LAK21: 11th International Learning Analytics and Knowledge Conference最新文献_第5页

What Classroom Audio Tells About Teaching: A Cost-effective Approach for Detection of Teaching Practices Using Spectral Audio Features 课堂音频告诉教学:利用频谱音频特征检测教学实践的一种经济有效的方法

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448152

D. Schlotterbeck, Pablo Uribe, R. Araya, Abelino Jiménez, Daniela Caballero

{"title":"What Classroom Audio Tells About Teaching: A Cost-effective Approach for Detection of Teaching Practices Using Spectral Audio Features","authors":"D. Schlotterbeck, Pablo Uribe, R. Araya, Abelino Jiménez, Daniela Caballero","doi":"10.1145/3448139.3448152","DOIUrl":"https://doi.org/10.1145/3448139.3448152","url":null,"abstract":"Acoustic features and machine learning models have been recently proposed as promising tools to analyze lessons. Furthermore, acoustic patterns, both in the time and spectral domain, have been found to be related to teacher pedagogical practices. Nonetheless, most of previous work relies on expensive or third party equipment, limiting its scalability, and additionally, it is mainly used for diarization. Instead, in this work we present a cost-effective approach to identify teachers’ practices according to three categories (Presenting, Administration, and Guiding) which are compiled from the Classroom Observation Protocol for Undergraduate STEM. Particularly, we record teachers’ lessons using low-cost microphones connected to their smartphones. We then compute the mean and standard deviation of the amplitude, Mel spectrogram, and Mel Frequency Cepstral coefficients of the recordings to train supervised models for the task of predicting three categories compiled from the Classroom Observation Protocol for Undergraduate STEM. We found that spectral features perform better at the task of predicting teachers’ activities along the lessons and that our models can predict the presence of the two most common teaching practices with over 80% of accuracy and good discriminative power. Finally, with these models, we found that using audio obtained from the teachers’ smartphones it is also possible to automatically discriminate between sessions where students are using or not an online platform. This approach is important for teachers and other stakeholders who could use an automatic and cost-effective tool for analyzing teaching practices.","PeriodicalId":108600,"journal":{"name":"LAK21: 11th International Learning Analytics and Knowledge Conference","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133648822","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

Linguistic Skill Modeling for Second Language Acquisition 第二语言习得的语言技能建模

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448153

Brian Zylich, Andrew S. Lan

{"title":"Linguistic Skill Modeling for Second Language Acquisition","authors":"Brian Zylich, Andrew S. Lan","doi":"10.1145/3448139.3448153","DOIUrl":"https://doi.org/10.1145/3448139.3448153","url":null,"abstract":"To adapt materials for an individual learner, intelligent tutoring systems must estimate their knowledge or abilities. Depending on the content taught by the tutor, there have historically been different approaches to student modeling. Unlike common skill-based models used by math and science tutors, second language acquisition (SLA) tutors use memory-based models since there are many tasks involving memorization and retrieval, such as learning the meaning of a word in a second language. Based on estimated memory strengths provided by these memory-based models, SLA tutors are able to identify the optimal timing and content of retrieval practices for each learner to improve retention. In this work, we seek to determine whether skill-based models can be combined with memory-based models to improve student modeling and especially retrieval practice performance for SLA. In order to define skills in the context of SLA, we develop methods that can automatically extract multiple types of linguistic features from words. Using these features as skills, we apply skill-based models to a real-world SLA dataset. Our main findings are as follows. First, incorporating lexical features to represent individual words as skills in skill-based models outperforms existing memory-based models in terms of recall probability prediction. Second, incorporating additional morphological and syntactic features of each word via multiple-skill tagging of each word further improves the skill-based models. Third, incorporating semantic features, like word embeddings, to model similarities between words in a learner’s practice history and their effects on memory also improves the models and appears to be a promising direction for future research.","PeriodicalId":108600,"journal":{"name":"LAK21: 11th International Learning Analytics and Knowledge Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128633026","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: 5

An Exact Algorithm for Group Formation to Promote Collaborative Learning 一种促进协作学习的精确分组算法

