Search results for: conventional learning

Authors: Denise Levy, Helen J. Khoury

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Nuclear technology is a controversial issue among a great share of the Brazilian population. Misinformation and common wrong beliefs confuse public’s perceptions and the scientific community is expected to offer a wider perspective on the benefits and risks resulting from ionizing radiation in everyday life. Attentive to the need of new approaches between science and society, the Nuclear Energy Museum, in northeast Brazil, is an initiative created to communicate the growing impact of the beneficial applications of nuclear technology in medicine, industry, agriculture and electric power generation. Providing accessible scientific information, the museum offers a rich learning environment, making use of different educational strategies, such as films, interactive panels and multimedia learning tools, which not only increase the enjoyment of visitors, but also maximize their learning potential. Developed according to modern active learning instructional strategies, multimedia materials are designed to present the increasingly role of nuclear science in modern life, transforming science education into a meaningful learning experience. In year 2016, nine different interactive computer-based activities were developed, presenting curiosities about ionizing radiation in different landmarks around the world, such as radiocarbon dating works in Egypt, nuclear power generation in France and X-radiography of famous paintings in Italy. Feedback surveys have reported a high level of visitors’ satisfaction, proving the high quality experience in learning nuclear science at the museum. The Nuclear Energy Museum is the first and, up to the present time, the only permanent museum in Brazil devoted entirely to nuclear science.

Keywords: Nuclear technology, multimedia learning tools, science museum, society and education.

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Authors: Raj Kumar Rajak, Bharat Mishra

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Bio-batteries represent an entirely new long-term, reasonable, reachable, and eco-friendly approach to generation of sustainable energy. In the present experimental work, we have studied the effect of the generation of power by bio-battery using different electrode pairs. The tests show that it is possible to generate electricity using cow’s urine as an electrolyte. C-Mg electrode pair shows maximum Voltage and Short Circuit Current (SCC), while C-Zn electrode pair shows less Open Circuit Voltage (OCV) and SCC. By the studies of cow urine and different electrodes, it is found that C-Zn electrode battery is more economical. The cow urine battery with C-Zn electrode provides maximum power (707.4 mW) and durability (up to 145 h). This result shows that the bio-batteries have the potency to full fill the need of electricity demand for lower energy equipment.

Keywords: Bio-batteries, cow’s urine, electrodes, non-conventional.

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Authors: Jin-Lung Guan, Jyh-Cherng Gu, Chun-Wei Huang, Hsin-Hung Chang

Abstract:

This investigation develops a revisable method for estimating the estimate value of equivalent 10 Hz voltage flicker (DV₁₀) of a DC Electric Arc Furnace (EAF). This study also discusses three 161kV DC EAFs by field measurement, with those results indicating that the estimated DV₁₀ value is significantly smaller than the survey value. The key point is that the conventional means of estimating DV₁₀ is inappropriate. There is a main cause as the assumed Q_max is too small.

Although DC EAF is regularly operated in a constant MVA mode, the reactive power variation in the Main Transformer (MT) is more significant than that in the Furnace Transformer (FT). A substantial difference exists between estimated maximum reactive power fluctuation (DQ_max) and the survey value from actual DC EAF operations. However, this study proposes a revisable method that can obtain a more accurate DV₁₀ estimate than the conventional method.

Keywords: Voltage Flicker, dc EAF, Estimate Value, DV10.

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Authors: Prariyada Theptat, Sumaeth Chavadej, John F. Scamehorn

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One of the most challenges for hard surface cleaning product is to get rid of soap scum, a filmy sticky layer in the bathroom. The deposits of soap scum can be removed by using a proper surfactant solution with chelating agent. Unfortunately, the conventional chelating agent, ethylenediamine tetraacetic acid (EDTA), has low biodegradability, which can be tolerance in water resources and harmful to aquatic animal and microorganism. In this study, two biodegradable chelating agents, ethylenediamine disuccinic acid (EDDS) and glutamic acid diacetic acid (GLDA) were introduced as a replacement of EDTA. The result shows that using GLDA with amphoteric surfactant gave the highest equilibrium solubility of soap scum.

Keywords: Biodegradable chelating agent, EDDS, GLDA, Soap scum.

