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Investigating the Impact of the Mindset of the Learners on their Behaviour in a Computer-Based learning Environment 24th

Investigating the Impact of the Mindset of the Learners on T...

引用

24th international conference on Artificial Intelligence in Education (AIED)

作者： Nishane, Indrayani Rajendran, Ramkumar Iyer, Sridhar Indian Inst Technol Mumbai India

ISBN: (纸本)9783031363351;9783031363368

Computer-based learning environments (CBLEs) are often used to provide customized learning experiences for students. To enhance their effectiveness, we propose analyzing learners' actions within CBLEs. In this study, we focus on the influence of learner mindset (fixed vs growth) on interaction patterns in a CBLE designed for teaching Python programming. Learner mindset refers to their beliefs about the malleability of their abilities. Individuals with a fixed mindset believe that their abilities are fixed, while those with a growth mindset believe abilities can be developed through learning. Using log data and pattern-mining techniques, we will identify learners' interaction patterns and behaviour while also assessing their mindset through a questionnaire. We will compare the interaction patterns of fixed and growth mindset learners using task models to support our findings.

关键词： Learner Mindset Learner Behaviour Process Modelling for learners

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"Why My Essay Received a 4?": A Natural Language Processing Based Argumentative Essay Structure Analysis 24th

"Why My Essay Received a 4?": A Natural Language Processing ...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Yang, Bokai Nam, Sungjin Huang, Yuchi Univ Wisconsin Eau Claire WI 54701 USA ACT Iowa City IA 52243 USA

ISBN: (纸本)9783031362712;9783031362729

Writing argumentative essays is a critical component of students' learning. Previous works on automatic assessments on essay writing often focused on providing a holistic score for the input essay, which only summarized the essay's overall quality. However, to provide more pedagogical value and equitable educational opportunities for all students, an automatized system needs to provide detailed feedback on students' essays. To address this issue, we developed an essay argumentative structure feedback system to support educators and students. We employed natural language processing (NLP) and data mining techniques to explore the association between argumentative structure and essay scores. First, we proposed a cross-prompt, sentence-level ensemble model to classify the argumentative elements and extract the argumentative structures from the essay. the model worked across multiple datasets and achieved high performance. Second, after applying the classification model on the ACT writing tests, we performed a sequential mining process to extract representative argumentative structures. Our findings highlight the role of organizational argumentative structure in essay scoring. Furthermore, we found a common argumentative structure used by the high-scored essays. Finally, with the knowledge of argumentative elements and structures used in the previous essays, we proposed a feedback tool design to complement the current AES systems and help students improve their argument writing skill.

关键词： Natural language processing Educational data mining Argument mining Teaching analytics automated feedback

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Consistency of Inquiry Strategies Across Subsequent Activities in Different Domains 24th

Consistency of Inquiry Strategies Across Subsequent Activiti...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Cock, Jade Mai Roll, Ido Kaser, Tanja Ecole Polytech Fed Lausanne Lausanne Switzerland Technion Haifa Israel

ISBN: (纸本)9783031363351;9783031363368

Interactive simulations encourage students to practice skills essential to understanding and learning sciences. Alas, inquiry learning with interactive simulations is challenging. In this paper, we seek to identify inquiry patterns across topics and evaluate their stability with regard to common behaviors and student membership. Applying a clustering approach, we propose an encoding through which we can model students' strategies in diverse environments. Specifically, we encode each sequence with three different levels of granularity which range from simulation-specific characteristics to simulation-agnostic features. Using this generalizable encoding, we find two clusters for each of two simulations. the formed groups exhibit similar learning patterns across environments. One systematically cycles through exploring and recording systematically over all variables. the other group explores the simulation more freely. this suggests that our feature encoding captures inherent quality of inquiry with simulations and can be used to characterize learners knowledge of productive exploration.

