Reviewerly/exHarmony huggingface dataset:
Preliminary Performance Assessment on Ask4Summary’s Reading Methods for Summary Generation

Ask4Summary creates summary for students’ questions based on text-based learning materials. This study conducts a preliminary assessment on Ask4Summary’s performance in terms of generating summaries with different subsets of course materials (e.g., supplement academic papers in PDF only, notes and slides in Word and PowerPoint only, and everything the teacher provides for the students) read and processed by two reading methods: the built-in algorithm based on Python NLTK and AWS Comprehend Keyphrase Extraction and Syntax Analysis. The course materials of a graduate level Academic Writing in English course in an Asian university and twenty-six common questions that students may ask in the class are provided by the course instructor. Each of the questions are read via the two methods and Ask4Summary generates the summaries with the six different datasets created by: (1) Python NLTK reading the academic papers in PDF only; (2) Python NLTK reading notes and slides in Word and PowerPoint format only; (3) Python NLTK reading every course materials; (4) AWS Comprehend reading academic papers in PDF only; (5) AWS Comprehend reading notes and slides in Word and PowerPoint format only; and (6) AWS Comprehend reading every course materials. For the 312 queries (i.e., ask 26 questions in 6 datasets with 2 methods analyzing the questions) made, 117 queries successfully generated the summary, where only 2 of them were read by AWS Comprehend. Among the rest of 115 summaries, 67 of them are from the datasets created via the built-in algorithm and 48 are from the datasets created by AWS Comprehend.
Detecting Interlingual Errors: The Case of Prepositions

Prepositions pose a particular challenge for many language learners, in part because of their seemingly arbitrary usage with certain verbs that does not necessarily translate directly across languages. As a consequence, many preposition errors in writing can be attributed to a direct transfer from a writer’s native language. While research in scalable tooling for second-language writing assistance has largely focused on automated error detection and correction, relatively little attention has been given to explaining why the errors may have occurred. A system that can distinguish interlingual errors – arising from an over-literal translation from a writer’s native language – from non-interlingual errors could serve as an insightful tool for language learners and teachers alike. In this work, we demonstrate the feasibility of classifying English preposition errors produced by native speakers of Spanish as interlingual. We propose a corpus-based method that exploits translation probabilities to estimate the likelihood that a writer has translated word-for-word from Spanish to English. We then show that this method correlates well with human judgments on the interlingual status of preposition errors and can be a basis for developing a tool for explaining one key source of errors in second-language writing.
A GPT-Based Vocabulary Tutor

Students of a foreign language need help to study vocabulary usually housed in foreign language textbooks. This need can be supported by an artificial vocabulary learning assistant, i.e., software that can ask relevant questions and reliably evaluate the answers. In this work, we present an assistant based on a combination of traditional dialogue generation technology with the latest technology of Generative Pre-trained Transformers. Moreover, we test our software and get admirable results that encourage us to continue our research in this direction.
On an Integrated Assessment for the Students Within an Academic Consortium

The design of a good assessment is related to compliance with several requirements. A good design ensures the validity and correctness of the assessment process within education. This paper presents a modality of designing an assessment test in respect of the degree of difficulty of the items and the test in an integrated academic environment, meaning that the items used for assessment are collected at an academic consortium level and they can be used in an integrated way for all the similar faculties from the academic group of institutions. The presented model is composed of two main components: the item collector and the test generator. The generation of tests is made automatically, by using evolutionary algorithms (genetic-based) and machine learning (ML) methods. An implementation of the model for the genetic-based algorithm is also presented.
An Approach for Detecting Gaming the System Behavior in Programming Problem-Solving