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Authors: Andreas D. Jansson

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The availability of inexpensive, yet competent hardware allows for increased level of automation and self-optimization in the context of Industry 4.0. However, such agents require high quality information about their surroundings along with a robust strategy for collision avoidance, as they may cause expensive damage to equipment or other agents otherwise. Manually defining a strategy to cover all possibilities is both time-consuming and counter-productive given the capabilities of modern hardware. This paper explores the idea of a model-free self-optimizing obstacle avoidance strategy for multiple autonomous agents in a simulated dynamic environment using the Q-learning algorithm.

Keywords: Autonomous vehicles, industry 4.0, multi-agent system, obstacle avoidance, Q-learning, simulation.

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Authors: Petar Halachev

Abstract:

Forecasting the values of the indicators, which characterize the effectiveness of performance of organizations is of great importance for their successful development. Such forecasting is necessary in order to assess the current state and to foresee future developments, so that measures to improve the organization-s activity could be undertaken in time. The article presents an overview of the applied mathematical and statistical methods for developing forecasts. Special attention is paid to artificial neural networks as a forecasting tool. Their strengths and weaknesses are analyzed and a synopsis is made of the application of artificial neural networks in the field of forecasting of the values of different education efficiency indicators. A method of evaluation of the activity of universities using the Balanced Scorecard is proposed and Key Performance Indicators for assessment of e-learning are selected. Resulting indicators for the evaluation of efficiency of the activity are proposed. An artificial neural network is constructed and applied in the forecasting of the values of indicators for e-learning efficiency on the basis of the KPI values.

Keywords: artificial neural network, balanced scorecard, e-learning

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Authors: Edison Muzenda, Ayo S Afolabi

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This study investigated the effect of oxygen and micro-cracking on the flotation of low grade nickel sulphide ore. The ore treated contained serpentine minerals which have a history of being difficult to process efficiently. The use of oxygen as a bubbling gas has been noted to be effective because it increases the pulp potential. The desired effect of micro cracking the ore is that the nickel sulphide minerals will become activated and this activation will render these minerals more susceptible to react with potassium amyl xanthate collectors, resulting in a higher recovery of nickel and hinder the recovery of other undesired minerals contained in the ore. Higher nickel recoveries were obtained when pure oxygen was used as a bubbling gas rather than the conventional air. Microwave cracking favored the recovery of nickel.

Keywords: Flotation, Conventional air, Oven micro-cracking, Recovery.

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Authors: Varun Gupta, Ankit Pandey

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Crude oil market is an immensely complex and dynamic environment and thus the task of predicting changes in such an environment becomes challenging with regards to its accuracy. A number of approaches have been adopted to take on that challenge and machine learning has been at the core in many of them. There are plenty of examples of algorithms based on machine learning yielding satisfactory results for such type of prediction. In this paper, we have tried to predict crude oil prices using Long Short-Term Memory (LSTM) based recurrent neural networks. We have tried to experiment with different types of models using different epochs, lookbacks and other tuning methods. The results obtained are promising and presented a reasonably accurate prediction for the price of crude oil in near future.

Keywords: Crude oil price prediction, deep learning, LSTM, recurrent neural networks.

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Authors: Zoja Raud, Valery Vodovozov

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Modelling and simulation provide effective way to acquire engineering experience. An active approach to modelling and simulation proposed in the paper involves, beside the compulsory part directed by the traditional step-by-step instructions, the new optional part basing on the human’s habits to design thus stimulating the efforts towards success in active learning. Computer exercises as a part of engineering curriculum incorporate a set of effective activities. In addition to the knowledge acquired in theoretical training, the described educational arrangement helps to develop problem solutions, computation skills, and experimentation performance along with enhancement of practical experience and qualification.

Keywords: Modelling, simulation, engineering education, electronics, active learning.

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Authors: Hsin-Yi Huang, Ming-Sheng Liu, Jiun-Yan Shiau

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Planning the order picking lists for warehouses to achieve some operational performances is a significant challenge when the costs associated with logistics are relatively high, and it is especially important in e-commerce era. Nowadays, many order planning techniques employ supervised machine learning algorithms. However, to define features for supervised machine learning algorithms is not a simple task. Against this background, we consider whether unsupervised algorithms can enhance the planning of order-picking lists. A double zone picking approach, which is based on using clustering algorithms twice, is developed. A simplified example is given to demonstrate the merit of our approach.

