An Auto-Grading Oriented Approach for Off-Line Handwritten Organic Cyclic Compound Structure Formulas Recognition

Auto-grading,as an instruction tool,could reduce teachers’workload,provide students with instant feedback and support highly personalized learning.Therefore,this topic attracts considerable attentions from researchers recently.To realize the automatic grading of handwritten chemistry assignments,the problem of chemical notations recognition should be solved first.The recent handwritten chemical notations recognition solutions belonging to the end-to-end trainable category suffered fromthe problem of lacking the accurate alignment information between the input and output.They serve the aim of reading notations into electrical devices to better prepare relevant edocuments instead of auto-grading handwritten assignments.To tackle this limitation to enable the auto-grading of handwritten chemistry assignments at a fine-grained level.In this work,we propose a component-detectionbased approach for recognizing off-line handwritten Organic Cyclic Compound Structure Formulas(OCCSFs).Specifically,we define different components of OCCSFs as objects(including graphical objects and text objects),and adopt the deep learning detector to detect them.Then,regarding the detected text objects,we introduce an improved attention-based encoder-decoder model for text recognition.Finally,with these detection results and the geometric relationships of detected objects,this article designs a holistic algorithm for interpreting the spatial structure of handwritten OCCSFs.The proposedmethod is evaluated on a self-collected data set consisting of 3000 samples and achieves promising results.

Solving Arithmetic Word Problems of Entailing Deep Implicit Relations by Qualia Syntax-Semantic Model

Solving arithmetic word problems that entail deep implicit relations is still a challenging problem.However,significant progress has been made in solving Arithmetic Word Problems(AWP)over the past six decades.This paper proposes to discover deep implicit relations by qualia inference to solve Arithmetic Word Problems entailing Deep Implicit Relations(DIR-AWP),such as entailing commonsense or subject-domain knowledge involved in the problem-solving process.This paper proposes to take three steps to solve DIR-AWPs,in which the first three steps are used to conduct the qualia inference process.The first step uses the prepared set of qualia-quantity models to identify qualia scenes from the explicit relations extracted by the Syntax-Semantic(S2)method from the given problem.The second step adds missing entities and deep implicit relations in order using the identified qualia scenes and the qualia-quantity models,respectively.The third step distills the relations for solving the given problem by pruning the spare branches of the qualia dependency graph of all the acquired relations.The research contributes to the field by presenting a comprehensive approach combining explicit and implicit knowledge to enhance reasoning abilities.The experimental results on Math23K demonstrate hat the proposed algorithm is superior to the baseline algorithms in solving AWPs requiring deep implicit relations.

Study on the demand for public health professional after the COVID-19 epidemic in Zhejiang Province, China: A survey based on human-machine collaboration

To understand the needs of public health institutions in Zhejiang Province,China for public health personnel,and provide basis for training public health personnel.Methods:512 public health institutions in Zhejiang Province were randomly selected from different levels and regions,and the number of ublic health professional and the demand for professional ability were investigated by questionnaire.Results:The preventive medicine personnel in public health institutions in Zhejiang Province are insufficient;There is a certain disjunction or dislocation between the abilities and needs of public health professional;The way of continuing education for public health professional is single and the opportunities are few.Conclusion:Zhejiang Province should appropriately expand the enrollment of preventive medicine majors,especially high-level preventive medicine talents,deepen the education and teaching reform of preventive medicine majors,and strengthen the continuing education and training of public health professional to meet the needs of public health services after the COVID-19 epidemic.

Solving Algebraic Problems with Geometry Diagrams Using Syntax-Semantics Diagram Understanding

Solving Algebraic Problems with Geometry Diagrams(APGDs)poses a significant challenge in artificial intelligence due to the complex and diverse geometric relations among geometric objects.Problems typically involve both textual descriptions and geometry diagrams,requiring a joint understanding of these modalities.Although considerable progress has been made in solving math word problems,research on solving APGDs still cannot discover implicit geometry knowledge for solving APGDs,which limits their ability to effectively solve problems.In this study,a systematic and modular three-phase scheme is proposed to design an algorithm for solving APGDs that involve textual and diagrammatic information.The three-phase scheme begins with the application of the statetransformer paradigm,modeling the problem-solving process and effectively representing the intermediate states and transformations during the process.Next,a generalized APGD-solving approach is introduced to effectively extract geometric knowledge from the problem’s textual descriptions and diagrams.Finally,a specific algorithm is designed focusing on diagram understanding,which utilizes the vectorized syntax-semantics model to extract basic geometric relations from the diagram.A method for generating derived relations,which are essential for solving APGDs,is also introduced.Experiments on real-world datasets,including geometry calculation problems and shaded area problems,demonstrate that the proposed diagram understanding method significantly improves problem-solving accuracy compared to methods relying solely on simple diagram parsing.

