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claims, counterexamples, misconception alerts, and knowledge boundaries in digital textbooks. Records include source material, normalized questions, corrective answers, misconception types, key concepts, correction points, and answer quality scores. It supports training models to clarify specialized knowledge with textbook-based explanations, distinguish long-tail concepts, and explain the limits of a claim.\u003C\u002Fp>","2026-10-15 18:04:49","2026-10-19 17:19:54","0.00",0,5000,"digital textbook Q&A data,misconception correction dataset,textbook knowledge questions,concept disambiguation training data,education LLM data,specialized domain Q&A,knowledge correction corpus,Q&A model fine-tuning","Digital textbook misconception Q&A with normalized questions, textbook-based explanations, correction points, and quality scores for specialized knowledge and Q","JSON","0.2GB","Textbook Learning, Misconception Correction, Conceptual Reasoning","question answering, explanation generation, concept reasoning","V1.0","rivo\u002Fsample-packages\u002Ff9fa0912-3542-4da0-bedd-bd69f409de7c\u002F20261015.zip",[583,588,593,598,603,609,615,621],{"id":572,"name":584,"field":585,"intro":586,"type":587},"File Name","file_name","File name","string",{"id":589,"name":590,"field":591,"intro":592,"type":587},8981,"Corrective Q&A Answer","answer","A complete user-facing answer that identifies the error, states the correct interpretation, and explains it with textbook evidence, a counterexample, or applicable boundaries.",{"id":594,"name":595,"field":596,"intro":597,"type":587},8982,"Normalized Question","question","A concept-disambiguation question rewritten from an incorrect claim in a form suitable for users.",{"id":599,"name":600,"field":601,"intro":602,"type":587},8983,"Key Concept","key_concept","The concept, term, or knowledge point that the Q&A item is intended to clarify.",{"id":604,"name":605,"field":606,"intro":607,"type":608},8984,"Original Textbook Misconception Record","source_record","Original misconception-correction content from digital textbooks or human curation, provided as input for question-and-answer generation and annotation.","object",{"id":610,"name":611,"field":612,"intro":613,"type":614},8985,"Answer Quality Score","answer_quality","An overall score from 0 to 1 for the corrective answer, based on accuracy, consistency with the textbook, adequacy of boundary explanations, and clarity.","number",{"id":616,"name":617,"field":618,"intro":619,"type":620},8986,"Correction Points","correction_points","Key points extracted from the corrective explanation and ordered to support understanding.","array",{"id":622,"name":623,"field":624,"intro":625,"type":587},8987,"Misconception Type","misconception_type","A category for the incorrect claim, such as concept confusion, omitted conditions, reversed causality, overgeneralization, or overlooking counterexamples.",[627,631,635,639],{"id":628,"question":629,"answer":630},6379,"What does the dataset contain?","It focuses on common misconceptions and confused concepts in digital textbooks. Records provide normalized questions, corrective answers, misconception types, key concepts, and correction points, along with source material and answer quality scores.",{"id":632,"question":633,"answer":634},6380,"How do the corrective answers teach knowledge boundaries?","Answers identify the inaccurate claim, state the correct interpretation, and explain it using textbook evidence, counterexamples, or applicable conditions. This helps models distinguish related concepts and determine when a conclusion applies.",{"id":636,"question":637,"answer":638},6381,"Which training tasks and use cases does it support?","The dataset supports knowledge question answering, misconception detection, concept disambiguation, and answer generation. Use cases include digital textbook Q&A, online learning support, educational AI assistants, and specialized knowledge services.",{"id":640,"question":641,"answer":642},6382,"How can the dataset be accessed, and what are the licensing terms?","Access options, permitted uses, and licensing conditions depend on the provider's product page or license agreement. Review the terms before use and ensure that downstream training, deployment, and content distribution comply with them.",[644,659,671],{"id":645,"relate_table":646,"relate_id":647,"file_size":572,"file_name":648,"path":8,"duration":572,"cate":483,"file_format":649,"audio_rate":8,"audio_channel":522,"audio_bit_depth":522,"quality":8,"annotation":650,"cate_name":651,"answer":652,"question":653,"key_concept":654,"source_record":655,"answer_quality":656,"correction_points":657,"misconception_type":658},15096,"union_cubo",411142894,"样例_3.json","json","{\"answer\": \"It cannot be guaranteed. Training set accuracy measures how well the model fits data it has already seen; if the model is too complex, it may achieve 100% accuracy by memorizing training samples or even noise, yet fail to correctly handle new samples, a phenomenon called overfitting. To judge generalization ability, one should use a validation set or test set appropriately isolated from the training process and comprehensively consider factors such as data representativeness, model complexity, and regularization. Therefore, high training accuracy does not necessarily mean the best performance on new data; the gap between training accuracy and validation or test performance also needs attention.