Keywords: order picking, warehouse, clustering, unsupervised learning

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Authors: Elham Shirvani–Ghadikolaei, Seyed Mahdi Sajjadi

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State of the art technology has the tremendous impact on our life, in this situation education system have been influenced as well as. In this paper, tried to compare two space of learning text and hypertext with each other, and some challenges of using hypertext in religious education. Regarding the fact that, hypertext is an undeniable part of learning in this world and it has highly beneficial for the education process from class to office and home. In this paper tried to solve this question: the consequences and challenges of applying hypertext in religious education. Also, the consequences of this survey demonstrate the role of curriculum designer and planner of education to solve this problem.

Keywords: Hyper-textual, education, religious text, religious education.

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Authors: Sean Paulsen, Michael Casey

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In this work, we present a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general understanding of the temporal and spatial dynamics of human auditory cortex during music listening. Our pretraining results are the first to suggest a synergistic effect of multitask training on fMRI data. Second, we finetune the pretrained models and train additional fresh models on a supervised fMRI classification task. We observe significantly improved accuracy on held-out runs with the finetuned models, which demonstrates the ability of our pretraining tasks to facilitate transfer learning. This work contributes to the growing body of literature on transformer architectures for pretraining and transfer learning with fMRI data, and serves as a proof of concept for our pretraining tasks and multitask pretraining on fMRI data.

Keywords: Transfer learning, fMRI, self-supervised, brain decoding, transformer, multitask training.

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Authors: Motlhale Judicial Sebatana, Washington Takawira Dudu

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This study investigated how one beginner Physical Sciences teacher implemented Problem-Based Learning (PBL) strategy in the teaching and learning of Particulate Nature of Matter (PNM) in the Grade 10 classroom. PBL was implemented to explore how it can promote a 21st-century skill of creativity and enhance understanding of PNM. This study was guided by theoretical framework of Social Interdependence Theory (SIT). This exploratory qualitative case study was conveniently conducted in the North West province, South Africa, where one Physical Sciences teacher was purposefully sampled. A self-developed open-ended questionnaire, portfolio and individual semi-structured interview were used as the methods of generating data for this study. The results show that the participant of this study had no prior knowledge of utilising PBL in the teaching and learning of PNM before the Teacher Professional Development (TPD) programme, no knowledge of creativity as a 21st-century skill, and a successful PBL implementation post TPD to promote creativity.

Keywords: Beginner teachers, physical sciences teachers, problem-based learning, 21st-century skills, creativity skill, particulate nature of matter.

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Authors: Zhenguo Vincent Chia, Sheung Yan Simon Ng, Minkyu Je

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This paper presents CMOS Current Mode Logic (CML) circuits for a high speed Digital to Analog Converter (DAC) using standard CMOS 65nm process. The CML circuits have the propagation delay advantage over its conventional CMOS counterparts due to smaller output voltage swing and tunable bias current. The CML circuits proposed in this paper can achieve a maximum propagation delay of only 9.3ps, which can satisfy the stringent requirement for the 5 GHz high speed DAC application. Another advantage for CML circuits is its dynamic symmetry characteristic resulting in a reduction of an additional inverter. Simulation results show that the proposed CML circuits can operate from 1.08V to 1.3V with temperature ranging from -40 to +120°C.

Keywords: Conventional, Current Mode Logic, DAC, Decoder

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Authors: Dejanira Araiza-Illan, Tony J. Dodd

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A novel biologically inspired controller for the autonomous navigation of a mobile robot in an evasion task is proposed. The controller takes advantage of the environment by calculating a measure of danger and subsequently choosing the parameters of a reinforcement learning based decision process. Two different reinforcement learning algorithms were used: Qlearning and Sarsa (λ). Simulations show that selecting dynamic parameters reduce the time while executing the decision making process, so the robot can obtain a policy to succeed in an escaping task in a realistic time.

Keywords: Autonomous navigation, mobile robots, reinforcement learning.

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Authors: Norfishah Mat Rabi, Rosma Osman, Norziana Mat Rabi

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It is difficult for autistic children to mix with and be around with other people. Language difficulties are a problem that affects their social life. A lack of knowledge and ability in language are factors that greatly influence their behavior, and their ability to communicate and interact. Autistic children need to be assisted to improve their language abilities through the use of suitable learning resources. This study is conducted to identify weather graphic animation resources can help autistic children learn and use transitive verbs more effectively. The study was conducted in a rural secondary school in Penang, Malaysia. The research subject comprised of three autistic students ranging in age from 14 years to 16 years. The 14-year-old student is placed in A Class and two 16-year-old students placed in B Class. The class placement of the subjects is based on the diagnostic test results conducted by the teacher and not based on age. Data collection is done through observation and interviews for the duration of five weeks; with the researcher allocating 30 minutes for every learning activity carried out. The research finding shows that the subjects learn transitive verbs better using graphic animation compared to static pictures. It is hoped that this study will give a new perspective towards the learning processes of autistic children.