In ITS, for students’ success in carrying out their activities, it is essential to provide scaffolding, such as hints and feedback. Although using the ITS has increased student engagement and effort in the classroom, some students have responded to the ITS’s support facilities with an inappropriate behavior called gaming the system. Thus, in this article, we explore the phenomenon of gaming the system behavior, studying underlying factors related to automatically detecting when students game the system. We aim to develop a model for detecting gaming the system behavior in a computer programming student during problem-solving activities. Particularly, we have had special attention on testing the influence of variables such as (i) the student’s level of belief and the system’s level of belief in the difficulty level of the problem in detecting gaming the system; and (ii) partial submission of the problem associated with complete submission, in program form, in the detection of system manipulation. To conduct the detector’s development, we developed an appropriate environment for data collection and preparation of these data to be used by supervised machine learning algorithms, allowing the detection of the behavior. The development of the detector involved training single and ensemble machine learning algorithms to classify the system’s gaming behavior and obtain models with the best accuracy, including exploring, evaluating, and comparing different algorithms. The results show, considering the average of all algorithm results concerning all measures, the XGBoost ensemble classifier achieved the best performance.
Using the ITS Components in Improving the Q-Learning Policy for Instructional Sequencing

In this paper, we aim to optimize the sequencing of learning activities using the Q-learning, a reinforcement learning method. The Q-learning agent decides each time which activity to propose to the student. The sequencing policy we propose is guided by the aim to improve efficiently the student knowledge state. Thus, the Q-learning learns a mapping of the student knowledge states to the optimal activity to perform in that state. In this paper, we tackle two main issues in implementing the Q-learning off-policy: the combinatorial explosion of the student knowledge states and the definition of the reward function allowing to improve efficiently the student knowledge state. We rely on the student model and the domain model to answer these two challenges. We carried out a study to evaluate the approach we propose on simulated students. We show that our approach is more efficient since it achieves better learning gain with fewer activities than a random policy or an expert based policy.
Synthesizing Didactic Explanatory Texts in Intelligent Tutoring Systems Based on the Information in Cognitive Maps

The paper deals with the problem of synthesizing texts that explain decisions of an Intelligent Tutoring System for the learner. Solving this problem is important for increasing human trust in the ITS decisions by finding effective arguments and synthesizing explanatory texts. We describe an approach to preparing and concentrating raw data that is similar to the process used for mapping. The texts explaining the system’s decisions are generated using the method of parametric maps, which are visualized as Cognitive Maps of Knowledge Diagnosis: the transition from particular maps to the combined map and the development of a system of arguments based on these data. We show the structure of an explanatory text and the accompanying visualization of a cognitive map. We demonstrate the explanation synthesis on the example of a graduate student in the course “Simulation modeling”. The explanation consists of the text and supporting visualization. Preliminary analysis has shown significant student interest in receiving explanations, containing verbal and non-verbal (cognitive maps) components, from the Intelligent Tutoring System about its decisions.
KEMMRL: Knowledge Extraction Model for Morphologically Rich Languages

There is a growing interest in automatic text processing and knowledge extraction from text repositories which often requires building new language resources and technologies. We present the KEMMRL model designed for the under-resourced but morphologically rich Croatian language. The proposed model uses natural language processing techniques, state-of-the-art deep learning algorithms and a rule-based approach to generate knowledge representations. The output of the newly developed HRtagger and HRparser methods in combination with the KEMMRL model is knowledge represented in the form of an ordered recursive hypergraph. Since the performance of KEMMRL is highly dependent on the applied deep learning methods, we evaluated them using hr500k reference corpus in the training and testing phase and manually designed out-of-domain Semantic Hypergraph Corpus (SemCro). The results of standard evaluation metrics showed that the HRtagger and HRparser achieved significantly better results than other state-of-the-art methods. These methods also showed the best results in measuring the structural similarity of hypergraphs, the highest average similarity to the manually annotated semantic hypergraphs and the number of semantic hyperedges correctly annotated by the model. The semantic hypergraph proved to be an ideal structure to capture and represent knowledge from more complex sentences without information loss. Researchers and developers of similar morphologically rich languages can customize and extend KEMMRL to their requirements. This article highlights the potential benefits of implementing the KEMMRL model into an Intelligent Tutoring System (ITS), and future research may focus on developing and testing such implementations.
Model-Based Support for Collaborative Concept Mapping in Open-ended Domains

This paper presents a concept of model-based support for collaborative learning using concept mapping, which is a tool for externalizing learners’ knowledge. The proposed support for this activity uses computational modeling of the process of constructing knowledge building. This paper first proposes cognitive models of individual and collaborative concept mapping based on the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture. We then discuss a method for supporting concept mapping in both individual and collaborative learning situations based on the proposed cognitive models.
Generating Pedagogical Questions to Help Students Learn

