Prior Content Knowledge Research Articles

Human or Humanoid Animated Pedagogical Avatars in Video Lectures: The Impact of the Knowledge Type on Learning Outcomes

This study examines the impacts of educational videos with human or humanoid animated pedagogical avatars (APAs) on cognitive, affective, and social learning outcomes in declarative and procedural knowledge contexts. A 2 × 2 factorial design was used, considering instructor type (human vs. humanoid APA) and knowledge type (declarative vs. procedural). The study involved 139 university students with no prior knowledge of the video content and was conducted in a human-computer interaction lab using an eye-tracking device. There were no significant differences in learning achievement, perceived lecture engagement, emotion, satisfaction, and social presence between the human and humanoid APA. However, learners showed greater visual interest in the human instructor, which influenced their interest in the learning content. Viewers of declarative knowledge videos showed more visual interest and achieved higher learning scores, while procedural videos increased lecture engagement, emotion, satisfaction, and social presence. The study suggests that humanoid APAs can effectively replace human instructors in educational videos and emphasizes the importance of considering knowledge types in video design to impact learning outcomes.

Development of an Online Isotope Dilution CE/ICP-MS Method for the Quantification of Sulfur in Biological Compounds.

We report an analytical methodology for the quantification of sulfur in biological molecules via a species-unspecific postcolumn isotope dilution (online ID) approach using capillary electrophoresis (CE) coupled online with inductively coupled plasma-mass spectrometry (online ID CE/ICP-MS). The method was optimized using a mixture of standard compounds including sulfate, methionine, cysteine, cystine, and albumin, yielding compound recoveries between 98 and 105%. The quantity of sulfur is further converted to the quantity of the compounds owing to the prior knowledge of the sulfur content in the molecules. The limit of detection and limit of quantification of sulfur in the compounds were 1.3-2.6 and 4.1-8.4 mg L-1, respectively, with a correlation coefficient of 0.99 within the concentration range of sulfur of 5-100 mg L-1. The capability of the method was extended to quantify albumin in its native matrix (i.e., in serum) using experimentally prepared serum spiked with a pure albumin standard for validation. The relative expanded uncertainty of the method for the quantification of albumin was 6.7% (k = 2). Finally, we tested the applicability of the method on real samples by the analysis of albumin in bovine and human sera. For automated data assessment, a software application (IsoCor)─which was developed by us in a previous work─was developed further for handling of online ID data. The method has several improvements compared to previously published setups: (i) reduced adsorption of proteins onto the capillary wall owing to a special capillary-coating procedure, (ii) baseline separation of the compounds in less than 30 min via CE, (iii) quantification of several sulfur species within one run by means of the online setup, (iv) SI traceability of the quantification results through online ID, and (v) facilitated data processing of the transient signals using the IsoCor application. Our method can be used as an accurate approach for quantification of proteins and other biological molecules via sulfur analysis in complex matrices for various fields, such as environmental, biological, and pharmaceutical studies as well as clinical diagnosis.

ViComp: Video Compensation for Projector-Camera Systems.

Projector video compensation aims to cancel the geometric and photometric distortions caused by non-ideal projection surfaces and environments when projecting videos. Most existing projector compensation methods start by projecting and capturing a set of sampling images, followed by an offline compensation model training step. Thus, abundant user effort is required before the users can watch the video. Moreover, the sampling images have little prior knowledge of the video content and may lead to suboptimal results. To address these issues, this paper builds a video compensation system that can online adapt the compensation parameters. Our approach consists of five threads and can perform compensation, projection, capturing, and short-term and long-term model updates in parallel. Due to the parallel mechanism, rather than projecting and capturing hundreds of sampling images and training the model offline, we can directly use the projected and captured video frames for model updates on the fly. To quickly apply to the new environment, we introduce a deep learning-based compensation model that integrates a fixed transformer-based method and a novel CNN-based network. Moreover, for fast convergence and to reduce error accumulation during fine-tuning, we present a strategy that cooperates with short-term and long-term memory model updates. Experiments show that it significantly outperforms state-of-the-art baselines.