\", \"question\": \"If a machine learning model's training set accuracy is raised to 100%, can it be guaranteed to perform best on new data?\", \"key_concept\": \"Training accuracy and model generalization ability\", \"source_record\": \"{\\\"misconception_statement\\\": \\\"As long as a machine learning model's training set accuracy is raised to 100%, its performance on new data must be the best.\\\", \\\"correction_explanation\\\": \\\"Training set accuracy only reflects how well the model fits the training samples it has seen; reaching 100% may mean the model has memorized details or even noise in the training data. Whether a model can generalize to new data should be evaluated in conjunction with its performance on an independent validation set or test set. Model complexity, data representativeness, regularization methods, and the training-validation split all affect generalization ability.\\\", \\\"concept_domain\\\": \\\"Machine learning: model fitting, overfitting, and generalization\\\", \\\"counterexample\\\": \\\"A high-complexity model can achieve 100% training accuracy by memorizing every training sample, but its accuracy on the validation set drops markedly; by contrast, a regularized model with slightly lower training accuracy may perform better on new data.\\\", \\\"knowledge_boundary\\\": \\\"Training set accuracy is one metric for evaluating a model, but it cannot by itself prove that the model generalizes well. The validation set and test set should be appropriately isolated from the training process, and their data distribution should represent the actual use scenario. Test set results are also not an absolute guarantee for all future data.\\\", \\\"textbook_reference\\\": \\\"Section 'Overfitting, underfitting, and dataset splitting' in Chapter 4, 'Model evaluation and generalization ability', of Foundations of Machine Learning.\\\"}\", \"answer_quality\": \"0.99\", \"correction_points\": \"[\\\"Training set accuracy only reflects how well the model fits the samples it has seen.\\\", \\\"Training accuracy reaching 100% may be accompanied by overfitting.\\\", \\\"Generalization ability needs to be evaluated using an independent validation set or test set.\\\", \\\"A model's performance on future data is also affected by factors such as data distribution, model complexity, and regularization.\\\"]\", \"misconception_type\": \"Overgeneralization\"}","文本","It cannot be guaranteed. Training set accuracy measures how well the model fits data it has already seen; if the model is too complex, it may achieve 100% accuracy by memorizing training samples or even noise, yet fail to correctly handle new samples, a phenomenon called overfitting. To judge generalization ability, one should use a validation set or test set appropriately isolated from the training process and comprehensively consider factors such as data representativeness, model complexity, and regularization. Therefore, high training accuracy does not necessarily mean the best performance on new data; the gap between training accuracy and validation or test performance also needs attention.","If a machine learning model's training set accuracy is raised to 100%, can it be guaranteed to perform best on new data?","Training accuracy and model generalization ability","{\"misconception_statement\": \"As long as a machine learning model's training set accuracy is raised to 100%, its performance on new data must be the best.\", \"correction_explanation\": \"Training set accuracy only reflects how well the model fits the training samples it has seen; reaching 100% may mean the model has memorized details or even noise in the training data. Whether a model can generalize to new data should be evaluated in conjunction with its performance on an independent validation set or test set. Model complexity, data representativeness, regularization methods, and the training-validation split all affect generalization ability.\", \"concept_domain\": \"Machine learning: model fitting, overfitting, and generalization\", \"counterexample\": \"A high-complexity model can achieve 100% training accuracy by memorizing every training sample, but its accuracy on the validation set drops markedly; by contrast, a regularized model with slightly lower training accuracy may perform better on new data.\", \"knowledge_boundary\": \"Training set accuracy is one metric for evaluating a model, but it cannot by itself prove that the model generalizes well. The validation set and test set should be appropriately isolated from the training process, and their data distribution should represent the actual use scenario. Test set results are also not an absolute guarantee for all future data.\", \"textbook_reference\": \"Section 'Overfitting, underfitting, and dataset splitting' in Chapter 4, 'Model evaluation and generalization ability', of Foundations of Machine Learning.