One of the important features of intelligent tutoring systems is providing feedback so that the learners who have problems can understand and fix their errors. Two popular approaches to implementing it are demonstrating worked examples and providing explanatory feedback about the errors the learner made. Pedagogical questions - small, specific questions aimed at the thought process required to make the correct conclusion are often overlooked. Generating pedagogical questions to help the learner is beneficial because answering questions stimulates information retrieval and thinking. It also gives the tutoring system detailed information about the learner’s misconceptions. However, creating systems of pedagogical questions manually requires a lot of effort, even in relatively simple domains. In this paper, we propose four kinds of pedagogical questions that can be generated from a formal description of the taught skill, provide examples of pedagogical questions, and discuss the advantages and disadvantages of their use.
Identification of Whatsapp Digital Evidence on Android Smartphones using The Android Backup APK (Application Package Kit) Downgrade Method

The use of WhatsApp for actions that lead to unlawful acts is a serious matter that needs to be proven in court. Android and the WhatsApp messaging application continue to update their features and security to provide maximum service and protection to its users, such as the WhatsApp database encryption using crypt14. With crypt14 encryption on the WhatsApp database, investigations of WhatsApp digital evidence against Electronic Evidence (BBE) require an acquisition and extraction method to identify artefacts relevant to digital evidence needs. The National Institute Standard Technology (NIST) reference methodology, from the collection, examination, and analysis to reporting stages, has become a widely used framework for digital forensics against BBE. The Android Backup Application Package Kit (APK) Downgrade method can decrypt the WhatsApp database crypt14 to become a solution that can be used in the framework of mobile forensics to answer the needs of investigations into certain criminal cases, including data that users have deleted. With the Cellebrite tools, the Android Backup Application Package Kit (APK) Downgrade method can identify approximately 651% more artefacts than the Android Backup and logical acquisition methods using the FinalData and MobilEdit tools.
Development of Robot Educational Tools Using LEGO DUPLO and Bread Board, and Robot Practice through Trial-and-Error Process

Robots are used in many industries, and it is important to educate students about robot technology. In order to utilize robots in industry, it is necessary to construct robot systems that combine arms, sensors, and other components. In addition, programming and sensor adjustment techniques are required to operate the robot. Therefore, we developed the educational tools using LEGO DUPLO and bread board that allow students to practice building robotic systems through a trial-and-error process. We report on the high educational effect of this tools in robot practice.
対面とオンラインによる企業内技術研修の満足度を規定する要因の分析

This study aims to evaluate how satisfaction of technical training courses in in-house education was influenced by comprehension, goal achievement and training design factors: difficulties, trainers′ instruction ways, training materials and training volumes. Covariance structure analysis was conducted on questionnaire answer data from approximately 16,000 participants of training courses, which were classified into lecture and practical training types in comparison of face-to-face classes with online classes. The analysis revealed that goal achievement had a significant effect on satisfaction, and comprehension had an indirect effect on satisfaction through goal achievement. The influence of the training design factors on satisfaction, comprehension and goal achievement differed significantly with training types. In particular, the difficulties showed no significant effect on comprehension in online lecture-type trainings. This indicates subjective difficulties of online class participants may be different from those of face-to-face class participants.
An Engineering Student′s Research for Differential Equations with Comprehensive Program-based Learning Guided by a Mathematics Lecturer in National Institute of Technology

In this paper, we describe an example of studying a partial differential equation by a student in a national institute of technology. Differential equation is important for scientific understanding various phenomena. However, it is not easy for students in lower grades to make full use of mathematics. Therefore, we attach importance to an engineering side of a problem and use relatively easy mathematical method. Moreover, this study is performed in environment equipped by the student, herself.
Advancement and Efficiency of Comprehensive PBL in Mechanical Engineering Education at National Institute of Technology