关键词： interactive simulation inquiry skills spectral clustering inquiry strategies log data open ended learning environments

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User Adaptive Language learning Chatbots with a Curriculum 24th

User Adaptive Language Learning Chatbots with a Curriculum

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Qian, Kun Shea, Ryan Li, Yu Fryer, Luke Kutszik Yu, Zhou Columbia Univ New York NY 10032 USA Univ Hong Kong Hong Kong Peoples R China Articulate AI Inc New York NY USA

ISBN: (纸本)9783031363351;9783031363368

Along with the development of systems for natural language understanding and generation, dialog systems have been widely adopted for language learning and practicing. Many current educational dialog systems perform chitchat, where the generated content and vocabulary are not constrained. However, for learners in a school setting, practice through dialog is more effective if it aligns with students' curriculum and focuses on textbook vocabulary. therefore, we adapt lexically constrained decoding to a dialog system, which urges the dialog system to include curriculum-aligned words and phrases in its generated utterances. We adopt a generative dialog system, BlenderBot3, as our backbone model and evaluate our curriculum-based dialog system with middle school students learning English as their second language. the constrained words and phrases are derived from their textbooks, suggested by their English teachers. the evaluation result demonstrates that the dialog system with curriculum infusion improves students' understanding of target words and increases their interest in practicing English.

关键词： Lexically constrained decoding Generative dialog system User adaptation

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Preserving Privacy of Face and Facial Expression in Computer Vision data Collected in learning Environments 24th

Preserving Privacy of Face and Facial Expression in Computer...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Ashwin, T. S. Rajendran, Ramkumar Indian Inst Technol Mumbai India

ISBN: (纸本)9783031363351;9783031363368

Learners' affective states are analyzed using their facial expressions obtained from image/video data. Deep learning architectures are the state-of-the-art affective state recognition methods used in the literature. Vast volumes of these data are used in the training, testing, and validation process. Now, sharing these data publicly for research purposes is a challenging task as it has privacy issues. the importance of creating a successful face de-identification algorithm for privacy protection cannot be overstated because the face is the single biometric feature that discloses the most recognizable qualities of a person in an image or a video frame. Existing approaches to face de-identification with facial expressions are not explored in the education domain. In this study, we design a methodology for a face de-identification technique that automatically creates a new face while maintaining the emotion and non-biometric facial characteristics of a target face from an input facial image. We consider a proxy set, which consists of a sizable number of synthetic faces produced by StyleGAN, and choose the proxy set face that most closely resembles the target face in terms of facial expression and position. Since the faces in the proxy settings were formed artificially, the face chosen from this collection is absolutely anonymous. We created a dataset of 10 students to test the methodology. the performance of StyleGAN was measured for standard parameters such as gender, emotion, age, etc., and the results show that the generated face preserved emotional attributes with a de-identified face.

关键词： Privacy protection Face de-identification Facial Expressions StyleGAN

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Enhancing the Automatic Identification of Common Math Misconceptions Using Natural Language Processing 24th

Enhancing the Automatic Identification of Common Math Miscon...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Gorgun, Guher Botelho, Anthony F. Univ Alberta Edmonton AB T6G 2G5 Canada Univ Florida Gainesville FL 32611 USA

ISBN: (纸本)9783031363351;9783031363368

In order to facilitate student learning, it is important to identify and remediate misconceptions and incomplete knowledge pertaining to the assigned material. In the domain of mathematics, prior research with computer-based learning systems has utilized the commonality of incorrect answers to problems as a way of identifying potential misconceptions among students. Much of this research, however, has been limited to the use of close-ended questions, such as multiple-choice and fill-in-the-blank problems. In this study, we explore the potential usage of natural language processing and clustering methods to examine potential misconceptions across student answers to both close- and open-ended problems. We find that our proposed methods show promise for distinguishing misconception from non-conception, but may need further development to improve the interpretability of specific misunderstandings exhibited through student explanations.

关键词： misconceptions sentence-BERT intelligent tutoring system natural language processing

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Leave No One Behind - A Massive Online learning Platform Free for Everyone 24th

Leave No One Behind - A Massive Online Learning Platform Fre...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Giraldo, Alejandra Holguin Gutierrez, Andrea Lozano Leyton, Gustavo Alvarez Camilo Sanguino, Juan Manrique, Ruben GCFGlobalLearning LLC Durham NC USA Univ Los Andes Bogota Colombia

ISBN: (纸本)9783031363351;9783031363368

GCFGloballearning is a non-profit organization committed to creating life-changing opportunities through an innovative virtual education project that incorporates technological tools. Our platform offers free courses to learners worldwide, available in English, Spanish, and Portuguese. Since our launch, we have welcomed millions of learners. Our current focus is on incorporating artificial intelligence tools that will enhance our learners' experience. To achieve this, we have developed a recommender system and a learning content search and organization tool that personalizes our learners' learning journey. Even with limited information about our learners, we can enhance their experience through the use of these AI tools. In this paper, we introduce the platform's primary components, detail how we overcome our limited learner information scenario, and share our vision of incorporating more artificial intelligence innovations in the future.