Keywords: Autistic, graphic animation, language learning, transitive verbs.

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Authors: Ali Sarhadi, Mohammad Reza Meybodi, Ali Yousefi

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Resource discovery is one of the chief services of a grid. A new approach to discover the provenances in grid through learning automata has been propounded in this article. The objective of the aforementioned resource-discovery service is to select the resource based upon the user-s applications and the mercantile yardsticks that is to say opting for an originator which can accomplish the user-s tasks in the most economic manner. This novel service is submitted in two phases. We proffered an applicationbased categorization by means of an intelligent nerve-prone plexus. The user in question sets his or her application as the input vector of the nerve-prone nexus. The output vector of the aforesaid network limns the appropriateness of any one of the resource for the presented executive procedure. The most scrimping option out of those put forward in the previous stage which can be coped with to fulfill the task in question is picked out. Te resource choice is carried out by means of the presented algorithm based upon the learning automata.

Keywords: Resource discovery, learning automata, neural network, economic policy

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Authors: Mackenzie Leake, Liyu Xia, Kamil Rocki, Wayne Imaino

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In the Hierarchical Temporal Memory (HTM) paradigm the effect of overlap between inputs on the activation of columns in the spatial pooler is studied. Numerical results suggest that similar inputs are represented by similar sets of columns and dissimilar inputs are represented by dissimilar sets of columns. It is shown that the spatial pooler produces these results under certain conditions for the connectivity and proximal thresholds. Following the discussion of the initialization of parameters for the thresholds, corresponding qualitative arguments about the learning dynamics of the spatial pooler are discussed.

Keywords: Hierarchical Temporal Memory, HTM, Learning Algorithms, Machine Learning, Spatial Pooler.

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Authors: Pradeep Kulshrestha, Maulana Ayoub Ali

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The level of penetration of Islamic banking products and services has recorded a reasonable growth at an exponential rate in many parts of the world. There are many factors which have contributed to this growth including, but not limited to the rapid growth of number of Muslims who are uncomfortable with the conventional ways of banking, interest and higher interest rates scheduled by conventional banks and financial institutions as well as the financial inclusion campaign conducted in many countries. The system is facing legal challenges which open the research fdoor for practitioners and academicians for the sake of finding out solutions to those challenges. This paper tries to investigate the development of the Islamic banking system in the United Kingdom (UK), Saudi Arabia, Malaysia, Iran, Kenya, Nigeria and Uganda in order to understand the modalities which have been employed to run an Islamic banking system in the aforementioned countries. The methodology which has been employed in doing this research paper is Doctrinal, of which legislations, policies and other legal tools have been carefully studied and analysed. Again, papers from academic journals, books and financial reports have been deeply analysed for the purpose of enriching the paper and come up with a tangible results. The paper found that in Asia, Malaysia has created the smoothest legal platform for Islamic banking system to work properly in the country. The United Kingdom has tried harder to smooth the banking system without affecting the conventional banking methods and without favouring the operations of Islamic banks. It also tries harder to make UK as an Islamic banking and finance hub in Europe. The entire banking system in Iran is Islamic, while Nigeria has undergone several legal reforms to suit Islamic banking system in the country. Kenya and Uganda are at a different pace in making Islamic Banking system work alongside the conventional banking system.  

Keywords: Shariah, Islamic banking, law, alternative banking.

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Authors: Ons Sassi, Wahiba Ramdane Cherif-Khettaf, Ammar Oulamara

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In this paper, we consider the vehicle routing problem with mixed fleet of conventional and heterogenous electric vehicles and time dependent charging costs, denoted VRP-HFCC, in which a set of geographically scattered customers have to be served by a mixed fleet of vehicles composed of a heterogenous fleet of Electric Vehicles (EVs), having different battery capacities and operating costs, and Conventional Vehicles (CVs). We include the possibility of charging EVs in the available charging stations during the routes in order to serve all customers. Each charging station offers charging service with a known technology of chargers and time dependent charging costs. Charging stations are also subject to operating time windows constraints. EVs are not necessarily compatible with all available charging technologies and a partial charging is allowed. Intermittent charging at the depot is also allowed provided that constraints related to the electricity grid are satisfied. The objective is to minimize the number of employed vehicles and then minimize the total travel and charging costs. In this study, we present a Mixed Integer Programming Model and develop a Charging Routing Heuristic and a Local Search Heuristic based on the Inject-Eject routine with different insertion methods. All heuristics are tested on real data instances.