Detection and Analysis of Fake News Users’ Communities in Social Media

The widespread use of social media platforms has led to an increase in the dissemination of fake news with the intention of manipulating public opinion and causing chaos and panic among the population. To address this issue, we focus on detecting the organized groups that participate together in fake news campaigns without prior knowledge of the news content or the profiles of social accounts. To this end, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spatial–temporal similarity graph</i> , a novel graph structure that connects social accounts that participate in the early stage of similar fake news campaigns. A community detection algorithm is applied on the similarity graph to cluster the users into communities. We propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">community labeling algorithm</i> to label the communities as benign or malicious based on the output of a fake news classifier. Evaluation results show that the community labeling algorithm can correctly label the communities with an accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$99.61\%$</tex-math> </inline-formula> . In addition, we perform a statistical comparison analysis to identify the structural community features that are statistically significant between benign and malicious communities.

Targeting fraction misconceptions and reducing high confidence errors in an online tutor

AbstractBackgroundProviding students with worked out problem solutions is a beneficial instructional technique in STEM disciplines, and studying examples that have been worked out incorrectly may be especially helpful for reducing misconceptions in students with low prior content knowledge. However, past results are inconclusive and the effects of incorrect worked examples alone or in combination with correct examples remains unclear.ObjectivesWe aim to address whether studying incorrect examples alone or in combination with correct examples can support the reduction of students' fraction misconceptions, operationalized as errors made with high confidence.MethodsAfter incorrectly solving a sampling problem, 130 students in 4th through 11th grade in the U.S. were randomly assigned to a condition in an online problem set focused on fraction equivalence. Students studied either single‐type worked examples (i.e., correct or incorrect; n = 49) or combination‐type worked examples (correct and incorrect; n = 41) or engaged in a problem‐solving control (n = 50).ResultsStudying a combination of correct and incorrect worked examples was as effective as the problem‐solving control with feedback at improving fraction equivalence knowledge and reducing the rate of high‐confidence errors. Students in both the combination condition and the problem‐solving with feedback condition outperformed those who studied either correct or incorrect worked examples alone.ConclusionsResults support the inclusion of a combination of correct and incorrect worked examples when teaching students with low prior content knowledge. Studying a combination of example types within an online tutor helps to reduce misconceptions about fractions, a topic students commonly struggle with. A problem‐solving task with corrective feedback worked equally well.

DNAzyme-based faithful probing and pulldown to identify candidate biomarkers of low abundance.

Biomarker discovery is essential for the understanding, diagnosis, targeted therapy and prognosis assessment of malignant diseases. However, it remains a huge challenge due to the lack of sensitive methods to identify disease-specific rare molecules. Here we present MORAC, molecular recognition based on affinity and catalysis, which enables the effective identification of candidate biomarkers with low abundance. MORAC relies on a class of DNAzymes, each cleaving a sole RNA linkage embedded in their DNA chain upon specifically sensing a complex system with no prior knowledge of the system's molecular content. We show that signal amplification from catalysis ensures the DNAzymes high sensitivity (for target probing); meanwhile, a simple RNA-to-DNA mutation can shut down their RNA cleavage ability and turn them into a pure affinity tool (for target pulldown). Using MORAC, we identify previously unknown, low-abundance candidate biomarkers with clear clinical value, including apolipoprotein L6 in breast cancer and seryl-tRNA synthetase 1 in polyps preceding colon cancer.

