Competency assessment of short free text answers

The increased adoption of competency-based education has posed the need of an automated competency assessment. Most of the existing assisted assessment does not cater for competency assessment. The high percentage on the use of short free text answer as competency assessment shows that the need of the competency assisted assessment is urgent. This paper studies on the need and also review on existing assisted assessments focusing on short free text answer. A Node Link (NL) Scoring technique is proposed as an alternative automated solution to assess learners’ competency in short free text answers. Keyword: competency assessment; short free text answers; Node Link Scoring techniqu

Deep Learning Approach for cognitive competency assessment in Computer Programming subject

This research examines the competencies that are essential for an lecturer or instructor to evaluate the student based on automated assessments. The competencies are the skills, knowledge, abilities and behavior that are required to perform the task given, whether in a learning or a working environment. The significance of this research is that it will assist students who are having difficulty learning a Computer Programming Language course to identify their flaws using a Deep Learning Approach. As a result, higher education institutions have a problem with assessing students based on their competency level because; they still use manual assessment to mark the assessment. In order to measure intelligence, it is necessary to identify the cluster of abilities or skills of the type in which intelligence expresses itself. This grouping of skills and abilities referred to as "competency". Then, an automated assessment is a problem-solving activity in which the student and the computer interact with no other human intervention. This review focuses on collecting different techniques that have been used. In addition, the review finding shows the main gap that exists within the context of the studied areas, which contributes to our key research topic of interest

Systematic Literature Review on Ontology-based Indonesian Question Answering System

Question-Answering (QA) systems at the intersection of natural language processing, information retrieval, and knowledge representation aim to provide efficient responses to natural language queries. These systems have seen extensive development in English and languages like Indonesian present unique challenges and opportunities. This literature review paper delves into the state of ontology-based Indonesian QA systems, highlighting critical challenges. The first challenge lies in sentence understanding, variations, and complexity. Most systems rely on syntactic analysis and struggle to grasp sentence semantics. Complex sentences, especially in Indonesian, pose difficulties in parsing, semantic interpretation, and knowledge extraction. Addressing these linguistic intricacies is pivotal for accurate responses. Secondly, template-based SPARQL query construction, commonly used in Indonesian QA systems, suffers from semantic gaps and inflexibility. Advanced techniques like semantic matching algorithms and dynamic template generation can bridge these gaps and adapt to evolving ontologies. Thirdly, lexical gaps and ambiguity hinder QA systems. Bridging vocabulary mismatches between user queries and ontology labels remains a challenge. Strategies like synonym expansion, word embedding, and ontology enrichment must be explored further to overcome these challenges. Lastly, the review discusses the potential of developing multi-domain ontologies to broaden the knowledge coverage of QA systems. While this presents complex linguistic and ontological challenges, it offers the advantage of responding to various user queries across various domains. This literature review identifies crucial challenges in developing ontology-based Indonesian QA systems and suggests innovative approaches to address these challenges

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Impact of big data congestion in IT: an adaptive knowledge-based bayesian network

Recent progress on real-time systems are growing high in information technology which is showing importance in every single innovative field. Different applications in IT simultaneously produce the enormous measure of information that should be taken care of. In this paper, a novel algorithm of adaptive knowledge-based Bayesian network is proposed to deal with the impact of big data congestion in decision processing. A Bayesian system show is utilized to oversee learning arrangement toward all path for the basic leadership process. Information of Bayesian systems is routinely discharged as an ideal arrangement, where the examination work is to find a development that misuses a measurably inspired score. By and large, available information apparatuses manage this ideal arrangement by methods for normal hunt strategies. As it required enormous measure of information space, along these lines it is a tedious method that ought to be stayed away from. The circumstance ends up unequivocal once huge information include in hunting down ideal arrangement. A calculation is acquainted with achieve quicker preparing of ideal arrangement by constraining the pursuit information space. The proposed algorithm consists of recursive calculation intthe inquiry space. The outcome demonstrates that the ideal component of the proposed algorithm can deal with enormous information by processing time, and a higher level of expectation rates

