Assessing K-9 Teachers' Computational Thinking Skills

Many national curricula have incorporated computational thinking (CT) into compulsory education. Teacher ability to deliver the revised curriculum determines whether these new skills can be successfully integrated into teaching. Therefore, it is crucial to examine teacher readiness. This study measured Swedish K-9 teacher CT skills through a CT test validated by an expert review panel and a principal component analysis. Additionally, we engaged statistical analyses to examine the relationship between the teachers' background and their CT test scores, as well as their self-reported ability to teach CT. The result demonstrated the teachers' proficiency in different types of CT skills. Another finding revealed that the type of programming language mastered by teachers was associated with both their CT test score and self-reported ability to teach CT. This CT test can support teachers to identify specific areas for professional development and may facilitate the school management to plan teachers' competence training strategically.

Prediction of Lung Cancer using Ensemble Classifiers

Carcinoma detection from CT scan images is extremely necessary for numerous diagnostic and healing applications. Because of the excessive amount of information in CT scan images and blurred boundaries, tumor segmentation and class are extremely laborious. The intention is to categorize carcinoma into benign and malignant categories. In MR pictures, the number of facts is a lot for interpreting and evaluating manually. Over the previous few years, carcinoma detection in CT has grown to be a rising evaluation space in the area of the scientific imaging system. Correct detection of length and site of lung cancer performs a vital position in the designation of carcinoma. In this paper, we introduce a novel carcinoma detection methodology that helps in predicting the carcinoma from the CT scanned images. The methodology has 4 different stages, pre-processing the image data, segmentation, extracting features, and classification stage to categorize the benign and malignant. This work makes use of extraordinary models for detecting carcinoma in a CT test via way of means of constructing an ensemble classifier. Techniques proposed in the paper helped us achieve an accuracy of 85% using Ensemble-Classifier which showcases that model has the capability of predicting the malignant cases correctly. The ensemble classifier consists of 5 machine learning models like SVM, LR, MLP, decision tree, and KNN. The inevitable parameters like accuracy, recall, and precision is calculated to determine the accurate results of the classifier.

Effects of a Mobile Cloud-Based Learning System Using a P2P Reverse Engineering Approach on Enhancing Computational Thinking

<p>In each country, skills development in the 21^st century plays a crucial role in education at all levels. One of these skills is computational thinking (CT), which is the ability to systematically think and solve problems in daily life. This research aims to ascertain the benefits of using a mobile cloud-based learning system with a peer-to-peer reverse engineering approach in enhancing CT skill. In this study, a cloud-based learning system and learning management plans were employed as research instruments, and a CT test was used as a data gathering instrument. The data were analyzed using the mean and standard deviation, repeated-measures analysis of variance (ANOVA), and t-test. The findings suggested that the students’ scores were higher after using the cloud-based learning system, at a 0.05 level of significance. After performing the repeated-measures ANOVA, the students’ scores were significantly higher at a 0.05 level in each round of the CT test. At the same time, peer learning increased the positive attitude of the learners toward the system and promoted the effectiveness of working in a team. This study provides a better understanding of the current research trends in the field of CT education, the perception of students, and the learning strategies and technology involved in CT education.</p>

Large-Scale CT Inspection of Feed-Through EMI Filters for Space Application

This paper describes a project to develop and deploy a systematic screening methodology involving computed tomography (CT) to inspect a set of electromagnetic interference (EMI) filter components for a spacecraft application. The goal was to deploy the nondestructive CT test to replace the destructive test method typically deployed for such components. The paper describes the development of test criteria, fixturing, inspection process, and data analysis, including quantitative image analysis of voids and cracks. The initial results indicated that the parts would not pass the requirements established in the test design. A waiver was written to the project clarifying that if the parts were to be used in the assembly, they should be considered as simple conductors with EMI filtering capability viewed as an added benefit rather than a guaranteed design requirement.

Assessing Computational Thinking: Development and Validation of the Algorithmic Thinking Test for Adults

This study describes the development and validation process of a computational thinking (CT) test for adults. The team designed a set of items and explored a subset of those through a couple of qualitative pilots. Then, in order to provide validity evidence based on the test content, a team of 11 subject-matter experts coded the initial pool of items using two different systems of categories based on CT components and contents. Then the items were piloted on a sample of 289 participants, 137 experts in CT and 152 novices. After a series of confirmatory factor analyses, a unidimensional model that represents algorithmic thinking was adopted. After analyzing the psychometric quality of the 27 items, 20 of them with excellent reliability indices were finally selected for the test. Thus, this study provides a tool to evaluate adults’ CT: the Algorithmic Thinking Test for Adults (ATTA), which was developed according to psychometric standards. This article also reflects on the nature of CT as a construct.

