Implementation and Evaluation on Automated E-Learning Contents Creation Based on Natural Language Processing Techniques

To the present day, the performance of microprocessors has progressed dramatically. Recently, almost all computer systems use reduced instruction set computer (RISC) architectures. However, about 30 years ago, complex instruction set computer (CISC) architectures were widely used for almost all computer systems. The advantages and successes of RISC architectures are attributable to their simplified structures. Conventional complex instruction set computer (CISC) architectures invariably included various and numerous instruction sets. Each instruction was able to execute a complicated multi-step operation. For that reason, the CISC architectures were useful in assembler-based programming environments and in systems with small amounts of memory. However, such complicated architectures prevent increases in clock frequency or a processor’s processing power. Therefore, RISC architectures—which use simple architectures based on single-step instruction sets—have been developed. The RISC architectures present advantages in terms of higher clock frequency, smaller implementation area, and lower power consumption than conventional complex instruction set computer (CISC) architectures. Observation of many examples reveals that, in circuit implementations, a simple structure is best to increase the overall performance. That principle is also applicable to programmable devices. If clock-by-clock reconfigurable devices are used, even a single instruction set computer (SISC) can be implemented onto them. A single instruction set computer is one in which a processor has only a single instruction. During production, various single instruction set computers are prepared: a single instruction set computer with an AND logic function, a single instruction set computer with an adder function, and so on. These processor units are implemented at necessary times and at necessary places of a programmable device. In CISC and RISC architectures, the hardware is fixed. Its operations are switched using software commands, as portrayed in Fig. 1(a). In contrast, in a single instruction set computer, the operation changes are executed by hardware reconfigurations, as shown in Figs. 1(b) and 1(c). Therefore, in a single instruction set computer, a processor with a certain function itself can be reconfigured to another processor with another function. The implementation of such single instruction set computers provides the following advantages under programmable device implementations. A single instruction set computer with the simplest architecture can operate at the highest clock frequency among all processor architectures. In RISC architectures, many selectors to change functions are implemented; such selectors have a certain delay. However, single instruction set computers