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448196

Zhiru Sun, Marco Chiarandini

{"title":"An Exact Algorithm for Group Formation to Promote Collaborative Learning","authors":"Zhiru Sun, Marco Chiarandini","doi":"10.1145/3448139.3448196","DOIUrl":"https://doi.org/10.1145/3448139.3448196","url":null,"abstract":"Collaborative learning has been widely used to foster students’ communication and joint knowledge construction. However, the classification of learners into well-structured groups is one of the most challenging tasks in the field. The aim of this study is to propose a novel method to form intra-heterogeneous and inter-homogeneous groups based on relevant student characteristics. Such a method allows for the consideration of multiple student characteristics and can handle both numerical and categorical characteristic types simultaneously. It assumes that the teacher provides an order of importance of the characteristics, then it solves the grouping problem as a lexicographic optimization problem in the given order. We formulate the problem in mixed integer linear programming (MILP) terms and solve it to optimality. A pilot experiment was conducted with 29 college freshmen considering three general characteristics (i.e., 13 specific features) including knowledge level, demographic information, and motivation. Results of such an experiment demonstrate the validity and computational feasibility of the algorithmic approach. Large-scale studies are needed to assess the impact of the proposed grouping method on students’ learning experience and academic achievement.","PeriodicalId":108600,"journal":{"name":"LAK21: 11th International Learning Analytics and Knowledge Conference","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131788432","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: 2

A network analytic approach to integrating multiple quality measures for asynchronous online discussions 一种集成异步在线讨论多种质量度量的网络分析方法

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448163

Elaine Farrow, Johanna D. Moore, D. Gašević

{"title":"A network analytic approach to integrating multiple quality measures for asynchronous online discussions","authors":"Elaine Farrow, Johanna D. Moore, D. Gašević","doi":"10.1145/3448139.3448163","DOIUrl":"https://doi.org/10.1145/3448139.3448163","url":null,"abstract":"Asynchronous online discussions within a community of learners can improve learning outcomes through social knowledge construction, but the depth and quality of student contributions often varies widely. Approaches to assessing critical discourse typically use content analysis to identify indicators that correspond to framework constructs, that in turn serve as measures of depth and quality. Often only a single construct is addressed for performing content analysis in the literature, although recent work has used both social presence and cognitive presence constructs from the Community of Inquiry (CoI) framework. Nevertheless, there is no effective, commonly used, analytic approach to combining insights from multiple perspectives about quality and depth of online discussions. This paper addresses the gap by proposing the combined use of cognitive engagement (the ICAP framework) and cognitive presence (CoI); and by proposing a network analytic approach that quantifies the associations between the two frameworks and measures the moderation effects of two instructional interventions on those associations. The present study found that these associations were moderated by one intervention but not the other; and that messages labelled with the most common phase of cognitive presence could be usefully assigned to smaller meaningful subgroups by also considering the mode of cognitive engagement.","PeriodicalId":108600,"journal":{"name":"LAK21: 11th International Learning Analytics and Knowledge Conference","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133328290","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: 7

Using Marginal Models to Adjust for Statistical Bias in the Analysis of State Transitions 用边际模型调整状态转换分析中的统计偏差

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448182

Jeffrey Matayoshi, Shamya Karumbaiah

{"title":"Using Marginal Models to Adjust for Statistical Bias in the Analysis of State Transitions","authors":"Jeffrey Matayoshi, Shamya Karumbaiah","doi":"10.1145/3448139.3448182","DOIUrl":"https://doi.org/10.1145/3448139.3448182","url":null,"abstract":"Many areas of educational research require the analysis of data that have an inherent sequential or temporal ordering. In certain cases, researchers are specifically interested in the transitions between different states—or events—in these sequences, with the goal being to understand the significance of these transitions; one notable example is the study of affect dynamics, which aims to identify important transitions between affective states. Unfortunately, a recent study has revealed a statistical bias with several metrics used to measure and compare these transitions, possibly causing these metrics to return unexpected and inflated values. This issue then causes extra difficulties when interpreting the results of these transition metrics. Building on this previous work, in this study we look in more detail at the specific mechanisms that are responsible for the bias with these metrics. After giving a theoretical explanation for the issue, we present an alternative procedure that attempts to address the problem with the use of marginal models. We then analyze the effectiveness of this procedure, both by running simulations and by applying it to actual student data. The results indicate that the marginal model procedure seemingly compensates for the bias observed in other transition metrics, thus resulting in more accurate estimates of the significance of transitions between states.","PeriodicalId":108600,"journal":{"name":"LAK21: 11th International Learning Analytics and Knowledge Conference","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128656122","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

Multimodal Data Collection Made Easy: The EZ-MMLA Toolkit: A data collection website that provides educators and researchers with easy access to multimodal data streams. 多模式数据收集变得容易:EZ-MMLA工具包:一个数据收集网站，为教育工作者和研究人员提供了轻松访问多模式数据流的途径。

