Pub Date : 2018-07-01DOI: 10.1109/iiai-aai.2018.00003
{"title":"[Copyright notice]","authors":"","doi":"10.1109/iiai-aai.2018.00003","DOIUrl":"https://doi.org/10.1109/iiai-aai.2018.00003","url":null,"abstract":"","PeriodicalId":309975,"journal":{"name":"2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133771321","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}
Pub Date : 2018-07-01DOI: 10.1109/IIAI-AAI.2018.00206
Chih-Fong Tsai
Image feature representation by bag-of-visual words (BOVW) has been widely considered in the image classification related problems. The feature extraction step is usually based on tokenizing the detected keypoints as the visual words. As a result, the visual-word vector of an image represents how often the visual words occur in an image. To train and test an image classifier, the BOVW features of the training and testing images can be extracted by either at the same time or separately. Therefore, the aim of this paper is to examine the classification performance of using these two different feature extraction strategies. We show that there is no significant difference between these two strategies, but extracting the BOVW features from the training and testing images at the same time requires much longer time. Therefore, the key criterion of choosing the right strategy of BOVW feature extraction is based on the dataset size.
{"title":"Two Strategies for Bag-of-Visual Words Feature Extraction","authors":"Chih-Fong Tsai","doi":"10.1109/IIAI-AAI.2018.00206","DOIUrl":"https://doi.org/10.1109/IIAI-AAI.2018.00206","url":null,"abstract":"Image feature representation by bag-of-visual words (BOVW) has been widely considered in the image classification related problems. The feature extraction step is usually based on tokenizing the detected keypoints as the visual words. As a result, the visual-word vector of an image represents how often the visual words occur in an image. To train and test an image classifier, the BOVW features of the training and testing images can be extracted by either at the same time or separately. Therefore, the aim of this paper is to examine the classification performance of using these two different feature extraction strategies. We show that there is no significant difference between these two strategies, but extracting the BOVW features from the training and testing images at the same time requires much longer time. Therefore, the key criterion of choosing the right strategy of BOVW feature extraction is based on the dataset size.","PeriodicalId":309975,"journal":{"name":"2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133904491","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}
Pub Date : 2018-07-01DOI: 10.1109/IIAI-AAI.2018.00041
Chih-Hung Chang, Chih-Ming Chen, Rong-Hua Zhao
Many studies have confirmed that the collaborative problem-based learning (CPBL) mode is an increasingly popular educational paradigm that has highly potential to cultivate learners' collaborative learning and problem solving abilities. However, how to effectively promote positive group members' interaction and group accountability is a critical issue in the CPBL mode. This work thus presents a group incentive mechanism (GIM) based on considering several important factors affecting peers' interaction and group accountability in collaborative learning theories to promote the learning performance learners in a CPBL system. To evaluate the effectiveness of the proposed GIM, this work recruited 48 Grade 4 students from an elementary school to participate in the instruction experiment. The quasi-experimental design was adopted to assess the differences in learning performance, interaction relationship, group efficacy, and group cohesiveness between the experimental group learners using the proposed GIM and control group learners using the individual incentive mechanism (IIM) under using the CPBL system to collaboratively solve a target problem. Analytical results show that although the control group learners using the IIM had higher social network interaction than the experimental group learners using the proposed GIM, the experimental group learners presents better learning performance and group efficacy than the control group.