Development of the control of variables strategy in physics among secondary school students

This study explores the developmental trend in scientific reasoning in the control of variables strategy (CVS) and how relevant factors contribute to explaining the individual abilities of secondary school students. A cross-sectional investigation involving 807 students from Grades 8 to 12 was conducted in eleven public schools in Vietnam. A 24-item test assessed CVS in basic physics (mechanics, thermodynamics, and electricity), emphasizing three CVS subskills (identifying controlled experiments, interpreting controlled experiment outcomes, and understanding the determinacy of confounded experiments). The results showed that the students’ CVS capacity increased in a nonlinear pattern across grade levels with a model fitting in the symmetric logistic function, in which the most rapid growth was flagged in the second year of high school. Although there was no significant difference for gender in CVS either within each grade cohort or within the whole sample, the mean score favoured males on the item bundle of understanding the determinacy of confounded experiments. Furthermore, multi-model Bayesian inference suggested that grade level (or student age), prior content knowledge, and mother's education were better factors in predicting students’ CVS capacities in this study. The implications for implementing the findings in educational practice are also discussed.

Unsupervised Video Segmentation using Quantization

Abstract: Unsupervised video segmentation is a challenging task in computer vision that involves dividing a video into meaningful segments without any labeled data or prior knowledge of the video content. One approach to achieving this is the use of quantization, which involves clustering similar image patches or video frames into discrete groups based on their visual features.Beta-VAE is a type of variational autoencoder (VAE) that is capable of learning disentangled representations of data. It can be applied to video segmentation using quantization, allowing for more effective segmentation and analysis of complex video datasets.The use of Beta-VAE and quantization is essential for several reasons. Firstly, it allows for the automatic analysis of large video datasets without the need for manual annotation, which can be time-consuming and expensive. Secondly, it enables the detection and tracking of objects and events in videos, which has applications in surveillance, robotics, and autonomous driving. Finally, it can be used for content-based video indexing and retrieval, which is crucial for video search and recommendation systems.The advantage of using Beta-VAE in video segmentation is that it can learn disentangled representations of video frames, which separates the data into meaningful factors of variation. This leads to more accurate segmentation, as the model can distinguish between different objects and events in the video, even if they have similar visual features.In conclusion, the use of Beta-VAE and quantization is necessary for advancing the field of computer vision and improving the analysis and understanding of videos. It has numerous applications in various industries and can contribute to the development of more effective and efficient video analysis tools.

An exploratory study on eye-gaze patterns of experts and novices of science inference graph items

&lt;abstract&gt;&lt;p&gt;Graphs are highly prevalent as a form of quantitative data in various science, technology, engineering and mathematics fields. Thus, graphical literacy is especially important in understanding today's world and being scientifically literate. However, students often face difficulties in graph interpretation and differ substantially in their graphical literacy. While many teachers are aware of students' difficulties in answering graph items, there is limited knowledge about how students go about attempting graph items. In this exploratory study, we investigated the eye-gaze patterns of experts and novices in graph interpretation of five science inference-based multiple-choice items requiring no prior content knowledge to problem-solve. Experts refer to science university faculty members who are currently teaching science content courses to undergraduate students. Novices refer to university undergraduates majoring in one of the science subjects. Participants' eye-gaze movements were recorded using the Dikablis eye-tracker, and their eye-gaze patterns and total glance time (s) were subsequently analyzed using the software D-Lab 3.0. Experts focused more on the question stem, whereas novices focused more on the graph. Additionally, experts tend to focus on contextual and graph data features initially, before moving to cues such as options. Conversely, novices demonstrated more sporadic search patterns. The findings contribute to the literature that compares how experts and novices' problem-solve graph items that are inference-based. An interesting future study on the eye gaze patterns and accuracy of answers is suggested from a finding. This study also provides a set of heuristics to be adopted in the teaching and learning of graph interpretation. The findings of this study have implications for teachers in the way they scaffold students' approach to answering graphical items. Additionally, students can employ heuristics to answer graphical items more effectively.&lt;/p&gt;&lt;/abstract&gt;

Obfuscation Algorithm for Privacy-Preserving Deep Learning-Based Medical Image Analysis