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448201

Javaria Hassan, Jovin Leong, Bertrand Schneider

{"title":"Multimodal Data Collection Made Easy: The EZ-MMLA Toolkit: A data collection website that provides educators and researchers with easy access to multimodal data streams.","authors":"Javaria Hassan, Jovin Leong, Bertrand Schneider","doi":"10.1145/3448139.3448201","DOIUrl":"https://doi.org/10.1145/3448139.3448201","url":null,"abstract":"While Multimodal Learning Analytics (MMLA) is becoming a popular methodology in the LAK community, most educational researchers still rely on traditional instruments for capturing learning processes (e.g., click-stream, log data, self-reports, qualitative observations). MMLA has the potential to complement and enrich traditional measures of learning by providing high frequency data on learners’ behavior, cognition and affects. However, there is currently no easy-to-use toolkit for recording multimodal data streams. Existing methodologies rely on the use of physical sensors and custom-written code for accessing sensor data. In this paper, we present the EZ-MMLA toolkit. This toolkit was implemented as a website that provides easy access to the latest machine learning algorithms for collecting a variety of data streams from webcams: attention (eye-tracking), physiological states (heart rate), body posture (skeletal data), hand gestures, emotions (from facial expressions and speech), and lower-level computer vision algorithms (e.g., fiducial / color tracking). This toolkit can run from any browser and does not require special hardware or programming experience. We compare this toolkit with traditional methods and describe a case study where the EZ-MMLA toolkit was used in a classroom context. We conclude by discussing other applications of this toolkit, potential limitations, and future steps.","PeriodicalId":108600,"journal":{"name":"LAK21: 11th International Learning Analytics and Knowledge Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129921140","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: 12

Automatic Student Writing Evaluation: Investigating the Impact of Individual Differences on Source-Based Writing 学生写作自动评估:调查个体差异对基于源的写作的影响

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448207

Püren Öncel, Lauren E. Flynn, A. Sonia, K. Barker, Grace C. Lindsay, Caleb M. McClure, D. McNamara, L. Allen

{"title":"Automatic Student Writing Evaluation: Investigating the Impact of Individual Differences on Source-Based Writing","authors":"Püren Öncel, Lauren E. Flynn, A. Sonia, K. Barker, Grace C. Lindsay, Caleb M. McClure, D. McNamara, L. Allen","doi":"10.1145/3448139.3448207","DOIUrl":"https://doi.org/10.1145/3448139.3448207","url":null,"abstract":"Automated Writing Evaluation systems have been developed to help students improve their writing skills through the automated delivery of both summative and formative feedback. These systems have demonstrated strong potential in a variety of educational contexts; however, they remain limited in their personalization and scope. The purpose of the current study was to begin to address this gap by examining whether individual differences could be modeled in a source-based writing context. Undergraduate students (n=106) wrote essays in response to multiple sources and then completed an assessment of their vocabulary knowledge. Natural language processing tools were used to characterize the linguistic properties of the source-based essays at four levels: descriptive, lexical, syntax, and cohesion. Finally, machine learning models were used to predict students’ vocabulary scores from these linguistic features. The models accounted for approximately 29% of the variance in vocabulary scores, suggesting that the linguistic features of source-based essays are reflective of individual differences in vocabulary knowledge. Overall, this work suggests that automated text analyses can help to understand the role of individual differences in the writing process, which may ultimately help to improve personalization in computer-based learning environments.","PeriodicalId":108600,"journal":{"name":"LAK21: 11th International Learning Analytics and Knowledge Conference","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131722766","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

Undergraduate Grade Prediction in Chinese Higher Education Using Convolutional Neural Networks 基于卷积神经网络的中国高等教育本科成绩预测

