Aspect-based Sentiment Analysis Model for Evaluating Teachers' Performance from Students' Feedback

Evaluating teachers' performance is a fundamental pillar of educational enhancement, guiding the evolution of pedagogical practices and fostering enriched learning environments. This study pioneers an innovative approach by harnessing sentiment analysis within an aspect-based framework to decipher the intricate emotional nuances embedded within students' feedback. By categorizing sentiments as positive, negative, and neutral, we delve into the diverse perceptions of teaching aspects, offering a multifaceted portrait of educators' contributions. Through meticulous data collection, preprocessing, and a deep learning sentiment analysis model, we dissected student comments into distinct teaching aspects. The subsequent sentiment analysis unearthed positive, negative, and neutral sentiments. Positive sentiments highlighted strengths and effective communication, while negative sentiments illuminated areas for growth. Neutral sentiments provided contextual equilibrium, forming a holistic tapestry of teachers' performance. The proposed model achieved 86\% F1 score for classifying sentiments into three classes

Evaluating Teachers’ Performance through Aspect-Based Sentiment Analysis

This research demonstrates a novel approach for evaluating teacher performance by conducting aspect-based sentiment analysis (ABSA) on student feedback. A large dataset of over 2 million student comments about teachers is analyzed using cutting-edge natural language processing and customized deep learning techniques. The methodology involves identifying positive, negative and neutral aspects of teaching using a BiLSTM model. Rigorous preprocessing, domain adaptation, and performance metrics ensure a robust and objective evaluation. The granular, nuanced insights obtained through this aspect-level sentiment analysis enable educational institutions to provide targeted and unbiased feedback to teachers on their strengths and areas needing improvement. Moreover, this work lays the foundation for detecting potentially fraudulent reviews in academic settings – a crucial capability for safeguarding assessment integrity. The detailed aspect-based analysis methodology presented here significantly advances subjective and holistic evaluation practices. This research has far-reaching implications for enriching teacher development while upholding the credibility of performance assessments through sentiment analysis innovations

A comprehensive dataset for aspect-based sentiment analysis in evaluating teacher performance

Teacher performance evaluation is an essential task in the field of education. In recent years, aspect-based sentiment analysis (ABSA) has emerged as a promising technique for evaluating teaching performance by providing a more nuanced analysis of student evaluations. This article presents a novel approach for creating a large-scale dataset for ABSA of teacher performance evaluation. The dataset was constructed by collecting student feedback from American International University-Bangladesh and then labeled by undergraduate-level students into three sentiment classes: positive, negative, and neutral. The dataset was carefully cleaned and preprocessed to ensure data quality and consistency. The final dataset contains over 2,000,000 student feedback instances related to teacher performance, making it one of the largest datasets for ABSA of teacher performance evaluation. This dataset can be used to develop and evaluate ABSA models for teacher performance evaluation, ultimately leading to better feedback and improvement for educators. The results of this study demonstrate the usefulness and effectiveness of ABSA in evaluating teacher performance and highlight the importance of creating high-quality datasets for this task

The bird gets caught by the WORM: tracking multiple deformable objects in noisy environments using weight ORdered logic maps

Object detection and tracking are active and important research areas in computer vision as well as neuroscience. Of particular interest is the detection and tracking of small, poorly lit, deformable objects in the presence of sensor noise, and large changes in background and foreground illumination. Such conditions are frequently encountered when an animal moves in its natural environment, or in an experimental arena. The problems are exacerbated with the use of high-speed video cameras as the exposure time for high-speed cameras is limited by the frame rate, which limits the SNR. In this paper we present a set of simple algorithms for detecting and tracking multiple, small, poorly lit, deformable objects in environments that feature drastic changes in background and foreground illumination, and poor signal-to-noise ratios. These novel algorithms are shown to exhibit better performance than currently available state-of-the art algorithms

WHoG: a weighted HOG-based scheme for the detection of birds and identification of their poses in natural environments

We describe a technique for object detection that uses a combination of global shape descriptors and local point descriptors. Our system is able to represent pose using a global shape descriptor, rather than the commonly used part based representation. This approach considerably reduces computational complexity and achieves a significant performance improvement on an extensive dataset: CUB-200-2011 [31]. Our methodology is valuable for the detection of textured objects that are viewed against background clutter and possess a high degree of articulation and variation of pose, as for example in birds. We demonstrate how high and low frequency gradients can be separated to better deal with the presence of interfering textures or stripes within the body, which is a major problem in the detection of bird-like objects. Furthermore, detection accuracy is improved by integrating appropriately designed scale invariant color features into the algorithm

Image denoising with Weighted ORientation-Matched filters(WORM)

Real world signals commonly exhibit slow variations or oscillations, punctuated with rapid transients. For example, images typically have smooth regions interrupted by edges or abrupt changes in contrast. These abrupt changes are often the most interesting parts of the data perceptually, as well as in terms of the information that they provide. Some of the high frequency content represents the important abrupt changes in image intensity that are associated with real edges of objects in the image. However, some of the high-frequency content also comprises the noise that is present in the image. We wish to retain this edge information, while removing the noise. In this paper, we present a dynamic filtering process where the dynamic mask is oriented to match the local gradients and its weights are proportional to the magnitude of the local gradients

Budgerigars adopt robust, but idiosyncratic flight paths

We have investigated the paths taken by Budgerigars while flying in a tunnel. The flight trajectories of nine Budgerigars (Melopsittacus undulatus) were reconstructed in 3D from high speed stereo videography of their flights in an obstacle-free tunnel. Individual birds displayed highly idiosyncratic flight trajectories that were consistent from flight to flight over the course of several months. We then investigated the robustness of each bird's trajectory by interposing a disk-shaped obstacle in its preferred flight path. We found that each bird continued to fly along its preferred trajectory up to a point very close to the obstacle before veering over the obstacle rapidly, making a minimal deviation to avoid a collision, and subsequently returning to its original path. Thus, Budgerigars show a high propensity to stick to their individual, preferred flight paths even when confronted with a clearly visible obstacle, and do not adopt a substantially different, unobstructed route. The robust preference for idiosyncratic flight paths, and the tendency to pass obstacles by flying above them, provide new insights into the strategies that underpin obstacle avoidance in birds. We believe that this is the first carefully controlled study of the behaviour of birds in response to a newly introduced obstacle in their flight path. The insights from the study could also have implications for conservation efforts to mitigate collisions of birds with man-made obstacles

An Automated Music Selector Derived from Weather Condition and its Impact on Human Psychology

Sometimes it is disquieting to generate a playlist to listen music for a specific moment. Though listening of music basically depends on our mood and it’s also been said that there exists a relation between our mood and weather, so our approach is to build an automated system to create a music playlist based on users mood and defined weather. Method is to measure the weight of each music files respect to defined mood and weather by using data mining algorithms