Keywords: charging problem, electric vehicle, heuristics, local search, optimization, routing problem.

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Authors: Tsuyoshi Okita

Abstract:

For a given specific problem an efficient algorithm has been the matter of study. However, an alternative approach orthogonal to this approach comes out, which is called a reduction. In general for a given specific problem this reduction approach studies how to convert an original problem into subproblems. This paper proposes a formal modeling language to support this reduction approach in order to make a solver quickly. We show three examples from the wide area of learning problems. The benefit is a fast prototyping of algorithms for a given new problem. It is noted that our formal modeling language is not intend for providing an efficient notation for data mining application, but for facilitating a designer who develops solvers in machine learning.

Keywords: Formal language, statistical inference problem, reduction.

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Authors: Nicholas Milburn, Yu Liang, Dalei Wu

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Athlete performance scoring within the climbing domain presents interesting challenges as the sport does not have an objective way to assign skill. Assessing skill levels within any sport is valuable as it can be used to mark progress while training, and it can help an athlete choose appropriate climbs to attempt. Machine learning-based methods are popular for complex problems like this. The dataset available was composed of dynamic force data recorded during climbing; however, this dataset came with challenges such as data scarcity, imbalance, and it was temporally heterogeneous. Investigated solutions to these challenges include data augmentation, temporal normalization, conversion of time series to the spectral domain, and cross validation strategies. The investigated solutions to the classification problem included light weight machine classifiers KNN and SVM as well as the deep learning with CNN. The best performing model had an 80% accuracy. In conclusion, there seems to be enough information within climbing force data to accurately categorize climbers by skill.

Keywords: Classification, climbing, data imbalance, data scarcity, machine learning, time sequence.

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Authors: S. Bokka

Abstract:

Fiber filter membranes have a high surface area-to-volume ratio and high porosity making them ideal for various filtration and separation applications. Using the conventional filter membrane, a filtration efficiency of > 95% can be achieved. Specific applications such as air and fuel filtration require nearly 100% filtration efficiency, which is harder to achieve using conventional filter membranes. To achieve high filtration efficiencies additional costs are incurred due to increasing the cost of membrane and operating cost. Due to the simultaneous electrostatic attraction and mechanical capture, the electret filters have shown nearly 100% filtration efficiency. This article presents an overview of the charged filter membrane, its applications, and a discussion on factors contributing to increasing charge.

Keywords: Charged fiber membrane, piezoelectric materials, filtration, polymeric materials.

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Authors: Omar Alshehri, Vic Lally

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This paper examined the attitudes of using social media tools to support learning at a university in Saudi Arabia. Moreover, it investigated the students’ current usage of these tools and examined the barriers they could face during the use of social media tools in the education process. Participants in this study were 42 university students. A web-based survey was used to collect data for this study. The results indicate that all of the students were familiar with social media and had used at least one type of social media for learning. It was found out that all students had very positive attitudes towards the use of social media and welcomed using these tools as a supplementary to the curriculum. However, the results indicated that the major barriers to using these tools in learning were distraction, opposing Islamic religious teachings, privacy issues, and cyberbullying. The study recommended that this study could be replicated at other Saudi universities to investigate factors and barriers that might affect Saudi students’ attitudes toward using social media to support learning.

Keywords: Saudi Arabia, social media, benefits of social media use, barriers to social media use, higher education.

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: Crime prediction, machine learning, public safety, smart city.

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Authors: Hussam Elias, Ninovic Perez, Holger Hirsch

Abstract:

The completion of the EMC regulations worldwide is growing steadily as the usage of electronics in our daily lives is increasing more than ever. In this paper, we present a method to perform the final phase of Electromagnetic Compatibility (EMC) measurement and to reduce the required test time according to the norm EN 55032 by using a developed tool and the Conventional Neural Network (CNN). The neural network was trained using real EMC measurements which were performed in the Semi Anechoic Chamber (SAC) by CETECOM GmbH in Essen Germany. To implement our proposed method, we wrote software to perform the radiated electromagnetic interference (EMI) measurements and use the CNN to predict and determine the position of the turntable that meet the maximum radiation value.

