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IJEE (International Journal of Engineering Education)
Published by Universitas Diponegoro
ISSN : -     EISSN : 25409808     DOI : -
Core Subject : Education, Engineering,
Education Engineering
The scope of journal covers all area in a wide variety of research areas in the field of engineering education. Some of research area such as (1) engineering epistemologies (what constitutes engineering thinking and knowledge), (2) engineering learning mechanisms (how learners develop knowledge and competencies), (3) engineering learning systems (instructional cultures and institutional practices), (4) engineering diversity and inclusiveness (how human diversity contributes to engineering processes and products), and (5) engineering assessment (development and use of assessment methods, instruments, and metrics).
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 6 Documents
 1 
Search results for , issue "Vol 3, No 2 (2021)" : 6 Documents clear
Gamifying Structural Analysis Assessments for First Year Architecture Engineering Students Amany Georgy Botros Micheal
International Journal of Engineering Education Vol 3, No 2 (2021)
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijee.3.2.%p

Abstract

Structural analysis modules delivered to first year in Architecture Engineering students is a real challenge for both teacher and students. Usually the assignments adopted in such modules are on campus  exams.  Such strategy may assess the capacity of the students to employ different sets of equations to solve a problem. However, this is not enough for a vivid education atmosphere. Transforming the assessments to digital simulations is the best solution for the education process. Digitalization is more appealing to nowadays students and it gives the teacher a wide spectrum of discussions without the hindrance of calculations time. The obstacle is that students at the first semester in engineering schools are not capable of dealing with solution packages. This paper presents a digital simulation of different structures using a simple tool based on closed-form equations. The tool is employed to generate different assessments that enable students to grasp the interaction between geometry, loading, boundary conditions, and internal forces and visualize the mutual effect. This cannot be achieved using classical assessments as it requires much calculation time. Furthermore, transforming this tool into an offline mobile app helps to gamify this tough module. This tool enables the students to work remotely in groups taking into account the Covid -19 pandemic social distancing. The students can submit a report where they demonstrate their understanding and conclusions. 
Optimized Active Contor Segmentation Model for Medical Image Compression: Introduction to Improved Marriage in Honey Bees Optimization Shabanam Shabbir Tamboli; Rajasekhar Butta; Rajakumar B R; Binu D; Abhishek Bhatt
International Journal of Engineering Education Vol 3, No 2 (2021)
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijee.3.2.%p

Abstract

Nowadays medical imaging systems tend to have the greatest impact on disease identification, diagnosis, and surgical preparation. At the same time, compression of image avoids data redundancy, reduces bandwidth, etc. This makes the system more peculiar in this field. Three main steps are being used in the proposed paradigm: (a) segmentation, (b) image compression, and (c) image decompression. Image segmentation is the first step, which is attained by the Optimized Active Contour Model (OACM). Using a new Modified marriage in honey bees optimization model (MMBO), the weighting factor and maximum iteration of ACM are fine-tuned. Thereby, the collected input image is differentiated or segmented into two: N-ROI and ROI, respectively. The ROI marked field will indeed be encoded using ISPIHT based lossy compression model, whereas the non-ROI area is encoded using DCT based lossy compression model. In terms of BSC, the outcomes from both the ISPIHT algorithm and the DCT model are merged and the compressed image is its output. Following that, the compressed image will then be subjected to image decompression. This will include bit-stream segregation, which will be processed separately for the ROI and non-ROI regions using both ISPIHT decoder and DCT based decomposition. This process results in the original image. Finally, a comparative evaluation is undergone between the proposed and the existing techniques in terms of PSNR, SSIM, and CR as well.
Analysis on Quality of Learning in e-learning Platforms Veeramanickam Murugappan M.R; Rajakumar B R; Binu D; Ramesh P.
International Journal of Engineering Education Vol 3, No 2 (2021)
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijee.3.2.%p