{"title":"An Effective Group Incentive Mechanism in a Collaborative Problem-Based Learning System for Enhancing Positive Peer Interaction and Learning Performance","authors":"Chih-Hung Chang, Chih-Ming Chen, Rong-Hua Zhao","doi":"10.1109/IIAI-AAI.2018.00041","DOIUrl":"https://doi.org/10.1109/IIAI-AAI.2018.00041","url":null,"abstract":"Many studies have confirmed that the collaborative problem-based learning (CPBL) mode is an increasingly popular educational paradigm that has highly potential to cultivate learners' collaborative learning and problem solving abilities. However, how to effectively promote positive group members' interaction and group accountability is a critical issue in the CPBL mode. This work thus presents a group incentive mechanism (GIM) based on considering several important factors affecting peers' interaction and group accountability in collaborative learning theories to promote the learning performance learners in a CPBL system. To evaluate the effectiveness of the proposed GIM, this work recruited 48 Grade 4 students from an elementary school to participate in the instruction experiment. The quasi-experimental design was adopted to assess the differences in learning performance, interaction relationship, group efficacy, and group cohesiveness between the experimental group learners using the proposed GIM and control group learners using the individual incentive mechanism (IIM) under using the CPBL system to collaboratively solve a target problem. Analytical results show that although the control group learners using the IIM had higher social network interaction than the experimental group learners using the proposed GIM, the experimental group learners presents better learning performance and group efficacy than the control group.","PeriodicalId":309975,"journal":{"name":"2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134369901","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}
Pub Date : 2018-07-01DOI: 10.1109/IIAI-AAI.2018.00120
Phasit Charoenkwan
Thailand has a huge number of Facebook user. Most company has their own public page to communicate with their customers. Thus, it's desirable to perform sentimental analysis on Facebook post messages to understand customer's reaction of specific promotion, event or news. This work aims to propose a novel method to perform sentimental analysis on Thai Facebook data by combining information generated from a classical Bag-Of-Words features and advance deep learning approaches called ThaiFBDeep. Remarkably, according to Thai people usually conduct new words every year, the proposed data preprocessing techniques should be able to handle this kind of words. The experiment results show that ThaiFBDeep achieved a 91.75% of train accuracy and an 83.36% of independent test accuracy which is better than other well-known methods i.e. Naïve Bayes, Support Vector Machine, Multi-Layers Perceptron, Long Short-Term Memory and Convolution Neural Networks. These results also show that the including of Bag-Of-Words features can improve efficiency of Deep Learning based approach for sentimental analysis.
{"title":"ThaiFBDeep: A Sentimental Analysis Using Deep Learning Combined with Bag-of-Words Features on Thai Facebook Data","authors":"Phasit Charoenkwan","doi":"10.1109/IIAI-AAI.2018.00120","DOIUrl":"https://doi.org/10.1109/IIAI-AAI.2018.00120","url":null,"abstract":"Thailand has a huge number of Facebook user. Most company has their own public page to communicate with their customers. Thus, it's desirable to perform sentimental analysis on Facebook post messages to understand customer's reaction of specific promotion, event or news. This work aims to propose a novel method to perform sentimental analysis on Thai Facebook data by combining information generated from a classical Bag-Of-Words features and advance deep learning approaches called ThaiFBDeep. Remarkably, according to Thai people usually conduct new words every year, the proposed data preprocessing techniques should be able to handle this kind of words. The experiment results show that ThaiFBDeep achieved a 91.75% of train accuracy and an 83.36% of independent test accuracy which is better than other well-known methods i.e. Naïve Bayes, Support Vector Machine, Multi-Layers Perceptron, Long Short-Term Memory and Convolution Neural Networks. These results also show that the including of Bag-Of-Words features can improve efficiency of Deep Learning based approach for sentimental analysis.","PeriodicalId":309975,"journal":{"name":"2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134521382","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}
Pub Date : 2018-07-01DOI: 10.1109/IIAI-AAI.2018.00137
Almog Boanos, S. Chandrababu, D. Bastola
Plant secondary metabolites are critical factors that aid plants in adaptation to their environment and are important sources of pharmaceuticals. Growth environment chambers nowadays are employed only to improve the overall production of the plant, while paying less attention to its quality. Automated strategies can be applied to attune the existing model, making it compatible for precisely controlling the environmental factors, which are the significant effectors of changes in the metabolic pathways of secondary metabolites. Previously, MIT has developed a Personal Food Computer (PFC) to control the growth environment of plants to maintain uniformity in their production in an urban setting. However, several challenges remained untouched, especially when the PFC was used in a research setting. One such instance was that an increase in the daylight negatively impacted the level of humidity, which could be undesirable and requires manual intervention to maintain grow-condition stability. To overcome the shortcomings of the existing model we have modified MIT's PFC by implementing cloud-based flexible automation techniques along with robotics to develop a Cost-Effective Automated Food Computer (CEAFC). The present article is aimed at addressing the automated features of CEAFC and its eloquent use in the production of secondary metabolites of therapeutic value.