Occupational health and safety (OHS) issues might be considered one of the main considerations among European countries as many employees encounter dangerous workplace conditions during work. Therefore, decision makers in these countries have initiated related projects to reduce the number of occupational accidents. Turkey is one of those countries that training activities have become mandatory due to a current OHS legislation amendment. In every workplace there are some OHS risks. It is well known that taking required precautions prior to occupational accidents is much more efficient and effective than doing it subsequently. In fact, only 2 percent of these OHS risks are unpreventable, whereas the remaining risks can be prevented easily (Kilkis & Demir, 2012). In Turkey, every year approximately 77.000 occupational accidents occur. More than 1000 employees lose their lives, while more than 5.000 employees become permanently disabled due to these occupational accidents (Korkmaz & Avsalli, 2012). In the institution, where this study was conducted, approximately 30.000 employees work. OHS training in the institution consists of theoretical and practical parts. In the practical part, when the necessity of gathering thousands of employees in various training centers and duration of those trainings are considered, it is obvious that OHS training activities would cost much and could cause administrative problems for the institution. Hence, the institution has decided to deliver theoretical part of the OHS training online in order to cope with this issue. Purpose: In this study, an online learning module on OHS, which had been previously developed by one of the biggest public institutions in Turkey, was improved. As the process of implementing improved learning materials takes much time, only preliminary findings were shared. The purpose of this study is twofold: first, to share the results of the impact of previous learning materials and staff’s thoughts about them; and secondly, to discuss features of new learning materials and process of improving them. Theoretical Framework: As the theoretical framework, Ricketts’s (2015) nine suggestions pertaining how to write safety stories were chosen so as to help designers write digital stories related to OHS. Besides, Cinar’s (2012) experience-based e-course design model was used to facilitate the process of improving the learning materials. Method Formative research method of Reigeluth and Frick (1999) was adopted as the study’s methodology. Approximately 9070 employees currently working at the institution filled in the online survey on the LMS. Staff were asked to answer nine survey questions related to their experience, attitude and thoughts about OHS module delivered via online learning. It should be noted that this survey was developed and administered by the institution at the end of the training without proving its reliability and validity. Since the number of the participants were quite high, this scale was used just to give some idea about staff’s experience, thoughts and attitudes towards OHS training. In addition to nine close-ended questions in the survey, there was also one open-ended question at the end to obtain staff’s detailed suggestions regarding how to deliver online learning more effectively. In line with Cinar’s (2012) suggestions, design team visited the institution to gain the broader sense of organizational culture. Then, OHS legislations and previous learning materials were examined carefully by each member of the design team. In order to analyze current situation of OHS training in the institution, survey results and staff’s posts on the LMS discussion board were also examined. After the analysis, it was agreed to write safety stories and use formative evaluation activities. An LMS, which had been previously used by the institution, was decided to be used in order to deliver the learning materials developed with an e-learning software. Safety stories and learning materials were shown to a line manager of the institution and a field expert to obtain their feedbacks. Following this, the design team checked the learning materials and gave feedback to each other. In this way, bugs and spelling errors were fixed. Finally, two short tutorial videos were recorded with the purpose of aiding staff to get used to new learning materials. Expected Outcomes As for findings, in a nutshell, previous learning materials lacked engagement, interaction, visuality, authenticity, and formative evaluation. Some staff of the institution demanded face-to-face OHS training claiming it was such an important training activity that it could not be risked by delivering via online learning. Possibly due to lack of visuality, many of the staff reported learning materials as quite boring. Another finding from the analysis indicates that institution’s staff request the learning materials to be customized to their workplace to better understand authentic implementation of the legislation. That is to say, rather than being generic to all institutional staff, they want it to encompass specific examples for each job title. Another one of the most prevalent complaints about the training was about the legislation’s applicability in workplaces. Personnel do not believe managers know regulations and are eager to implement those in the institution. In addition, some personnel expressed that an evaluation is necessary to be able to monitor to what extent the staff have learnt from the learning materials. As it is aimed to improve previous learning materials, it is expected to raise the awareness of OHS to reduce the number of occupational accidents in the institution. In addition, the new learning materials consist of different activities to enrich learning process, which can also be expected to contribute to interaction and engagement. By doing this, the legislations could be learnt easier and more effectively. Not only do improved learning materials contribute to learning process in a way of taking courses such as drag and drop activities, they might also promote transformative learning with safety stories to adapt the legislations to the staff’s workplaces. Finally, we expect to make contribution to the OHS field in order to improve training activities in different workplaces.

The single or one instruction set computer (OISC, pronounced, “whisk”) is the penultimate reduced instruction set computer (RISC)1. In OISC, the instruction set consists of one instruction, and then by the orthogonality of the instruction along with composition, a complete set of operations is synthesized. This approach is completely opposed to a complex instruction set computer (CISC), which incorporates many complex instructions as microprograms within the processor.

One of the main problems enterprises face today is the bulk of data derived from various resources. Furthermore, the growth of technology and sciences has greatly influenced the area of management and decision-making procedures, and has dramatically changed the decision-making processes in different levels, both quantitatively and qualitatively. Knowledge management plays a vital role in supporting enterprise learning, since it facilitates the effective collective intellect of the enterprise. Question Answering (QA) system is playing an important role in current search engine optimization. Natural language processing technique is mostly implemented in QA system for asking user's question and several steps are also followed for conversion of questions to query form for getting an exact answer. Query languages have complex syntax, requiring a good understanding of the representation schema, including knowledge of details like namespaces, class and property names. In this research we proposed an initial model to implement Conceptual Question Answering and Automatic Information Inferences for the enterprise's operational knowledge management in ontology-based learning organization.

Question answering (QA) system aims at retrieving precise information from a large collection of documents against a query.This paper describes the architecture of a Natural Language Question Answering (NLQA) system for a specific domain based on the ontological information, a step towards semantic web question answering.The proposed architecture defines four basic modules suitable for enhancing current QA capabilities with the ability of processing complex questions.The first module was the question processing, which analyses and classifies the question and also reformulates the user query.The second module allows the process of retrieving the relevant documents.The next module processes the retrieved documents, and the last module performs the extraction and generation of a response.Natural language processing techniques are used for processing the question and documents and also for answer extraction.Ontology and domain knowledge are used for reformulating queries and identifying the relations.The aim of the system is to generate short and specific answer to the question that is asked in the natural language in a specific domain.We have achieved 94 % accuracy of natural language question answering in our implementation.