Qualia Role-Based Quantity Relation Extraction for Solving Algebra Story Problems

A qualia role-based entity-dependency graph(EDG)is proposed to represent and extract quantity relations for solving algebra story problems stated in Chinese.Traditional neural solvers use end-to-end models to translate problem texts into math expressions,which lack quantity relation acquisition in sophisticated scenarios.To address the problem,the proposed method leverages EDG to represent quantity relations hidden in qualia roles of math objects.Algorithms were designed for EDG generation and quantity relation extraction for solving algebra story problems.Experimental result shows that the proposedmethod achieved an average accuracy of 82.2%on quantity relation extraction compared to 74.5%of baseline method.Another prompt learning result shows a 5%increase obtained in problem solving by injecting the extracted quantity relations into the baseline neural solvers.

Noise-Filtering Enhanced Deep Cognitive Diagnosis Model for LatentSkill Discovering

Educational data mining based on student cognitive diagnosis analysis can provide an important decision basis for personalized learning tutoring of students,which has attracted extensive attention from scholars at home and abroad and has made a series of important research progress.To this end,we propose a noise-filtering enhanced deep cognitive diagno-sis method to improve the fitting ability of traditional models and obtain students’skill mastery status by mining the interaction between students and problems nonlinearly through neural networks.First,modeling complex interactions between students and problems with multidimensional features based on cognitive processing theory can enhance the interpretability of the proposed model;second,the neural network is used to predict students’learning performance,diagnose students’skill mastery and provide immediate feedback;finally,by comparing the proposed model with several baseline models,extensive experimental results on real data sets demonstrate that the proposed Finally,by comparing the proposed model with several baseline models,the extensive experimental results on the actual data set demon-strate that the proposed model not only improves the accuracy of predicting students’learning performance but also enhances the interpretability of the neurocognitive diagnostic model.

Exercise Recommendation with Preferences and Expectations Based on Ability Computation

In the era of artificial intelligence,cognitive computing,based on cognitive science;and supported by machine learning and big data,brings personalization into every corner of our social life.Recommendation systems are essential applications of cognitive computing in educational scenarios.They help learners personalize their learning better by computing student and exercise characteristics using data generated from relevant learning progress.The paper introduces a Learning and Forgetting Convolutional Knowledge Tracking Exercise Recommendation model(LFCKT-ER).First,the model computes students’ability to understand each knowledge concept,and the learning progress of each knowledge concept,and the model consider their forgetting behavior during learning progress.Then,students’learning stage preferences are combined with filtering the exercises that meet their learning progress and preferences.Then students’ability is used to evaluate whether their expectations of the difficulty of the exercises are reasonable.Then,the model filters the exercises that best match students’expectations again by students’expectations.Finally,we use a simulated annealing optimization algorithm to assemble a set of exercises with the highest diversity.From the experimental results,the LFCKT-ER model can better meet students’personalized learning needs and is more accurate than other exercise recommendation systems under various metrics on real online education public datasets.

Research on the Application Framework of Intelligent Technologies to Promote Teachers’Classroom Teaching Behavior Evaluation

With the advantages of real-time analysis and visual evaluation results,intelligent technology-enabled teaching behavior evaluation has gradually become a powerful means to help teachers adjust teaching behaviors and improve teaching quality.However,at present,the evaluation of intelligent teachers’behaviors is still in the preliminary exploration stage,and the application research is not deep enough.This paper analyzes the application of intelligent technology in the evaluation of teachers’classroom teaching behaviors from the perspectives of evaluation data,methods,and results.Voice print recognition technology is used to recognize the teachers’identities and track the speech in the classroom videos,and the videos are segmented.Then,the evaluation framework of teachers’classroom teaching behaviors is constructed using three dimensions of emotion,posture,and position preference.Finally,evaluation results are presented to teachers in a more intuitive and easy-to-understand visual way,to help teachers reflect on teaching.This paper aims to promote the transformation of teachers’classroom teaching behavior evaluation toward an intelligent,efficient,and sustainable direction through current research.