{"title":"Automation of Personal Food Computers for Research in Drug Development and Biomedicine","authors":"Almog Boanos, S. Chandrababu, D. Bastola","doi":"10.1109/IIAI-AAI.2018.00137","DOIUrl":"https://doi.org/10.1109/IIAI-AAI.2018.00137","url":null,"abstract":"Plant secondary metabolites are critical factors that aid plants in adaptation to their environment and are important sources of pharmaceuticals. Growth environment chambers nowadays are employed only to improve the overall production of the plant, while paying less attention to its quality. Automated strategies can be applied to attune the existing model, making it compatible for precisely controlling the environmental factors, which are the significant effectors of changes in the metabolic pathways of secondary metabolites. Previously, MIT has developed a Personal Food Computer (PFC) to control the growth environment of plants to maintain uniformity in their production in an urban setting. However, several challenges remained untouched, especially when the PFC was used in a research setting. One such instance was that an increase in the daylight negatively impacted the level of humidity, which could be undesirable and requires manual intervention to maintain grow-condition stability. To overcome the shortcomings of the existing model we have modified MIT's PFC by implementing cloud-based flexible automation techniques along with robotics to develop a Cost-Effective Automated Food Computer (CEAFC). The present article is aimed at addressing the automated features of CEAFC and its eloquent use in the production of secondary metabolites of therapeutic value.","PeriodicalId":309975,"journal":{"name":"2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114772960","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}
Agent-based traffic simulation has become more and more attractive and important to develop new ITS (Intelligent Transport Systems) services. So far a variety of studies and developments that combine simulators and evaluate ITS services on the combined simulators have been conducted. In this paper, we introduce a simulation environment, called Agent-based USE (Agent-based Unified Simulation Environment), and some application examples for ITS services. The Agent-based USE provides an easy-to-build simulation environment for ITS-related services. In particular, by connecting simulators with ITS services, the Agent-based USE determines behaviors to be changed on the simulators using the data of the services such as recommendation results generated by the services, tells the decisions to simulators; the Agent-based USE then obtains the data representing the current situation on the simulators and sends the data to the services as feedback so as to enable the services to generate the next recommendation. In addition, by using the Agent-based USE, it is possible to construct a co-simulation environment where simulation is performed by synchronizing some kinds of simulators and services and by sharing each simulation information. In this paper, we introduce the overview and architecture of the Agent-based USE for traffic simulation, and discuss its usefulness through some application examples.