This research was conducted during the COVID-19 pandemic where all schools used an online learning system or learning at home. As in MI Nidhomiyah Sentul, we took samples from fiqh subjects and the learning strategy used by fiqh teachers was inquiry learning strategy. What is done is by providing learning materials in the form of reading texts on textbooks or learning videos made by the teacher, after that students are asked to understand the content in the material by means of voicenotes, students do question and answer among their friends/discussions after all the teachers give assignments. to students. The impact of the changing learning system has resulted in teachers, students and parents experiencing their respective difficulties. Such as inadequate facilities for online learning, lack of internet network, parents who do not understand online learning and learning materials, and so on. This study uses qualitative research methods data obtained through interviews.
 
 Keywords:Covid-19 pandemic, learning strategies, impact

Question-Answering (QA) system is improved version of search engines or can be stated as specific Search engines because search engines deliver data in the form of URLs whereas Question-Answering system produces direct answer to the query asked. That's why more focus has been put on developing better Question Answering systems that could respond to large variety of user query. Semantic Web Technology has aided to the development of such systems from past many years because it could add meaning to the query being asked. Such systems are more efficient than the systems developed in other languages. In this paper, we have proposed a QA system of JAVA Programming Language, where knowledge will be represented in the form of Ontology and based on relations derived from domain knowledge to obtained final answer. Information present in Ontology is represented in the form of tables to feed in knowledge base. It combines the techniques of Natural Language Processing and Semantic Web.

E-learning is a response to the new educational needs of society and an important development in information and communication technologies because it represents the future of the teaching and learning processes. However, this trend presents many challenges, such as the processing of online forums which generate a huge number of messages with an unordered structure and a great variety of topics. These forums provide an excellent platform for learning and connecting students of a subject but the difficulty of following and searching the vast volume of information that they generate may be counterproductive. The main goal of this paper is to review the approaches and techniques related to online courses in order to present a set of learning analytics techniques and a general architecture that solve the main challenges found in the state of the art by managing them in a more efficient way: 1) efficient tracking and monitoring of forums generated; 2) design of effective search mechanisms for questions and answers in the forums; and 3) extraction of relevant key performance indicators with the objective of carrying out an efficient management of online forums. In our proposal, natural language processing, clustering, information retrieval, question answering, and data mining techniques will be used.

During the outbreak of COVID-19, schools at all levels were closed and face-to-face classes were cancelled. The stagnation was considered as the most impactful in human history. Surprisingly, the incident also revealed that the education sector was most vulnerable and yet resilient. Schools that have been implementing e-learning/online learning/blended or flexible learning prior to the pandemic mostly survived and were able to adapt to the new normal while those heavily relied on face-to-face classes delivery suffered the most. Readiness and responsiveness were the two key factors for educational institutions to survive. Readiness here included not merely the preparation of e-learning and teaching resources but also the stakeholders’ learning and teaching practices, habits and willingness. Schools’ timely responses and decision making on the changes of policies, curricula, timetabling as well as choosing the appropriate learning and teaching platforms were equally important. This study looked specifically into one of Hong Kong largest vocational and professional education and training (VPET) institute’s learning and teaching policy and students’ online learning experience in response to the outbreak of the pandemic. The institute also experienced the closed down of campuses and classes suspension due to the social unrest in Hong Kong in 2019. This paper first reviewed the contingency policy followed by a study on the implementation of online learning and teaching activities during the pandemic. A questionnaire survey was then conducted to 1,381 full-time and 193 part-time students on their online learning experience for the enhancement of the online learning and teaching practices and future planning. Results of the study showed that online learning experience varied amongst students from different learning programmes. This study also found that part-time students had a more satisfying online learning experience and were more adaptable to online learning than full-time students. Findings also revealed that clarity and sufficient guideline in using online learning platform and learning materials, enhancement of online assessment as well as guidance, support and feedback from teachers were the salient points to be addressed.