Applying project‐based learning in artificial intelligence and marine discipline:An evaluation study on a robotic sailboat platform

intelligence is penetrating various fields.The demand for interdisciplinary talent is increasingly important,while interdisciplinary educational activities for high school students are lagging behind.Project‐based learning(PBL)in artificial intelligence(AI)and robotic education activities supported by a robotic sailboat platform,the sailboat test arena(STAr),has been shown to popularise AI and robotic knowledge in young students.In the implementation of the programme,PBL was provided for students,and gamification pedagogy was applied to increase participants'learning motivation and engagement.The results show that the proposed STAr‐based programme is capable of delivering the desired knowledge and skills to students at high school levels.The assessment results suggest that most students achieve learning outcomes on average.Students showed more interest in AI and marine disciplines and were willing to participate in more such educational programs.The findings fill the research gap that few existing education platforms have facilitated the teaching and learning of AI and marine disciplines for high school students.

Correction:Noise-Filtering Enhanced Deep Cognitive Diagnosis Model for Latent Skill Discovering

In the article“Noise-Filtering Enhanced Deep Cognitive Diagnosis Model for Latent Skill Discovering”by Jing Geng,Huali Yang and Shengze Hu(Intelligent Automation&Soft Computing,2023,Vol.37,No.2,pp.1311-1324.doi:10.32604/iasc.2023.038481),the References[1-2],[4-12],and[23-29]were not appropriately aligned with the context of the main text.

Effects of In-video Questions and Feedback on Learning Performance

The eye-tracking technology was used in this study to investigate the effects of embedded questions and feedback in instructional videos on learning performance and attention allocation and whether an expertise reversal effect existed.The experiment involved 49 learners with high-level prior knowledge and 45 ones with low-level prior knowledge from a university.Meanwhile,they learned instructional videos with no embedded feedback,embedded questions without feedback and embedded questions with feedback.Findings from the experiment showed that the instructional videos with embedded questions but without feedback not only improved the participants’attention but also enhanced their learning performance.Furthermore,there was an expertise reversal effect on the learning performance whereby instructional videos with embedded questions but without feedback improved the learning performance of learners with low-level prior knowledge,but not those with high-level prior knowledge.

How to Optimize Homework Evaluation under the"Double Reduction"Policy?--An Analysis Based on Teachers'Attention Allocation

It is essential to ensure that teachers allocate adequate attention to homework evaluation and effectively carry it out in order to successfully implement the"double reduction"policy.From the perspective of teachers'attention allocation,this study employed the NVivo 12 software to conduct text analysis on 39 cases of homework evaluation reform practices in primary and secondary schools in City N,China,from 2017 to 2021.The findings indicate that homework evaluation reform in these schools is a practical problem-oriented behavioral decision,and implementing the"double reduction"policy enhances teachers'attention allocation on homework evaluation.The attention allocation of teachers encompasses multiple aspects,such as determining the purpose of evaluation,setting evaluation content,and selecting evaluation subjects and methods.Following the implementation of the"double reduction"policy,teachers allocate more attention to reducing the homework burden on students.However,certain issues persist in the practices of homework evaluation reform,including inadequate consideration of constraints,an unbalanced content structure,and a lack of process coordination.Therefore,under the"double reduction"policy,it is imperative to improve school incentive systems,enhance teachers'evaluation capabilities,and alleviate their workload.These measures can guide teachers to allocate more attention to homework evaluation,thereby enhancing the efficiency and sustainability of attention allocation and fully realizing the educational function of homework evaluation.

K-12 Students’Online Learning Experiences during COVID-19:Lessons from China

Online learning has become the new educational pattern during the COVID-19 pandemic and is likely to supplement conventional schooling in the post-pandemic world.Lacking prior online learning experiences,the population of K-12 students deserves our special attention.Using purposeful sampling,this study investigated K-12 online learning experiences in China based on a large-scale survey(N=118,589).Leveraging both quantitative and qualitative evidence,this study supported online learning as a flexible alternative to conventional schooling in emergency situations with a discussion of its benefits and limitations,and revealed key findings regarding K-12 students’online learning pattern,experiences,and engagement,as well as the influencing factors.The research findings can inform the future design and implementation of online learning programs in primary and secondary schools.