{"title":"Overview and Application Examples of Agent-Based Unified Simulation Environment","authors":"Takahiro Ando, Ryo Fujii, K. Hisazumi, Tsunenori Mine, Tsuneo Nakanishi, Akira Fukuda","doi":"10.1109/IIAI-AAI.2018.00027","DOIUrl":"https://doi.org/10.1109/IIAI-AAI.2018.00027","url":null,"abstract":"Agent-based traffic simulation has become more and more attractive and important to develop new ITS (Intelligent Transport Systems) services. So far a variety of studies and developments that combine simulators and evaluate ITS services on the combined simulators have been conducted. In this paper, we introduce a simulation environment, called Agent-based USE (Agent-based Unified Simulation Environment), and some application examples for ITS services. The Agent-based USE provides an easy-to-build simulation environment for ITS-related services. In particular, by connecting simulators with ITS services, the Agent-based USE determines behaviors to be changed on the simulators using the data of the services such as recommendation results generated by the services, tells the decisions to simulators; the Agent-based USE then obtains the data representing the current situation on the simulators and sends the data to the services as feedback so as to enable the services to generate the next recommendation. In addition, by using the Agent-based USE, it is possible to construct a co-simulation environment where simulation is performed by synchronizing some kinds of simulators and services and by sharing each simulation information. In this paper, we introduce the overview and architecture of the Agent-based USE for traffic simulation, and discuss its usefulness through some application examples.","PeriodicalId":309975,"journal":{"name":"2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116583295","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}
Pub Date : 2018-07-01DOI: 10.1109/IIAI-AAI.2018.00060
Y. S. Wong, M. H. M. Yatim
In order to understand the important of object-oriented programming in tertiary level, a propriety game-based learning multiplatform game has been designed and develops as a learning tool to improve the student understanding toward object-oriented programming paradigm such as encapsulation, abstraction, inheritance and polymorphism. The proposed game is a 2D role-playing game in computer and mobile platform that allow players to learn Object-Oriented programming in an interaction way. Players will play along the flow of each game world and they will learn object-oriented programming paradigm subconsciously. Total of 214 undergraduate year one student had been participate to this research to determine the proposed game that design based on game-based learning approach is able to improve their understanding toward object-oriented programming paradigm compare to the traditional teaching and learning method. Thus, this paper is a research paper of an academic who worked with game designers, game developer to design and develop a propriety game-based learning game for learning object-oriented programming.
{"title":"A Propriety Multiplatform Game-Based Learning Game to Learn Object-Oriented Programming","authors":"Y. S. Wong, M. H. M. Yatim","doi":"10.1109/IIAI-AAI.2018.00060","DOIUrl":"https://doi.org/10.1109/IIAI-AAI.2018.00060","url":null,"abstract":"In order to understand the important of object-oriented programming in tertiary level, a propriety game-based learning multiplatform game has been designed and develops as a learning tool to improve the student understanding toward object-oriented programming paradigm such as encapsulation, abstraction, inheritance and polymorphism. The proposed game is a 2D role-playing game in computer and mobile platform that allow players to learn Object-Oriented programming in an interaction way. Players will play along the flow of each game world and they will learn object-oriented programming paradigm subconsciously. Total of 214 undergraduate year one student had been participate to this research to determine the proposed game that design based on game-based learning approach is able to improve their understanding toward object-oriented programming paradigm compare to the traditional teaching and learning method. Thus, this paper is a research paper of an academic who worked with game designers, game developer to design and develop a propriety game-based learning game for learning object-oriented programming.","PeriodicalId":309975,"journal":{"name":"2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116055001","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}
Pub Date : 2018-07-01DOI: 10.1109/IIAI-AAI.2018.00023
Chun-Chia Wang, J. C. Hung, Chun-Hong Huang, Jia-Yu Chen
Traditionally, previous studies computed the click-through rate (CTR) to estimate a number of people who reviewed the advertisements placed online or used a self-report indicator to measure the effectiveness. However, these research results couldn't reveal potential customers for lack of valid common view on Internet advertising. This study recruited 65 participants whose eye movements were tracked and recorded by the eye tracking system, of which 35 were assigned to Facebook friend group and the other 30 were assigned to non-Facebook friend group according to the interpersonal relationships with the researcher. Eye tracking measurements, including total fixation duration (TFD) and latency of first (LFF) on the defined regions of interest (ROIs) of Facebook page were compared to indicate their visual attentions. The experimental results showed that 1) participants of the two groups spent less time viewing the ads at the right hand side (RHS) of Facebook based on TFD and 2) participants of non-Facebook friend group spent much time than participants of Facebook friend group while viewing the ads in the desktop news feed (DNF) of Facebook based on TFD. 3) Participants of the two groups have the same sequences of viewing ROIs placed ads at the right hand side (RHS). 4) Participants of the two groups have different sequences of viewing ROIs placed ads in the desktop news feed (DNF).