코로나19로 인한 대면접촉이 제한된 상황에서 교육부는 학습결손을 방지하기 위해 온라인 개학을 시행하여 전국의 초·중·고등학생들에게 원격수업을 통해 정규수업을 제공하였다. 특히, 원격수업 수업 유형 중 하나인 실시간 쌍방향 원격수업은 높은 잠재적 효과에도 불구하고 해당 수업 유형에 대한 낯섦과 설계자원의 부족이라는 한계로 인해. 코로나19 상황에서 교사들의 활용에 어려움이 있다. 하지만 실시간 쌍방향 원격수업이 가진 장점(실시간 피드백, 상호작용 촉진, 높은 교육효과성 등)으로 활용에 대한 요구 또한 높아지고 있는 상황이다. 이 연구의 목적은 교사들의 실시간 쌍방향 원격수업 활용을 지원하기 위한 교수자원을 제공하는 데 있다. 이 연구는 실제 원격수업 사례로부터 교수자원을 도출하기 위해 전국 시·도교육청이 공개하고 있는 코로나19 상황에서의 실시간 쌍방향 원격수업 사례를 수집하였다. 수집된 사례를 분석한 결과 사례별 다른 구성항목, 낮은 접근성과 내용 구체성 문제 등으로 개별 사례로는 기존 수업사례가 수행하던 교수자원으로서의 활용이 어렵다는 것을 확인하였다. 이에 연구자들은 수집된 사례들로부터 교사들이 실시간 쌍방향 원격수업 설계 및 운영에 활용할 수 있는 유의미한 교수자원을 도출하기 위해 의미연결망 분석을 진행하였다. 이 연구는 자연어 처리기법을 활용하여, 총 57개의 사례에 대한 텍스트마이닝을 수행하고 이어서 의미연결망 분석을 진행하였다. 의미연결망 분석 외에도 원격수업 전문성을 가진 연구자들이 분석 결과에 대한 질적 해석을 통해 실시간 쌍방향 원격수업의 활용을 위한 정보를 제시하고자 하였다. 이 연구는 원격수업 활용을 위한 교수자원 제공뿐만 아니라 코로나19 상황에서 실시간 쌍방항 원격수업의 활용 현황과 수준을 질적연구적 시각에서 설명한다. 또한 이 연구는 분석 결과에 근거하여 실시간 쌍방향 원격수업 활용의 시사점을 제언한다.This qualitative study conducted semantic network analysis to illustrate the key features of instructional resources for synchronous online courses. Recently, the Korean government decided to run full online classes for the current semester to prevent learning disruption of students amid COVID-19. Although the government encourages in-service teachers to design and implement synchronous online courses during the semester, both teachers’ unfamiliarity and a lack of relevant templates on synchronous online courses limit its integration in schools. However, the needs of synchronous online courses is growing due to its benefits (e.g. prompt feedback, high interaction, and robust effectiveness on learning). Considering such needs, this study is aimed to present the instructional resources that support synchronous online courses for secondary schools. The researchers gathered a total of 57 sample cases that demonstrate the current integration of synchronous online courses in secondary schools. To resolve several data-handling issues from the sampled cases (i.e. low data accessibility and concreteness, unorganized formats of cases), using natural language processing techniques, the researchers implemented semantic network analysis to visualize keywords from individual case into a network model. The goal of semantic network analysis is to better extract meaningful instructional resources for designing and implementing synchronous online courses. To corroborate the results of semantic network analysis, expert reviewers carefully reviewed the networks and then presented those qualitative findings. Also, this study findings suggest several implications for use of synchronous online courses in secondary schools.