Deep learning (DL)-based algorithms have demonstrated remarkable results in potentially improving the performance and the efficiency of healthcare applications. Since the data typically needs to leave the healthcare facility for performing model training and inference, e.g., in a cloud based solution, privacy concerns have been raised. As a result, the demand for privacy-preserving techniques that enable DL model training and inference on secured data has significantly grown. We propose an image obfuscation algorithm that combines a variational autoencoder (VAE) with random non-bijective pixel intensity mapping to protect the content of medical images, which are subsequently employed in the development of DL-based solutions. A binary classifier is trained on secured coronary angiographic frames to evaluate the utility of obfuscated images in the context of model training. Two possible attack configurations are considered to assess the security level against artificial intelligence (AI)-based reconstruction attempts. Similarity metrics are employed to quantify the security against human perception (structural similarity index measure and peak signal-to-noise-ratio). Furthermore, expert readers performed a visual assessment to determine to what extent the reconstructed images are protected against human perception. The proposed algorithm successfully enables DL model training on obfuscated images with no significant computational overhead while ensuring protection against human eye perception and AI-based reconstruction attacks. Regardless of the threat actor’s prior knowledge of the target content, the coronary vessels cannot be entirely recovered through an AI-based attack. Although a drop in accuracy can be observed when the classifier is trained on obfuscated images, the performance is deemed satisfactory in the context of a privacy–accuracy trade-off.

Preservice Elementary Teachers’ Mathematical Knowledge on Fractions as Operator in Word Problems

The preservice primary school mathematics teacher needs training that focuses on school mathematics and its didactics. Research has shown the importance of developing mathematical content knowledge as well as adequate didactic knowledge. Teacher education programs must address both to provide an effective teaching-learning process. The present research aims to assess the mathematical content knowledge of 194 prospective teachers about fraction word problems faced by students at the Primary Education stage, based on the Mathematics Teachers′ Specialized Knowledge (MTSK) model. By means of the preservice teachers’ written answers, we explored their knowledge and identified the associated error to fraction word problems (one-step and multistep) in which the fraction has a meaning as an operator. Preservice teachers showed some difficulties when working with this content, difficulties that were intensified when they solved multistep problems. The most common error founded in both types of problems is related to the meaning of fraction as operator. The results show a weak prior mathematical content knowledge and as a consequence, it is necessary to establish preventive actions in the training degrees since an insufficient mathematical content knowledge prevents them having a proper didactic knowledge.

State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures.

In modern digital microscopy, deconvolution methods are widely used to eliminate a number of image defects and increase resolution. In this review, we have divided these methods into classical, deep learning-based, and optimization-based methods. The review describes the major architectures of neural networks, such as convolutional and generative adversarial networks, autoencoders, various forms of recurrent networks, and the attention mechanism used for the deconvolution problem. Special attention is paid to deep learning as the most powerful and flexible modern approach. The review describes the major architectures of neural networks used for the deconvolution problem. We describe the difficulties in their application, such as the discrepancy between the standard loss functions and the visual content and the heterogeneity of the images. Next, we examine how to deal with this by introducing new loss functions, multiscale learning, and prior knowledge of visual content. In conclusion, a review of promising directions and further development of deconvolution methods in microscopy is given.

The Multidimensional Knowledge in Text Comprehension framework

Prior knowledge is one of the strongest contributors to comprehension, but there is little specificity about different aspects of prior knowledge and how they impact comprehension. This article introduces the Multidimensional Knowledge in Text Comprehension framework, which conceptualizes prior knowledge along four intersecting dimensions: amount, accuracy, specificity, and coherence. Amount refers to how many relevant concepts the reader knows. Accuracy refers to the extent to which the reader’s knowledge is correct. Specificity refers the degree to which the knowledge is related to information in the target text. Coherence refers to the interconnectedness of prior knowledge. Conceptualizing prior content knowledge along these dimensions deepens understanding of the construct and lends to more specific predictions about how learners process information. Considering knowledge across multiple dimensions is crucially important to the development and selection of prior knowledge assessments and, in turn, educators’ ability to capitalize on learners’ strengths across various comprehension tasks.