Project-Based Learning (PBL) is widely adopted in colleges of technology. Especially, comprehensive PBL including mechanical design and system development is considered to be effective. However, it essentially has problems such as long-term classes hours and excessive burden on teachers. In addition, many of the comprehensive PBL do not include process planning education, even though they are manufacturing education. In this study, we developed an advanced comprehensive PBL including process planning education, and proposed an educational method to implement it efficiently and intensively by efficient system development and online implementation. Furthermore, we implemented this in third- and fourth-year students of our college, and achieved an advanced and efficient comprehensive PBL. We also have shown that this program has a remarkable educational improvement effect compared to less specialized and limited PBL.
Engineering and Resource Science Education on the Investigation of the Activation Method of Formation Water

A purpose of the engineering and resource science education is to bring up human resources of effective utilizing natural resources, considering environmental conservation for making sustainable society. As seen in the description of the course of study, the promotion of ESD is regarded as important in elementary and secondary education. Japan ranks second, after Chile, in the production of iodine in the world, and formation water in Minami-Kanto gas fields is the biggest supply source in Japan. The formation water can be substantially used for science education. However, the activation method of the formation water has not been well investigated. In this study, we developed the following teaching materials using the formation water in Minami-Kanto gas fields:1) extraction of iodine from the formation water and 2) making polarizing plates using the extracted iodine. We found that 18 L of the formation water was required to perform two experiments for one class: preliminary experiments and educational practice for university students. The teaching material developed has the potential to create interest in students not only in science experiments but also in engineering and resource science.
Development in Math. and Science Education Project by High School-College Collaboration

We report on the research activities and exchange of views carried out in mathematics and science education project through high school-college collaboration from 2020 to 2021 during the COVID-19 crisis.
Designing and Evaluating a Project Management Course for Increasing Students′ Self-Confidence

We have developed a syllabus to inspire proficiency in project management by assigning students multiple small projects of increasing difficulty. This enables them to practice the knowledge they have learned outside of the classroom. After completing this syllabus for second-year students at Kinki University in FY2019, questionnaire responses showed that about 80% of the students became more enthusiastic about project management and gained confidence in utilizing what they had learned in the class. Furthermore, when we conducted this course in FY2020 and FY2021, the results were equivalent to those of FY2019.
Proposing a Continuous Spectral-Spatial Sparse Representation Model for Denoising Hyperspectral Images

Due to its high capability in acquiring spectral and spatial information, hyperspectral imaging technology has gained significant attention in remote sensing. However, in practice, it is impossible to avoid noise in hyper spectral images due to camera artifacts and the external environment during the acquisition and transmission process. The presence of noise in these images hinders the detection of subtle differences between different materials in the image. Therefore, it is crucial to minimize noise as much as possible before performing any analysis and interpretation. Removing noise from hyperspectral images is a crucial preprocessing step that enhances image quality for various applications, including object recognition and classification. The challenge arises when we need to remove additive white mean-spherohomogeneous Gaussian noise from the given image. Previous research has suggested that thinning the noise-free parts of the image can be effective in removing noise. This article aims to implement the method proposed in using a programming language. The method involves extracting intra-band structure and inter-band correlation while displaying the common tank and learning the common dictionary. In the continuous thin coding phase, the inter-band correlation is extracted to maintain the same structure and achieve spectrum continuity. In contrast, the intra-band structure is used to encode differences in the spatial structure of different bands. Furthermore, a joint dictionary training algorithm is used to obtain a dictionary that simultaneously describes the content of different bands. This ensures that the resulting dictionary preserves the inter-band correlations and enhances the noise-removal process.
A New Corpus of Lexical Substitution and Word Blend Errors: Probing the Semantic Structure of Lemma Access Failures

Models of lemma access in language production predict occasional mis-selection of lemmas linked to highly similar concepts (synonyms) and concepts standing in a set-superset relation (subsumatives). It is unclear, however, if such errors occur in spontaneous speech, and if they do, whether humans can detect them given their minimal impact on sentence meaning. This data report examines a large corpus of English spontaneous speech errors and documents a low but non-negligible occurrence of these categories. The existence of synonym and subsumative errors is documented in a larger open access data set that supports a range of new investigations of the semantic structure of lexical substitution and word blend speech errors.