This study is concerned with measurements of the characteristics of current use of digital computer instruction sets, and an analysis of those measurements to produce conclusions useful for future design evaluation. The results include conclusions about computer architectures and their implementations. In order to apportion the time spent by an executing program among the various system components, a model of high-performance computers is derived from instruction timing formulas, with compensation for pipeline and cache memory effects. The model is used to predict the performance of the IBM 370/168 and the Amdahl 470 V/6 on specific programs, and the results are verified by comparison with actual performance. Measurements are made and discussed for other instruction set architectures, including a current microprocessor (the INTEL 8080), a popular minicomputer (the DEC PDP11), and a pseudo-computer used as an intermediate step for PASCAL compilation or interpretation. Some predictive models are investigated. An improved mechanism for conditional branch prediction is proposed and simulated. The performance of a radically different implementation of the 370 is examined and its performance is predicted. Finally, some of the data gathered is used as the basis for a model of the instruction fetch process; that model is solved analytically. Design rules which should be followed to allow instruction sets to be implemented with high-performance processors are given. 27 figures, 24 tables.

The ISP (for Instruction Set Processor) notation was developed for a text [Bell& Newell, 1971] to precisely describe the programming level of a computer in terms of its Memory, Instruction Format, Data Types, Data Operations, Interpreting a Specific Instruction Set.

Scheduling satellite tasks requires intricate coordination of numerous interconnected activities, posing challenges for ground station operators who struggle to decipher autonomous decision-making systems without explanatory responses. This paper advocates for explainable artificial intelligence to address this gap, leveraging large language models and knowledge graphs to enhance the transparency of scheduling logic. The study explores how operators can retrieve relevant information through natural language queries by integrating language processing with knowledge graphs as core data structures. This approach allows for manual access to knowledge graphs, supplemented with textual explanations, and enables operators to explore different scheduling scenarios, thus improving system robustness and adaptability. Additionally, this paper analyzes how current knowledge graphs and natural language processing techniques are utilized and how they can enhance explainability in satellite scheduling. It investigates the modeling of satellite schedules and environmental data within the knowledge graph. The study introduces a novel approach for generating categorized queries, executable code for knowledge graph data extraction via language modeling, and providing explanatory answers. These are evaluated for correctness, validity, and linguistic quality, demonstrating the effectiveness of this approach.

The construction sector is notorious for the dichotomy between (a) its intensive use of information in its decision-making processes and (b) its limited access to, and insufficient use of, the pertinent information that is potentially available. In the context of the potential availability of valid information on the Web, we have developed a question-and-answer system, which enables construction practitioners to seek for information by posing questions in English or French, instead of entering a list of relevant keywords. Based on a modular systems approach using natural language, relevant answers to questions are selected and presented in a convivial way, thus improving and speeding up the classical Web search procedure. Our system consists of two main components: An intelligent robot that traverses a Web space and decides whether a page is construction oriented or not, and a question-answer (Q-A) component (comprising three modules) that uses a domain-specific thesaurus to extract meaningful parts of a question and to detect, process, and present paragraph passages extracted from relevant Web pages. The robot is trained on positive and unlabeled examples using a machine learning approach, while the Q-A component uses natural language processing techniques. In our experiments, we show that our automatically collected database consists of approximately 16% of noise, while the performance of the Q-A component is 65.45% in mean reciprocal rank.

We describe a novel architectural framework that allows software applications written for a given Complex Instruction Set Computer (CISC) to migrate to a different, higher performance architecture, without a significant investment on the part of the application user or developer. The framework provides a hardware mechanism for seamless switching between two instruction sets, resulting in a machine that enhances application performance while keeping the same program behavior (from a user perspective). High execution speed on migrated applications is achieved through automated translation of the object code of one machine to that of the other, using advanced global optimization and scheduling techniques. Issues affecting application behavior, such as precise exceptions, as well as self-modifying code, are addressed. Relaxation of full compatibility on these issues lead to further possible performance gains, encouraging applications to adopt the newer architecture.The proposed framework offers a path for moving from complex instruction set computers (CISCs) to newer architectures, such as reduced instruction set computers (RISCs), superscalars, or very long instruction word (VLIW) machines, while protecting the extensive economic investment represented by existing software. To illustrate our approach, we show how system code written (and compiled) for the IBM System/390 can yield fine-grain parallelism, as it is targeted for execution by a VLIW machine, with encouraging performance results.