Language-Independent Approach for Automatic Computation of Vowel Articulation Features in Dysarthric Speech Assessment

Imprecise vowel articulation can be observed in people with Parkinson's disease (PD). Acoustic features measuring vowel articulation have been demonstrated to be effective indicators of PD in its assessment. Standard clinical vowel articulation features of vowel working space area (VSA), vowel articulation index (VAI) and formants centralization ratio (FCR), are derived the first two formants of the three corner vowels /a/, /i/ and /u/. Conventionally, manual annotation of the corner vowels from speech data is required before measuring vowel articulation. This process is time-consuming. The present work aims to reduce human effort in clinical analysis of PD speech by proposing an automatic pipeline for vowel articulation assessment. The method is based on automatic corner vowel detection using a language universal phoneme recognizer, followed by statistical analysis of the formant data. The approach removes the restrictions of prior knowledge of speaking content and the language in question. Experimental results on a Finnish PD speech corpus demonstrate the efficacy and reliability of the proposed automatic method in deriving VAI, VSA, FCR and F2i/F2u (the second formant ratio for vowels /i/ and /u/). The automatically computed parameters are shown to be highly correlated with features computed with manual annotations of corner vowels. In addition, automatically and manually computed vowel articulation features have comparable correlations with experts' ratings on speech intelligibility, voice impairment and overall severity of communication disorder. Language-independence of the proposed approach is further validated on a Spanish PD database, PC-GITA, as well as on TORGO corpus of English dysarthric speech.

Modeling the relationship between students' prior knowledge, causal reasoning processes, and quality of causal maps

Despite the 30 years of research on the effects of using computer-based mapping tools to construct concept maps, the research has provided little insight into the cognitive processes and strategies students use to create more accurate maps and the types of behaviors that can be mined in computer-based mapping tools to reveal and support processes that improve student performance. This study used video analysis to identify, operationally define, and code processes of reasoning students used while using a computer-based tool to construct causal maps. The process data was then used to conduct a path analysis to determine which and how specific processes impact the quality of students' causal maps and to determine how students’ prior knowledge on the given problems influence which processes students use. The findings reveal that students exhibiting the tendency to use more backward than forward processing and use more breadth than depth-first processing produced higher quality causal maps, and that the influence of prior content knowledge on what processes students use are not conclusive. The findings and the methods developed in this study present new ideas on ways to integrate learning analytics into computer-based mapping tools to model and manage the cognitive processes students use to construct better cognitive maps.

Beyond the threshold: Exploring English language proficiency, linguistic challenges, and academic language skills of Japanese students in an English medium instruction programme

This article examines the relationship between Japanese undergraduate students’ English language proficiency and English language-related challenges faced when studying an international business course through English. It also examines English language proficiency thresholds students need to reach in each academic skill (i.e. reading, listening, speaking and writing) to experience a lower level of linguistic challenges. A total of 264 students were surveyed in Tokyo, Japan, and 13 follow-up interviews were conducted. Exploratory and confirmatory factor analyses confirmed the underlying factors in the EMI (English medium of instruction) Challenges Scale loaded onto a priori assumptions of dimensions falling along skill-based constructs. Analysis of questionnaire data revealed that English language proficiency (i.e. TOEIC score) was a statistically significant predictor of challenges in the EMI programme. While no clear discernible threshold was observed, the differences in perceived ease of study at different levels of English proficiency influenced the challenges students reported for each academic skill. Interview data uncovered the multi-faceted nature of how the thresholds are determined not only by language proficiency but also by other factors, such as prior content knowledge, motivation, and the classroom learning environment. Practical implications for pedagogy are also discussed.