关键词： Recommender systems MOOC platform Limited learner information scenarios

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Amortised Design Optimization for Item Response theory 24th

Amortised Design Optimization for Item Response Theory

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Keurulainen, Antti Westerlund, Isak Keurulainen, Oskar Howes, Andrew Aalto Univ Espoo Finland Bitville Oy Espoo Finland Univ Birmingham Birmingham England

ISBN: (纸本)9783031363351;9783031363368

Item Response theory (IRT) is a well known method for assessing responses from humans in education and psychology. In education, IRT is used to infer student abilities and characteristics of test items from student responses. Interactions with students are expensive, calling for methods that efficiently gather information for inferring student abilities. Methods based on Optimal Experimental Design (OED) are computationally costly, making them inapplicable for interactive applications. In response, we propose incorporating amortised experimental design into IRT. Here, the computational cost is shifted to a precomputing phase by training a Deep Reinforcement learning (DRL) agent with synthetic data. the agent is trained to select optimally informative test items for the distribution of students, and to conduct amortised inference conditioned on the experiment outcomes. During deployment the agent estimates parameters from data, and suggests the next test item for the student, in close to real-time, by taking into account the history of experiments and outcomes.

关键词： Item Response theory (IRT) Experimental Design Deep Reinforcement learning (DRL)

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DancÆR: Efficient and Accurate Dance Choreography learning by Feedback through Pose Classification 24th

DancÆR: Efficient and Accurate Dance Choreography Learning ...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Bas, Iremsu Alp, Demir Ergenc, Lara Ceren Kocak, Andy Emre Yalcin, Sedat Hisar Sch Comp Sci IdeaLab Istanbul Turkiye

ISBN: (纸本)9783031363351;9783031363368

Educational philosophies have slowly focused on differentiated and self-learning systems that respectively emphasize tailoring instruction to meet individual needs and gathering, processing, and retaining knowledge without the help of another person in recent years. In this regard, creating opportunities to further self-learning resources has become increasingly important. Some people started to prefer these over traditional learning practices for various reasons, such as the difficulty of transportation in metropolises or fitting their timetables to in-person lessons. the creation of such platforms or opportunities for physical education, however, proves to be more difficult as individuals require continuous and precise feedback regarding the usage of their bodies. Accordingly, we have developed an augmented reality application that presents a platform for dance that focuses on differentiated and self-learning principles with accurate feedback. We built the augmented reality (AR) app prototype using the Swift programming language and used the MoveNet pose detection model along with our own neural network to capture the body position. Our proposal could prove a valuable addition to learning physical activities assisted by AI systems since the application of AI technologies to dance and physical education could be improved by further investigation and research.

关键词： Machine learning AI-Assisted Tutoring Motivational Diagnosis and Feedback AR

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Learner Perception of Pedagogical Agents 24th

Learner Perception of Pedagogical Agents

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Beukman, Marei Chen, Xiaobin Univ Tubingen Tubingen Germany

ISBN: (纸本)9783031363351;9783031363368

Hyper-realistic human video generation (also referred to as "deepfake") is a recent development in artificial intelligence that may be applied to create pedagogical agents (PAs) for education purposes. Traditionally, PA research has been using 2D or 3D virtual figures to examine how their design may affect student learning or perceptions. In this study, we employed the latest human video generation technology to create PAs and investigated how students' perceived stereotypes of competent teachers would affect their preferences of a PA. It was found that students prefer to learn Japanese with Asian looking PAs over Caucasian or Black agents, supporting the hypothesis that real-world stereotype ideas can persist in the virtual world. Findings of the study offer references for how to better design PAs with generated human videos to boost learning motivation and enhance student perceptions.

关键词： pedagogical agents AI avatars learner perception stereotypes language learning

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