Assessing Computational Thinking: Development and Validation of the Algorithmic Thinking Test for Adults

This study describes the development and validation process of a computational thinking (CT) test for adults. The team designed a set of items and explored a subset of those through a couple of qualitative pilots. Then, in order to provide validity evidence based on the test content, a team of 11 subject-matter experts coded the initial pool of items using two different systems of categories based on CT components and contents. Then the items were piloted on a sample of 289 participants, 137 experts in CT and 152 novices. After a series of confirmatory factor analyses, a unidimensional model that represents algorithmic thinking was adopted. After analyzing the psychometric quality of the 27 items, 20 of them with excellent reliability indices were finally selected for the test. Thus, this study provides a tool to evaluate adults’ CT: the Algorithmic Thinking Test for Adults (ATTA), which was developed according to psychometric standards. This article also reflects on the nature of CT as a construct.

Online Learning Using Block-based Programming to Foster Computational Thinking Abilities during the COVID-19 Pandemic

The COVID 19 pandemic has affected global education. In Thailand, all educational institutions temporarily closed to prevent the spread of COVID-19. However, teaching and learning still need to be continued. It is necessary to switch the learning activities to online learning. In this study, we designed online learning activities for developing computational thinking (CT) of students and carried out an experiment with 90 participants (first-year students enrolled in a Bachelor of Education Program in educational technology and communications at King Mongkut's University of Technology Thonburi). At the beginning of the experiment, all participants were asked to take a CT test to measure their CT. The test is consistent with other CT tests under validation. During the sessions, all participants were taught by Thunkable. After the experiment, they took the CT test again. The results show that by improving CT through block-based programming projects, students’ performance improved significantly. In conclusion, block-based programming and working in pairs are combinations that can potentially help students to perform better, in turn affecting their performance in projects.

Performance Testing of Hot Mix Asphalt Modified with Recycled Waste Plastic

Plastic pollution has become one of the major concerns in the world. Plastic waste is not biodegradable, which makes it difficult to manage waste plastic pollution. Recycling and reusing waste plastic is an effective way to manage plastic pollution. Because of the huge quantity of waste plastic released into the world, industries requiring a large amount of material, like the pavement industry, can reuse some of this mammoth volume of waste plastics. Similarly, the use of reclaimed asphalt pavement (RAP) has also become common practice to ensure sustainability. The use of recycled waste plastics and RAP in HMA mix can save material costs and conserve many pavement industries’ resources. To successfully modify HMA with RAP and waste plastic, the modified HMA should exhibit similar or better performance compared to conventional HMA. In this study, recycled waste plastic, linear low-density polyethylene (LLDPE), and RAP were added to conventional HMA, separately and together. The mechanical properties of conventional and modified HMA were examined and compared. The fatigue cracking resistance was measured with the IDEAL Cracking (IDEAL CT) test, and the Hamburg Wheel Tracking (HWT) test was conducted to investigate the rutting resistance of compacted HMA samples. The IDEAL CT test results showed that the cracking resistance was similar across plastic modified HMA and conventional HMA containing virgin aggregates. However, when 20% RAP aggregates were used in the HMA mix, the fatigue cracking resistance was found to be significantly lower in plastic modified HMA compared to conventional HMA. The rutting resistance from the HWT test at 20,000 passes was found to be similar in all conventional and modified HMA.

Prediction of Crack Resistance of LFSMA-13 with and without Anti-Rut Agent Using Parameters of FTIR Spectrum under Different Aging Degrees

This paper aims to better analyze the crack resistance of lignin fiber reinforced SMA-13 (LFSMA-13) asphalt mixtures, with and without polymer anti-rut agent (ARA), under different aging degrees. IDEAL-CT test and Fourier transform infrared (FTIR) spectroscopy were utilized to analyze the relationships between the crack resistance of LFSMA-13, with and without ARA, and the parameters of the FTIR spectrum of the asphalt extracted from the test samples. A convenient testing method to predict the anti-crack ability of the mixtures in a road was also derived in this study. The test samples were prepared using the specifications listed by AASHTO. The fracture formation work (Winitial) and cracking index (CTIndex) in the IDEAL-CT test were adopted to reflect the cracking ability of the asphalt mixtures in both the crack formation stage and the crack propagation stage. The peak areas of the FTIR spectrum were utilized to reveal the chemical properties of the asphalt material inside the SMA-13 asphalt mixtures, with and without ARA under different aging degrees. Grey correlation analysis was adopted to choose the most suitable FTIR spectrum parameters to derive the prediction models of Winitial and CTIndex under different aging degrees. After conducting a series of tests, the results showed that the aging process could well affect the crack resistance of the test samples and the peak areas of the asphalt extracted from the mixtures. The FTIR parameters selected from the grey correlation analysis could be used to well predict the anti-crack ability of the asphalt mixtures.