\"}","0.99","[\"Training set accuracy only reflects how well the model fits the samples it has seen.\", \"Training accuracy reaching 100% may be accompanied by overfitting.\", \"Generalization ability needs to be evaluated using an independent validation set or test set.\", \"A model's performance on future data is also affected by factors such as data distribution, model complexity, and regularization.\"]","Overgeneralization",{"id":660,"relate_table":646,"relate_id":661,"file_size":572,"file_name":662,"path":8,"duration":572,"cate":483,"file_format":649,"audio_rate":8,"audio_channel":522,"audio_bit_depth":522,"quality":8,"annotation":663,"cate_name":651,"answer":664,"question":665,"key_concept":666,"source_record":667,"answer_quality":668,"correction_points":669,"misconception_type":670},15095,426383639,"样例_2.json","{\"answer\": \"Usually not. Influenza is caused by influenza viruses, while antibiotics mainly act on specific microorganisms such as bacteria and cannot directly kill influenza viruses. Therefore, the presence of infection symptoms such as fever and cough should not lead to the inference that antibiotics are needed. If there is clear clinical evidence of a concurrent bacterial infection, doctors may consider antibacterial treatment based on the pathogen and the condition. Treating antibiotics as drugs for all infections is an incorrect claim that confuses viruses with bacteria and overextends the scope of drug use; unnecessary use may also cause adverse effects and drug resistance problems.\", \"question\": \"When someone has influenza, can antibiotics kill the influenza virus and help treat it?\", \"key_concept\": \"Targets of antibiotics and viral infections\", \"source_record\": \"{\\\"misconception_statement\\\": \\\"Antibiotics can kill all pathogens that cause infections, so antibiotics should also be used for influenza.\\\", \\\"correction_explanation\\\": \\\"Antibiotics mainly act against specific microorganisms such as bacteria and cannot directly kill influenza viruses. Influenza is caused by influenza viruses; using antibiotics usually cannot shorten the course of a viral infection, and may instead cause unnecessary adverse effects and increase the risk of drug resistance. Only when there is clear medical evidence indicating a concurrent bacterial infection may corresponding antibacterial treatment be considered.\\\", \\\"concept_domain\\\": \\\"Biology and medical common knowledge: pathogen classification and anti-infective drugs\\\", \\\"counterexample\\\": \\\"Influenza virus infection is a viral disease, and antibiotics cannot directly disrupt the replication process of influenza viruses; only certain infections caused by bacteria may be susceptible to the corresponding antibiotics.\\\", \\\"knowledge_boundary\\\": \\\"Different antibiotics have different targets, and antibiotics are not universal drugs for treating all bacterial infections. Whether a bacterial infection exists, whether medication is needed, and which specific drug to choose should be based on medical evaluation, not self-judged solely from fever or cough.\\\", \\\"textbook_reference\\\": \\\"Section 'Viruses, bacteria, and their prevention and treatment' in Chapter 5, 'Microorganisms and disease', of Basic Biology and Health Education.\\\"}\", \"answer_quality\": \"0.97\", \"correction_points\": \"[\\\"Antibiotics mainly target specific microorganisms such as bacteria, not all pathogens.\\\", \\\"Influenza is caused by a virus, and antibiotics cannot directly kill influenza viruses.\\\", \\\"The presence of infection symptoms does not necessarily mean antibiotics are needed.\\\", \\\"Only when there is medical evidence suggesting a bacterial infection should professionals evaluate whether to use antibacterial drugs.\\\"]\", \"misconception_type\": \"Conceptual confusion\"}","Usually not. Influenza is caused by influenza viruses, while antibiotics mainly act on specific microorganisms such as bacteria and cannot directly kill influenza viruses. Therefore, the presence of infection symptoms such as fever and cough should not lead to the inference that antibiotics are needed. If there is clear clinical evidence of a concurrent bacterial infection, doctors may consider antibacterial treatment based on the pathogen and the condition. Treating antibiotics as drugs for all infections is an incorrect claim that confuses viruses with bacteria and overextends the scope of drug use; unnecessary use may also cause adverse effects and drug resistance problems.","When someone has influenza, can antibiotics kill the influenza virus and help treat it?","Targets of antibiotics and viral infections","{\"misconception_statement\": \"Antibiotics can kill all pathogens that cause infections, so antibiotics should also be used for influenza.\", \"correction_explanation\": \"Antibiotics mainly act against specific microorganisms such as bacteria and cannot directly kill influenza viruses. Influenza is caused by influenza viruses; using antibiotics usually cannot shorten the course of a viral infection, and may instead cause unnecessary adverse effects and increase the risk of drug resistance. Only when there is clear medical evidence indicating a concurrent bacterial infection may corresponding antibacterial treatment be considered.