{"title":"Advertising Visual Attention to Facebook Social Network: Evidence from Eye Movements","authors":"Chun-Chia Wang, J. C. Hung, Chun-Hong Huang, Jia-Yu Chen","doi":"10.1109/IIAI-AAI.2018.00023","DOIUrl":"https://doi.org/10.1109/IIAI-AAI.2018.00023","url":null,"abstract":"Traditionally, previous studies computed the click-through rate (CTR) to estimate a number of people who reviewed the advertisements placed online or used a self-report indicator to measure the effectiveness. However, these research results couldn't reveal potential customers for lack of valid common view on Internet advertising. This study recruited 65 participants whose eye movements were tracked and recorded by the eye tracking system, of which 35 were assigned to Facebook friend group and the other 30 were assigned to non-Facebook friend group according to the interpersonal relationships with the researcher. Eye tracking measurements, including total fixation duration (TFD) and latency of first (LFF) on the defined regions of interest (ROIs) of Facebook page were compared to indicate their visual attentions. The experimental results showed that 1) participants of the two groups spent less time viewing the ads at the right hand side (RHS) of Facebook based on TFD and 2) participants of non-Facebook friend group spent much time than participants of Facebook friend group while viewing the ads in the desktop news feed (DNF) of Facebook based on TFD. 3) Participants of the two groups have the same sequences of viewing ROIs placed ads at the right hand side (RHS). 4) Participants of the two groups have different sequences of viewing ROIs placed ads in the desktop news feed (DNF).","PeriodicalId":309975,"journal":{"name":"2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116283900","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}
Pub Date : 2018-07-01DOI: 10.1109/IIAI-AAI.2018.00052
Qi-Zhone Huang, Chih-Chao Hsu, Tzone-I Wang
Scientific experiments are essential for science and technology education. Experiments in laboratory cost materials, require preparations, and sometimes cause hazards. A widely used educational tool with many advantages, e.g. cheap, repeatable, suspendable, and safe, virtual laboratory has gradually become a major experimental tool in most elementary and high schools. In educational science experiments, one major challenge is how to initiate students on scientific inquiry and ensure there are multiple opportunities for their formative self-assessment and revision. The self-explanation strategy has proven effective in deepen students' understanding of the concepts they are trying to learn. Using self-explanation strategy in educational science experiments might be an effective way to help students think about the observed results of science experiments and build correct scientific concepts. On the other hand, researches point out that using open-ended questions is better than traditional multiple-choice questions for self-explanation strategy. But when using open-ended question self-explanation strategy, without proper prior knowledge and guidance, a student may go wrong in the processes of deduction and result in constructing misconceptions that will become obstacles in further knowledge constructions. Therefore, a learning system that uses open-ended question self-explanation strategy should give proper feedback in order to help students build correct concepts when in self-learning mode. To help students operating in virtual science laboratory and constructing correct concepts from observed results this study constructs an online virtual laboratory learning system with open-ended question self-explanation strategy and proper feedback for natural science course of primary schools. The system uses natural language processing (NLP) technology to analyze students' self-explanation strings, compares the results with coded classification rules, established by an expert from reference explanations, to check the correctness of the strings and possible misconceptions in them, and gives proper learning material, as feedback, for the students to revise possible misconceptions. In the final experiment, the system records and checks all self-explanation strings from 53 students and gives them proper feedback, which reaches an average accuracy of 84.45% after the expert verify the results.
