Rivas, M. Warschauer, Brian K. Sato

{"title":"Utilizing Learning Analytics to Map Students' Self-Reported Study Strategies to Click Behaviors in STEM Courses","authors":"F. Rodriguez, Renzhe Yu, Jihyun Park, Mariela J. Rivas, M. Warschauer, Brian K. Sato","doi":"10.1145/3303772.3303841","DOIUrl":"https://doi.org/10.1145/3303772.3303841","url":null,"abstract":"Informed by cognitive theories of learning, this work examined how students' self-reported study patterns (spacing vs. cramming) corresponded to their engagement with the Learning Management System (LMS) across two years in a large biology course. We specifically focused on how students accessed non-mandatory resources (lecture videos, lecture slides) and considered whether this pattern differed by underrepresented minority (URM) status. Overall, students who self-reported utilizing spacing strategies throughout the course had higher grades than students who reported cramming throughout the course. When examining LMS engagement, only a small percentage of students accessed the lecture videos and lecture slides. Applying a negative binomial regression model to daily counts of click activities, we also found that students who utilized spacing strategies accessed LMS resources more often but not earlier before major deadlines. Moreover, this finding was not different for underrepresented students. Our results provide some initial evidence showing how spacing behaviors correspond to accessing learning resources. However, given the lack of general engagement with LMS resources, our results underscore the value of encouraging students to utilize these resources when studying course material.","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":"133833047","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}

引用次数: 13

Modeling gender dynamics in intra and interpersonal interactions during online collaborative learning 在线协作学习中内部和人际互动中的性别动态建模

Proceedings of the 9th International Conference on Learning Analytics & Knowledge Pub Date : 2019-03-04 DOI: 10.1145/3303772.3303837

Yiwen Lin, Nia Dowell, Andrew Godfrey, Heeryung Choi, Christopher A. Brooks

{"title":"Modeling gender dynamics in intra and interpersonal interactions during online collaborative learning","authors":"Yiwen Lin, Nia Dowell, Andrew Godfrey, Heeryung Choi, Christopher A. Brooks","doi":"10.1145/3303772.3303837","DOIUrl":"https://doi.org/10.1145/3303772.3303837","url":null,"abstract":"There has been long-standing stereotypes on men and women's communication styles, such as men using more assertive or aggressive language and women showing more agreeableness and emotions in interactions. In the context of collaborative learning, male learners often believed to be more active participants while female learners are less engaged. To further explore gender differences in learners communication behavior and whether it has changed in the context of online synchronous collaboration, we examined students interactions at a sociocognitive level with a methodology called Group Communication Analysis (GCA). We found that there were no significant differences between men and women in the degree of participation. However, women exhibited significantly higher average social impact, responsivity and internal cohesion compared to men. We also compared the proportion of learners interaction profiles, and results suggest that women are more likely to be effective and cohesive communicators. We discussed implications of these findings for pedagogical practices to promote inclusivity and equity in collaborative learning online.","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":"116357261","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}

引用次数: 17

Fairer but Not Fair Enough On the Equitability of Knowledge Tracing 公平但不够公平——论知识追溯的公平性

Proceedings of the 9th International Conference on Learning Analytics & Knowledge Pub Date : 2019-03-04 DOI: 10.1145/3303772.3303838

Shayan Doroudi, E. Brunskill

{"title":"Fairer but Not Fair Enough On the Equitability of Knowledge Tracing","authors":"Shayan Doroudi, E. Brunskill","doi":"10.1145/3303772.3303838","DOIUrl":"https://doi.org/10.1145/3303772.3303838","url":null,"abstract":"Adaptive educational technologies have the capacity to meet the needs of individual students in theory, but in some cases, the degree of personalization might be less than desired, which could lead to inequitable outcomes for students. In this paper, we use simulations to demonstrate that while knowledge tracing algorithms are substantially more equitable than giving all students the same amount of practice, such algorithms can still be inequitable when they rely on inaccurate models. This can arise as a result of two factors: (1) using student models that are fit to aggregate populations of students, and (2) using student models that make incorrect assumptions about student learning. In particular, we demonstrate that both the Bayesian knowledge tracing algorithm and the N-Consecutive Correct Responses heuristic are susceptible to these concerns, but that knowledge tracing with the additive factor model may be more equitable. The broader message of this paper is that when designing learning analytics algorithms, we need to explicitly consider whether the algorithms act fairly with respect to different populations of students, and if not, how we can make our algorithms more equitable.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"os-47 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":"127785263","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}

引用次数: 41

Would you?: Could you? On a tablet? Analytics of Children's eBook Reading 你会吗?你能吗?在平板电脑上?儿童电子书阅读分析

Proceedings of the 9th International Conference on Learning Analytics & Knowledge Pub Date : 2019-03-04 DOI: 10.1145/3303772.3303833

Beata Beigman Klebanov, Anastassia Loukina, Nitin Madnani, J. Sabatini, J. Lentini

引用次数: 8

Affect Sequences and Learning in Betty's Brain 影响顺序和学习在贝蒂的大脑

Proceedings of the 9th International Conference on Learning Analytics & Knowledge Pub Date : 2019-03-04 DOI: 10.1145/3303772.3303807

J. Andres, Jaclyn L. Ocumpaugh, R. Baker, Stefan Slater, L. Paquette, Yangbo Jiang, Shamya Karumbaiah, Nigel Bosch, Anabil Munshi, Allison L. Moore, Gautam Biswas

引用次数: 27