Using process features to investigate scientific problem-solving in large-scale assessments

Introduction

This study investigates the process data from scientific inquiry tasks of fair tests [requiring test-takers to manipulate a target variable while keeping other(s) constant] and exhaustive tests (requiring test-takers to construct all combinations of given variables) in the National Assessment of Educational Progress program.

Methods

We identify significant associations between item scores and temporal features of preparation time, execution time, and mean execution time.

Results

Reflecting, respectively, durations of action planning and execution, and execution efficiency, these process features quantitatively differentiate the high- and low-performing students: in the fair tests, high-performing students tended to exhibit shorter execution time than low-performing ones, but in the exhaustive tests, they showed longer execution time; and in both types of tests, high-performing students had shorter mean execution time than low-performing ones.

Discussion

This study enriches process features reflecting scientific problem-solving process and competence and sheds important light on how to improve performance in large-scale, online delivered scientific inquiry tasks.

Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation

A major archetype of artificial intelligence is developing algorithms facilitating temporal efficiency and accuracy while boosting the generalization performance. Even with the latest developments in machine learning, a key limitation has been the inefficient feature extraction from the initial data, which is essential in performance optimization. Here, we introduce a feature extraction method inspired by energy-entropy relations of sensory cortical networks in the brain. Dubbed the brain-inspired cortex, the algorithm provides convergence to orthogonal features from streaming signals with superior computational efficiency while processing data in a compressed form. We demonstrate the performance of the new algorithm using artificially created complex data by comparing it with the commonly used traditional clustering algorithms, such as Birch, GMM, and K-means. While the data processing time is significantly reduced-seconds versus hours-encoding distortions remain essentially the same in the new algorithm, providing a basis for better generalization. Although we show herein the superior performance of the cortical coding model in clustering and vector quantization, it also provides potent implementation opportunities for machine learning fundamental components, such as reasoning, anomaly detection and classification in large scope applications, e.g., finance, cybersecurity, and healthcare.

Directionally-Enhanced Binary Multi-Objective Particle Swarm Optimisation for Load Balancing in Software Defined Networks

Various aspects of task execution load balancing of Internet of Things (IoTs) networks can be optimised using intelligent algorithms provided by software-defined networking (SDN). These load balancing aspects include makespan, energy consumption, and execution cost. While past studies have evaluated load balancing from one or two aspects, none has explored the possibility of simultaneously optimising all aspects, namely, reliability, energy, cost, and execution time. For the purposes of load balancing, implementing multi-objective optimisation (MOO) based on meta-heuristic searching algorithms requires assurances that the solution space will be thoroughly explored. Optimising load balancing provides not only decision makers with optimised solutions but a rich set of candidate solutions to choose from. Therefore, the purposes of this study were (1) to propose a joint mathematical formulation to solve load balancing challenges in cloud computing and (2) to propose two multi-objective particle swarm optimisation (MP) models; distance angle multi-objective particle swarm optimization (DAMP) and angle multi-objective particle swarm optimization (AMP). Unlike existing models that only use crowding distance as a criterion for solution selection, our MP models probabilistically combine both crowding distance and crowding angle. More specifically, we only selected solutions that had more than a 0.5 probability of higher crowding distance and higher angular distribution. In addition, binary variants of the approaches were generated based on transfer function, and they were denoted by binary DAMP (BDAMP) and binary AMP (BAMP). After using MOO mathematical functions to compare our models, BDAMP and BAMP, with state of the standard models, BMP, BDMP and BPSO, they were tested using the proposed load balancing model. Both tests proved that our DAMP and AMP models were far superior to the state of the art standard models, MP, crowding distance multi-objective particle swarm optimisation (DMP), and PSO. Therefore, this study enables the incorporation of meta-heuristic in the management layer of cloud networks.

Distance Motor Learning during the COVID-19 Induced Confinement: Video Feedback with a Pedagogical Activity Improves the Snatch Technique in Young Athletes

The purpose of the present study was to investigate which of two strategies, Video Feedback with Pedagogical Activity (VF-PA) or Video Feedback (VF), would be more beneficial for the remote error correction of the snatch weightlifting technique during the confinement period. Thirty-five school aged children with at least three months of weightlifting experience were randomized to one of three training conditions: VF-PA, VF or the Control group (CONT). Subjects underwent test sessions one week before (T0) and one day after (T1) a six-session training period and a retention test session a week later (T2). During each test session, the Kinovea version 0.8.15 software measured the kinematic parameters of the snatch performance. Following distance learning sessions (T1), the VF-PA improved various kinematic parameters (i.e., barbell horizontal displacements, maximum height, looping and symmetry) compared with T0 (p < 0.5; Cohen's d = 0.58-1.1). Most of these improvements were maintained during the retention test (T2) (p<0.01, Cohen's d = 1.2-1.3) when compared withT0. However, the VF group improved only twoparameters (i.e., barbell symmetry and horizontal displacement) at T1 (p < 0.05; Cohen's d = 0.9), which were not maintained at T2. Better horizontal displacement and looping values were registered during the retention test in the VF-PA group compared with theCONT group (p < 0.05, Cohen's d = 1.49-1.52). The present findings suggest combining video feedback with pedagogical activity during the pandemic induced online coaching or physical education to improve movement learning in school aged children.

A Fast and Scalable Workflow for SNPs Detection in Genome Sequences Using Hadoop Map-Reduce

Next generation sequencing (NGS) technologies produce a huge amount of biological data, which poses various issues such as requirements of high processing time and large memory. This research focuses on the detection of single nucleotide polymorphism (SNP) in genome sequences. Currently, SNPs detection algorithms face several issues, e.g., computational overhead cost, accuracy, and memory requirements. In this research, we propose a fast and scalable workflow that integrates Bowtie aligner with Hadoop based Heap SNP caller to improve the SNPs detection in genome sequences. The proposed workflow is validated through benchmark datasets obtained from publicly available web-portals, e.g., NCBI and DDBJ DRA. Extensive experiments have been performed and the results obtained are compared with Bowtie and BWA aligner in the alignment phase, while compared with GATK, FaSD, SparkGA, Halvade, and Heap in SNP calling phase. Experimental results analysis shows that the proposed workflow outperforms existing frameworks e.g., GATK, FaSD, Heap integrated with BWA and Bowtie aligners, SparkGA, and Halvade. The proposed framework achieved 22.46% more efficient F-score and 99.80% consistent accuracy on average. More, comparatively 0.21% mean higher accuracy is achieved. Moreover, SNP mining has also been performed to identify specific regions in genome sequences. All the frameworks are implemented with the default configuration of memory management. The observations show that all workflows have approximately same memory requirement. In the future, it is intended to graphically show the mined SNPs for user-friendly interaction, analyze and optimize the memory requirements as well.