\", \"concept_domain\": \"Biology and medical common knowledge: pathogen classification and anti-infective drugs\", \"counterexample\": \"Influenza virus infection is a viral disease, and antibiotics cannot directly disrupt the replication process of influenza viruses; only certain infections caused by bacteria may be susceptible to the corresponding antibiotics.\", \"knowledge_boundary\": \"Different antibiotics have different targets, and antibiotics are not universal drugs for treating all bacterial infections. Whether a bacterial infection exists, whether medication is needed, and which specific drug to choose should be based on medical evaluation, not self-judged solely from fever or cough.\", \"textbook_reference\": \"Section 'Viruses, bacteria, and their prevention and treatment' in Chapter 5, 'Microorganisms and disease', of Basic Biology and Health Education.\"}","0.97","[\"Antibiotics mainly target specific microorganisms such as bacteria, not all pathogens.\", \"Influenza is caused by a virus, and antibiotics cannot directly kill influenza viruses.\", \"The presence of infection symptoms does not necessarily mean antibiotics are needed.\", \"Only when there is medical evidence suggesting a bacterial infection should professionals evaluate whether to use antibacterial drugs.\"]","Conceptual confusion",{"id":672,"relate_table":646,"relate_id":673,"file_size":572,"file_name":674,"path":8,"duration":572,"cate":483,"file_format":649,"audio_rate":8,"audio_channel":522,"audio_bit_depth":522,"quality":8,"annotation":675,"cate_name":651,"answer":676,"question":677,"key_concept":678,"source_record":679,"answer_quality":680,"correction_points":681,"misconception_type":670},15094,1410756724,"样例_1.json","{\"answer\": \"Not necessarily. Zero net force means acceleration is zero, not that velocity is necessarily zero. In an inertial reference frame, if an object is initially at rest, it will remain at rest; if it is initially moving at a certain velocity, it will maintain uniform rectilinear motion. For example, a curling stone sliding at constant speed on an approximately frictionless ice surface experiences a net force close to zero but is still moving. Therefore, 'zero net force' describes an equilibrium state, and rest is only one form of equilibrium state; the two cannot be equated. This conclusion also needs to be discussed in an inertial reference frame, and it cannot be used to determine the magnitude of an object's velocity.\", \"question\": \"When the net force on an object is zero, is it necessarily at rest?\", \"key_concept\": \"Zero net force and equilibrium state\", \"source_record\": \"{\\\"misconception_statement\\\": \\\"If the net force acting on an object is zero, it must be at rest.\\\", \\\"correction_explanation\\\": \\\"Zero net force means the object's acceleration is zero, not that its velocity must be zero. In an inertial reference frame, an object can remain at rest or continue in uniform rectilinear motion. Therefore, zero net force corresponds to an equilibrium state, and rest is only one such case.\\\", \\\"concept_domain\\\": \\\"High school physics: Newton's laws of motion and equilibrium states\\\", \\\"counterexample\\\": \\\"A curling stone moving in uniform rectilinear motion on ice, if the resistance it experiences can be ignored, has an approximately zero net force, but it is still moving and not at rest.\\\", \\\"knowledge_boundary\\\": \\\"The above conclusion presupposes an inertial reference frame; zero net force only implies zero acceleration and cannot by itself imply zero velocity. If the object's initial velocity is not zero, it will continue in uniform rectilinear motion.\\\", \\\"textbook_reference\\\": \\\"Section 'Kinematic meaning of zero net force' in Chapter 2, 'Newton's second law and equilibrium states', of High School Physics Concept Analysis.\\\"}\", \"answer_quality\": \"0.98\", \"correction_points\": \"[\\\"Zero net force means acceleration is zero.\\\", \\\"An object can be at rest or in uniform rectilinear motion.\\\", \\\"Rest is only one case of equilibrium and should not be equated with zero net force.\\\", \\\"When evaluating this conclusion, the inertial reference frame condition should be made explicit.\\\"]\", \"misconception_type\": \"Conceptual confusion\"}","Not necessarily. Zero net force means acceleration is zero, not that velocity is necessarily zero. In an inertial reference frame, if an object is initially at rest, it will remain at rest; if it is initially moving at a certain velocity, it will maintain uniform rectilinear motion. For example, a curling stone sliding at constant speed on an approximately frictionless ice surface experiences a net force close to zero but is still moving. Therefore, 'zero net force' describes an equilibrium state, and rest is only one form of equilibrium state; the two cannot be equated. This conclusion also needs to be discussed in an inertial reference frame, and it cannot be used to determine the magnitude of an object's velocity.","When the net force on an object is zero, is it necessarily at rest?","Zero net force and equilibrium state","{\"misconception_statement\": \"If the net force acting on an object is zero, it must be at rest.