{"title":"An Open-Ended Question Self-Explanation Classification Methodology for a Virtual Laboratory Learning System","authors":"Qi-Zhone Huang, Chih-Chao Hsu, Tzone-I Wang","doi":"10.1109/IIAI-AAI.2018.00052","DOIUrl":"https://doi.org/10.1109/IIAI-AAI.2018.00052","url":null,"abstract":"Scientific experiments are essential for science and technology education. Experiments in laboratory cost materials, require preparations, and sometimes cause hazards. A widely used educational tool with many advantages, e.g. cheap, repeatable, suspendable, and safe, virtual laboratory has gradually become a major experimental tool in most elementary and high schools. In educational science experiments, one major challenge is how to initiate students on scientific inquiry and ensure there are multiple opportunities for their formative self-assessment and revision. The self-explanation strategy has proven effective in deepen students' understanding of the concepts they are trying to learn. Using self-explanation strategy in educational science experiments might be an effective way to help students think about the observed results of science experiments and build correct scientific concepts. On the other hand, researches point out that using open-ended questions is better than traditional multiple-choice questions for self-explanation strategy. But when using open-ended question self-explanation strategy, without proper prior knowledge and guidance, a student may go wrong in the processes of deduction and result in constructing misconceptions that will become obstacles in further knowledge constructions. Therefore, a learning system that uses open-ended question self-explanation strategy should give proper feedback in order to help students build correct concepts when in self-learning mode. To help students operating in virtual science laboratory and constructing correct concepts from observed results this study constructs an online virtual laboratory learning system with open-ended question self-explanation strategy and proper feedback for natural science course of primary schools. The system uses natural language processing (NLP) technology to analyze students' self-explanation strings, compares the results with coded classification rules, established by an expert from reference explanations, to check the correctness of the strings and possible misconceptions in them, and gives proper learning material, as feedback, for the students to revise possible misconceptions. In the final experiment, the system records and checks all self-explanation strings from 53 students and gives them proper feedback, which reaches an average accuracy of 84.45% after the expert verify the results.","PeriodicalId":309975,"journal":{"name":"2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123451501","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}
Pub Date : 2018-07-01DOI: 10.1109/IIAI-AAI.2018.00034
Yuming Guo, M. Iwaihara
Nowadays people can find almost all kinds of information they want from the Internet. However, in most cases, users are not willing to find their target among segment among long paragraphs, by spending much time browsing texts. Existing work on topic labeling works effectively and performs well on document categorization, but inadequate for granularity of detailed contents. Thus we propose a method for selecting titles for segments in long documents. We analyze the characteristics of high quality titles for article segments, from the aspect of semantic relatedness between the target segment and related articles as well as other segments. Then we revise three features proposed before. We improve the phraseness feature, for giving appropriate scores for long titles. Meanwhile, we combine the features SimPF and Embedding-vector to enhance the efficiency and rationality. We use Wikipedia articles for experimental evaluations, in which a large number of article segments are titled manually, and a great number of articles lack detailed segment titles. We evaluate scoring functions by where hidden original segment titles are ranked, through precision@K. Through rigorous evaluations, we show an optimum combination of the features.
{"title":"Selecting Article Segment Titles Based on Keyphrase Features and Semantic Relatedness","authors":"Yuming Guo, M. Iwaihara","doi":"10.1109/IIAI-AAI.2018.00034","DOIUrl":"https://doi.org/10.1109/IIAI-AAI.2018.00034","url":null,"abstract":"Nowadays people can find almost all kinds of information they want from the Internet. However, in most cases, users are not willing to find their target among segment among long paragraphs, by spending much time browsing texts. Existing work on topic labeling works effectively and performs well on document categorization, but inadequate for granularity of detailed contents. Thus we propose a method for selecting titles for segments in long documents. We analyze the characteristics of high quality titles for article segments, from the aspect of semantic relatedness between the target segment and related articles as well as other segments. Then we revise three features proposed before. We improve the phraseness feature, for giving appropriate scores for long titles. Meanwhile, we combine the features SimPF and Embedding-vector to enhance the efficiency and rationality. We use Wikipedia articles for experimental evaluations, in which a large number of article segments are titled manually, and a great number of articles lack detailed segment titles. We evaluate scoring functions by where hidden original segment titles are ranked, through precision@K. Through rigorous evaluations, we show an optimum combination of the features.","PeriodicalId":309975,"journal":{"name":"2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123065509","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}
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