Education: the quest for the lost wisdom in the maze of knowledge

A novel method to assess the complex Education process has been devised. The technique involves the quantification of Learning Outcome, which has hitherto been largely subjective and cumbersome despite the technological advance in learning and teaching aids. The new technique presents an objective assessment in a mathematical form. The approach is an innovated Metric Suit based on a measure of Entropy related to learning outcome. The ‘Information’ entropy is computed and used as a measure of Knowledge. Another learning outcome is the proper application of relevant knowledge termed ‘Wisdom’. Wisdom can also be measured using entropy computations. In this sense, entropy is related to the factor of disorder. The various parameters are represented by random variables. Because the amount of the required computations is very large, only the most effective of variables will be considered. The results obtained so far are encouraging. However, more tests on the proposed Metric Suit from various areas of application will further ascertain its robustness. Comprehensive tests and thorough analyses will provide a strong basis for evaluation judgment. The model treats the education process as a communications channel. The transfer of information between the sender and the recipient depends on the amount of uncertainty presented by each of the components that constitute the system. The computations of “Entropy” involve all the programs that constitute a discipline at university level

A review of techniques in automatic programming assessment for practical skill test

Computer programming ability is a challenging competency that requires several cognitive skills and extensive practice. The increased number of students enrolled in computer and engineering courses and also the increased of failure and drop rate in programming subject is the motivational factor to this research. Due to the importance of this skill, this paper intends to study the landscape of current scenario in assisted assessment for hands-on practical programming focusing on competency-based assessment. The Bloom Taxonomy is used as a competency-based assessment platform. The review showed to-date that there are several automatic assessments for programming skills. However, there is no common grading being applied. Thus, further research is required to propose an automatic assessment that grades the student achievement based on learning taxonomy such as Bloom Cognitive Competency model

An Efficient Classification of Emotions in Students\u27 Feedback using Deep Neural Network

Background and Objective: In both the corporate and academic worlds, the collection and analysis of feedback (product evaluation, social media debate, and student input) has long been a significant topic. The traditional approaches to collect student feedback focused on data collection and analysis via questionnaires. However, the student makes comments on social media sites that need to be looked at to improve educational standards at schools.Methods: The purpose of this work is to construct a deep neural network-based system to assess students\u27 feedback and emotions found in the reviews. Our approach applies a Deep Learning-based Bi-LSTM Model to a benchmark student input dataset. It would categorize students\u27 feedback about their instructors according to their emotional states, such as love, happiness, fury, and disdain.Results: The experimental findings demonstrate that the proposed approach outperforms both benchmark studies and state-of-the-art machine learning classifiers

Dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm

The advancements in electronic devices have increased the demand for the internet of things (IoT) based smart homes, where the connecting devices are growing at a rapid pace. Connected electronic devices are more common in smart buildings, smart cities, smart grids, and smart homes. The advancements in smart grid technologies have enabled to monitor every moment of energy consumption in smart buildings. The issue with smart devices is more energy consumption as compared to ordinary buildings. Due to smart cities and smart homes’ growth rates, the demand for efficient resource management is also growing day by day. Energy is a vital resource, and its production cost is very high. Due to that, scientists and researchers are working on optimizing energy usage, especially in smart cities, besides providing a comfortable environment. The central focus of this paper is on energy consumption optimization in smart buildings or smart homes. For the comfort index (thermal, visual, and air quality), we have used three parameters, i.e., Temperature (◦F), illumination (lx), and CO2 (ppm). The major problem with the previous methods in the literature is the static user parameters (Temperature, illumination, and CO2); when they (parameters) are assigned at the beginning, they cannot be changed. In this paper, the Alpha Beta filter has been used to predict the indoor Temperature, illumination, and air quality and remove noise from the data. We applied a deep extreme learning machine approach to predict the user parameters. We have used the Bat algorithm and fuzzy logic to optimize energy consumption and comfort index management. The predicted user parameters have improved the system’s overall performance in terms of ease of use of smart systems, energy consumption, and comfort index management. The comfort index after optimization remained near to 1, which proves the significance of the system. After optimization, the power consumption also reduced and stayed around the maximum of 15-18w