Keywords: Conventional neural network, electromagnetic compatibility measurement, mean absolute error, position error.

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Authors: Zeineb Deymi-Gheriani

Abstract:

In the last two decades, one can clearly observe a boom of interest for e-learning and web-supported programs. However, one can also notice that many of these programs focus on the accumulation and delivery of content generally as a business industry with no much concern for theoretical underpinnings. The existing research, at least in online English language teaching (ELT), has demonstrated a lack of an effective online teaching pedagogy anchored in a well-defined theoretical framework. Hence, this paper comes as an attempt to present constructivism as one of the theoretical bases for the design of an effective online language teaching pedagogy which is at the same time technologically intelligent and theoretically informed to help envision how education can best take advantage of the information and communication technology (ICT) tools. The present paper discusses the key principles underlying constructivism, its implications for online language teaching design, as well as its limitations that should be avoided in the e-learning instructional design. Although the paper is theoretical in nature, essentially based on an extensive literature survey on constructivism, it does have practical illustrations from an action research conducted by the author both as an e-tutor of English using Moodle online educational platform at the Virtual University of Tunis (VUT) from 2007 up to 2010 and as a face-to-face (F2F) English teaching practitioner in the Professional Certificate of English Language Teaching Training (PCELT) at AMIDEAST, Tunisia (April-May, 2013).

Keywords: Active learning, constructivism, experiential learning, Piaget, Vygotsky.

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Authors: Sanmohan Krüger, Volker Krüger

Abstract:

Imitation learning is considered to be an effective way of teaching humanoid robots and action recognition is the key step to imitation learning. In this paper an online algorithm to recognize parametric actions with object context is presented. Objects are key instruments in understanding an action when there is uncertainty. Ambiguities arising in similar actions can be resolved with objectn context. We classify actions according to the changes they make to the object space. Actions that produce the same state change in the object movement space are classified to belong to the same class. This allow us to define several classes of actions where members of each class are connected with a semantic interpretation.

Keywords: Parametric actions, Action primitives, Hand gesture recognition, Imitation learning

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Authors: Azza Taha Zaki

Abstract:

Technology-enhanced teaching has been in the limelight since the 90s when educators started investigating and experimenting with using computers in the classroom as a means of building 21^st. century skills and motivating students. The concept of technology-enhanced strategies in education is kaleidoscopic! It has meant different things to different educators. For the purpose of this paper, however, it will be used to refer to the diverse technology-based strategies used to support and enrich the flipped learning process, in the classroom and outside. The paper will investigate how technology is put in the service of teaching and learning to improve the students’ learning experience as manifested in students’ attendance and engagement, achievement rates and finally, students’ projects at the end of the semester. The results will be supported by a student survey about relevant specific aspects of their learning experience in the modules in the study.

Keywords: Attendance, British University, Egypt, flipped, student achievement, student-centred, student engagement, students’ projects.

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Authors: Vilas P. Mahatme, K. K. Bhoyar

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Nowadays, education cannot be imagined without digital technologies. It broadens the horizons of teaching learning processes. Several universities are offering online courses. For evaluation purpose, e-examination systems are being widely adopted in academic environments. Multiple-choice tests are extremely popular. Moving away from traditional examinations to e-examination, Moodle as Learning Management Systems (LMS) is being used. Moodle logs every click that students make for attempting and navigational purposes in e-examination. Data mining has been applied in various domains including retail sales, bioinformatics. In recent years, there has been increasing interest in the use of data mining in e-learning environment. It has been applied to discover, extract, and evaluate parameters related to student’s learning performance. The combination of data mining and e-learning is still in its babyhood. Log data generated by the students during online examination can be used to discover knowledge with the help of data mining techniques. In web based applications, number of right and wrong answers of the test result is not sufficient to assess and evaluate the student’s performance. So, assessment techniques must be intelligent enough. If student cannot answer the question asked by the instructor then some easier question can be asked. Otherwise, more difficult question can be post on similar topic. To do so, it is necessary to identify difficulty level of the questions. Proposed work concentrate on the same issue. Data mining techniques in specific clustering is used in this work. This method decide difficulty levels of the question and categories them as tough, easy or moderate and later this will be served to the desire students based on their performance. Proposed experiment categories the question set and also group the students based on their performance in examination. This will help the instructor to guide the students more specifically. In short mined knowledge helps to support, guide, facilitate and enhance learning as a whole.

Keywords: Data mining, e-examination, e-learning, moodle.

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