\", \"correction_explanation\": \"Zero net force means the object's acceleration is zero, not that its velocity must be zero. In an inertial reference frame, an object can remain at rest or continue in uniform rectilinear motion. Therefore, zero net force corresponds to an equilibrium state, and rest is only one such case.\", \"concept_domain\": \"High school physics: Newton's laws of motion and equilibrium states\", \"counterexample\": \"A curling stone moving in uniform rectilinear motion on ice, if the resistance it experiences can be ignored, has an approximately zero net force, but it is still moving and not at rest.\", \"knowledge_boundary\": \"The above conclusion presupposes an inertial reference frame; zero net force only implies zero acceleration and cannot by itself imply zero velocity. If the object's initial velocity is not zero, it will continue in uniform rectilinear motion.\", \"textbook_reference\": \"Section 'Kinematic meaning of zero net force' in Chapter 2, 'Newton's second law and equilibrium states', of High School Physics Concept Analysis.\"}","0.98","[\"Zero net force means acceleration is zero.\", \"An object can be at rest or in uniform rectilinear motion.\", \"Rest is only one case of equilibrium and should not be equated with zero net force.\", \"When evaluating this conclusion, the inertial reference frame condition should be made explicit.\"]","{\"answer\":\"It cannot be guaranteed. Training set accuracy measures how well the model fits data it has already seen; if the model is too complex, it may achieve 100% accuracy by memorizing training samples or even noise, yet fail to correctly handle new samples, a phenomenon called overfitting. To judge generalization ability, one should use a validation set or test set appropriately isolated from the training process and comprehensively consider factors such as data representativeness, model complexity, and regularization. Therefore, high training accuracy does not necessarily mean the best performance on new data; the gap between training accuracy and validation or test performance also needs attention.\",\"question\":\"If a machine learning model's training set accuracy is raised to 100%, can it be guaranteed to perform best on new data?\",\"key_concept\":\"Training accuracy and model generalization ability\",\"source_record\":\"{\\\"misconception_statement\\\": \\\"As long as a machine learning model's training set accuracy is raised to 100%, its performance on new data must be the best.\\\", \\\"correction_explanation\\\": \\\"Training set accuracy only reflects how well the model fits the training samples it has seen; reaching 100% may mean the model has memorized details or even noise in the training data. Whether a model can generalize to new data should be evaluated in conjunction with its performance on an independent validation set or test set. Model complexity, data representativeness, regularization methods, and the training-validation split all affect generalization ability.\\\", \\\"concept_domain\\\": \\\"Machine learning: model fitting, overfitting, and generalization\\\", \\\"counterexample\\\": \\\"A high-complexity model can achieve 100% training accuracy by memorizing every training sample, but its accuracy on the validation set drops markedly; by contrast, a regularized model with slightly lower training accuracy may perform better on new data.\\\", \\\"knowledge_boundary\\\": \\\"Training set accuracy is one metric for evaluating a model, but it cannot by itself prove that the model generalizes well. The validation set and test set should be appropriately isolated from the training process, and their data distribution should represent the actual use scenario. Test set results are also not an absolute guarantee for all future data.\\\", \\\"textbook_reference\\\": \\\"Section 'Overfitting, underfitting, and dataset splitting' in Chapter 4, 'Model evaluation and generalization ability', of Foundations of Machine Learning.\\\"}\",\"answer_quality\":\"0.99\",\"correction_points\":\"[\\\"Training set accuracy only reflects how well the model fits the samples it has seen.\\\", \\\"Training accuracy reaching 100% may be accompanied by overfitting.\\\", \\\"Generalization ability needs to be evaluated using an independent validation set or test set.\\\", \\\"A model's performance on future data is also affected by factors such as data distribution, model complexity, and regularization.\\\"]\",\"misconception_type\":\"Overgeneralization\"}","MOBIUSI INC","https:\u002F\u002Fstatic.mobiusi.com\u002Fmobiwusi\u002F20250829\u002F7bf4ac62b4533e2b809998a6f1c5491e.png?Expires=1792463130&OSSAccessKeyId=LTAI5tH8MQqhgeiAtrzCHrxM&Signature=v6Tr8jBrdm4s5wU1SnnyGsYg%2Bhw%3D&x-oss-process=image%2Fresize%2Cm_fill%2Cw_35%2Ch_35",[686,692],{"type":687,"title":688,"count_data":689},"bar","Length distribution",[690],{"name":691,"value":483},"1k lower",{"type":693,"title":694,"count_data":695},"pie","Language distribution",[696],{"name":697,"value":483},"English"]