Abstract

This paper plans to introduce a model that concerns on e-learning quality management system under two phases: (i) Questionnaire preparation and (ii) Predicting the impact of e-learning quality. In order to analyze the quality of learning in e-learning platform, initially, the questionnaire will be prepared with respect to various drivers such as (i) Degree of flexibility and adaptability, (ii) Degree of supportability (students and staffs) (iii) Staff qualification and experience, (iv) Performance assessment and (v) Learner’s interest. The first driver includes factors like learner control, learner activity, motivation and feedback. The second driver includes factors like technical skills, cost and technical crisis and internet access. The third driver includes factors like awareness of new technology, whether the team includes instructional designers, multimedia procedures and so on. The fourth driver (Performance assessment) includes the impact of performance evaluation by means of Artificial Intelligence (AI) methods. The fifth driver includes factors like course materials, gaming and learners self interest. The prepared questionnaire is distributed to different age group people and are demanded to fill up the precise information as much as possible. These responses from the people are then taken for analysis purpose. In this research work, the analysis is carried out based on SEM analysis, which is a way to identify the learning quality in e-learning platform.
Hybrid Optimized LMMSE based Channel Estimation with Low Power Trellis Coded Modulation Neeta Nitin Thune; Senthil Kumaran V N; Ramakrishna Guttula; Rajakumar B R; GONDHI NAVABHARAT REDDY; Binu D
International Journal of Engineering Education Vol 3, No 2 (2021)
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijee.3.2.%p

Abstract

In the wireless channel environment, the data transmission faces many safety problems and power transmission loss. This results with the negative impacts while precise channel estimation. Furthermore, the existing channel estimation (CE) model directly estimates the channel matrix; still, the accuracy and the high complexity may cause path loss. Alternate to the direct estimation in the channel matrix, the parameter estimation model is used to solve the above issues. Moreover, the training based CE including Linear Minimum Mean Square Error (LMMSE) and Least Square Error (LSE) are ubiquitous in certain wireless standards to reduce the Mean Square Error (MSE) among the estimated and original channel. Certain intelligent optimized techniques are introduced to optimize the channel. This paper intends to introduce a Trellis Coded Modulation (TCM) with hybrid optimization in LMMSE for the Optimal CE. Moth amalgamated Elephant Herding Optimization (MAEHO) algorithm is the proposed hybrid optimization. At last, the performance of the adopted model is computed over other existing schemes in terms of various measures.  
A Study on E-Learner’s Affective-State concerning the Course Complexity in Engineering Education Snehal Rahul Rathi; Yogesh D. Deshpande
International Journal of Engineering Education Vol 3, No 2 (2021)
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijee.3.2.%p

Abstract

Affective states in learning have gained immense attention in education. The precise affective states prediction can increase the learning gain by adapting targeted interventions which are able to adjust the changes in individual affective states of students. Several techniques are devised for predicting the affective states considering audio, video and bio sensors, but the system that relied on analyzing audio, video cannot certify anonymity and are subjected to privacy problems. This paper devises a novel strategy, namely Rider Squirrel Search Algorithm-based Deep Long Short Term Memory (RiderSSA-based Deep LSTM) for affective state prediction. The training of Deep LSTM is done using proposed RiderSSA. Here, the RiderSSA-based Deep LSTM effectively predicts the affective states like confusion, engagement, frustration, Anger, Happiness, disgust, boredom, surprise, and so on. In addition, the learning styles are predicted based on the extracted features using Rider Neural Network (RideNN) by which the Felder Silverman Learning Style Model (FSLSM) is considered. Here, the RideNN classifies the learners. Finally, the course ID, student ID, affective state, learning style, and the exam score are taken as output data to determine the correlative study. The proposed RiderSSA-based Deep LSTM provided superior performance in contrast to other techniques with highest accuracy of 0.962 and highest correlation of 0.406 respectively.
An observer based robust state feedback control for active suspension system with adaptive compensation of nonlinear actuator Vahid Mokhtari; Ali Akbarzadeh Kalat; Naeimeh Fakhr Shamloo
International Journal of Engineering Education Vol 3, No 2 (2021)
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijee.3.2.%p

Abstract

In this paper, an observer based robust state feedback control strategy is proposed for a car suspension system with nonlinear actuator in order to moderate vertical motion of the body. At first, an adaptive inverse control is developed to overcome the nonlinearity of the actuator. Secondly, to cope with unavailability of some states of the system, a velocity observer is employed. Afterward, an observer based robust state feedback control is suggested for the system and robust stability of the overall control system is proved by Lyapunov theory. Efficiency and robustness of the proposed method is shown by comparing simulation results for the active and passive suspension system in the same road conditions.

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