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448184

Yupei Zhang, Rui An, Jiaqi Cui, Xuequn Shang

{"title":"Undergraduate Grade Prediction in Chinese Higher Education Using Convolutional Neural Networks","authors":"Yupei Zhang, Rui An, Jiaqi Cui, Xuequn Shang","doi":"10.1145/3448139.3448184","DOIUrl":"https://doi.org/10.1145/3448139.3448184","url":null,"abstract":"Prediction of undergraduate grades before their course enrollments is beneficial to the student’s learning plan on selective courses and failure warnings to compulsory courses in Chinese higher education. This study proposed to use a deep learning-based model composed of sparse attention layers, convolutional neural layers, and a fully connected layer, called Sparse Attention Convolutional Neural Networks (SACNN), to predict undergraduate grades. Concretely, sparse attention layers response to the fact that courses have different contributions to the grade prediction of the target course; convolutional neural layers aim to capture the one-dimensional temporal feature on these courses organized in terms; the fully connected layer is to complete the final classification based on achieved features. We collected a dataset including grade records, student’s demographics and course descriptions from our institution in the past five years. The dataset contained about 54k grade records from 1307 students and 137 courses, where all mentioned methods were evaluated by the hold-out evaluation. The result shows SACNN achieves 81% prediction precision and 85% accuracy on the failure prediction, which is more effective than those compared methods. Besides, SACNN delivers a potential explanation to the reason of the predicted result, thanks to the sparse attention layer. This study provides a useful technique for personalized learning and course relationship discovery in undergraduate education.","PeriodicalId":108600,"journal":{"name":"LAK21: 11th International Learning Analytics and Knowledge Conference","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133675117","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: 11

Facilitators First: Building a Tool With Facilitators to Foster a More Collaborative Makerspace Community Through Movement Traces 促进者优先:与促进者一起构建工具，通过运动轨迹培养更具协作性的创客空间社区

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448194

Léonore V. Guillain, Bertrand Schneider

{"title":"Facilitators First: Building a Tool With Facilitators to Foster a More Collaborative Makerspace Community Through Movement Traces","authors":"Léonore V. Guillain, Bertrand Schneider","doi":"10.1145/3448139.3448194","DOIUrl":"https://doi.org/10.1145/3448139.3448194","url":null,"abstract":"Research indicates that makerspaces equip students with the practical skills needed to build their own projects and thrive in the twenty-first-century workforce. While the appeal of makerspaces lies in their spirit of tinkering and community-driven ethos, these same attributes make it difficult to monitor and facilitate learning. Makerspaces also attract students from diverse backgrounds and skills, further challenging facilitators to accommodate the needs of each student and their self-directed projects. We propose a dashboard interface that visualizes Kinect sensor data to aid facilitators in monitoring student collaboration. The tool was designed with an iterative and participatory approach. Five facilitators were involved at each phase of the design process, from need-finding to prototyping to implementation and evaluation. Insights derived from interviews were used to inform the design decisions of the final interface. The final evaluation suggests that the use of normalized summary scores and an interactive network graph can successfully support facilitators in tasks related to improving collaboration. Moreover, the use of a red-green color scheme and the inclusion of student photos improved the usability for facilitators, but issues of trustworthiness need to be further examined.","PeriodicalId":108600,"journal":{"name":"LAK21: 11th International Learning Analytics and Knowledge Conference","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134435649","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: 2

Beyond First Encounters with Analytics: Questions, Techniques and Challenges in Instructors’ Sensemaking 超越与分析的初次接触:教师意义建构中的问题、技术和挑战

LAK21: 11th International Learning Analytics and Knowledge Conference Pub Date : 2021-04-12 DOI: 10.1145/3448139.3448172

Qiujie Li, Yeonji Jung, A. Wise

{"title":"Beyond First Encounters with Analytics: Questions, Techniques and Challenges in Instructors’ Sensemaking","authors":"Qiujie Li, Yeonji Jung, A. Wise","doi":"10.1145/3448139.3448172","DOIUrl":"https://doi.org/10.1145/3448139.3448172","url":null,"abstract":"Despite growing implementation of teacher-facing analytics in higher education, relatively little is known about the detailed processes through which instructors make sense of analytics in their teaching practices beyond their initial encounters with tools. This study unpacked the sensemaking process of thirteen instructors with analytic experience, using interviews that included walkthroughs of their analytics use. Qualitative inductive analysis was used to identify themes related to (1) the questions they asked of the analytics, (2) the techniques they used to interpret them, and (3) the challenges they encountered. Findings indicated that instructors went beyond a general curiosity to develop three types of questions of the analytics (goal-oriented, problem-oriented, and instruction modification questions). Instructors also used specific techniques to read and explain data by (a) developing expectations about the answers the analytics would provide, and (b) making comparisons to reveal student diversity, identify effects of instructional revision and diagnose issues. The study found instructors faced an initial learning curve when seeking and making use of relevant information, but also continued to revisit these challenges when they were not able to develop a routine of analytics use. These findings both contribute to a conceptual understanding of instructor analytic sensemaking and have practical implications for its systematic support.","PeriodicalId":108600,"journal":{"name":"LAK21: 11th International Learning Analytics and Knowledge Conference","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114278384","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