Influencia del Modelo Interactivo de Keith E. Stanovich en la Comprensión de los Textos Cortos en Inglés de los Estudiantes de 9° de la I. E. Escuela Normal Superior San Mateo, Boyacá

Después de algunas observaciones realizadas en el año 2019 con alumnos pertenecientes a octavo grado fue posible descubrir, a través de pruebas estandarizadas, que los mismos contaban con falencias que les impedían comprender eficazmente textos cortos es inglés. Por ende, fue llevada a cabo esta investigación acción con el objetivo de analizar la influencia del Modelo Interactivo de Keith E. Stanovich en la comprensión lectora de textos cortos en inglés por parte de los estudiantes de noveno grado de la Institución Educativa Escuela Normal de San Mateo (Boyacá). El modelo de lectura previamente mencionado cuenta con una interacción continua que se da entre el modelo ascendente “bottom- up” en el cual el lector decodifica un texto partiendo de unidades más pequeñas (letras a palabras, frases a oraciones, etc.) y descendente “top-down” en el cual el lector aporta una gran cantidad de conocimientos, expectativas, suposiciones, preguntas al texto y, dando un conocimiento básico del vocabulario, continúa leyendo mientras el texto confirma sus expectativas; Este estudio fue desarrollado bajo un enfoque mixto; Los instrumentos empleados para recopilar los datos fueron dos pruebas diagnósticas, cuatro talleres, y dos pruebas finales aplicadas a diez estudiantes de noveno grado. Los datos se analizaron tanto cuantitativa como cualitativamente, utilizando los instrumentos de investigación propuestos por Vanegas (2020). Con respecto a los hallazgos, se concluyó que el uso del Modelo Interactivo fue efectivo para mejorar la capacidad de comprensión lectora de los estudiantes, tanto literal como inferencialmente.

Automatic Image Pixel Clustering based on Mussels Wandering Optimization

Image pixel clustering or segmentation intends to identify pixel groups on an image without any preliminary labels. It remains a challenging task in computer vision since the size and shape of object segments are varied. Moreover, determining the segment number in an image without prior knowledge of the image content is an NP-hard problem. In this paper, we present an automatic image pixel clustering scheme based on mussels wandering optimization. An activation variable is applied to determine the number of clusters automatically with the cluster centers optimization. We revise the within- and between-class sum of squares ratio for random natural image content and develop a novel fitness function for the image pixel clustering task. Our proposed scheme is compared against existing state-of-the-art techniques using both synthetic data and real ASD dataset. Experimental results show the superiority performance of the proposed scheme.

Learning about knowledge: threshold concepts for Indigenous studies in education

The idea of ‘threshold concepts’ for Indigenous studies has received little attention in the literature to date. Page’s work is the notable exception, identifying the basis for what may constitute threshold concepts in this field. This paper builds on Page’s approach by providing a theoretical consideration of five ideas that may function as threshold concepts in Indigenous studies: race, Country, relationality, policy and evidence. Much research on Indigenous teaching and learning focuses on curriculum and pedagogy, but it is unclear what prior content knowledge of Indigenous issues pre-service teachers require. Threshold concepts allow the specification of content knowledge, which could suggest that learning about knowledge production may function as a part of learning about teaching, and thus describe a foundational purpose for Indigenous studies in the field of education.

Variable control and conceptual change: A large-scale quantitative study in elementary school

We examined the predictive value and interplay of elementary school students' understanding of the control-of-variables strategy, a domain-general experimentation skill, and their prior content knowledge for subsequent conceptual knowledge acquisition and conceptual change. Trained teachers provided N = 1809 first to sixth graders with 15 lessons of guided inquiry-based instruction on floating and sinking. We assessed understanding of the control-of-variables strategy before instruction, and conceptual content knowledge from before to after instruction. A mixture model analysis, specifically, a latent transition analysis, indicates that understanding of the control-of-variables strategy predicts content knowledge structure before instruction, and content knowledge development from before to after instruction. These findings corroborate lab-based research on the interplay of experimentation skills and content knowledge in inquiry settings and extend it to teacher-guided classroom instruction. We describe how learning pathways vary depending on students' understanding of the control-of-variables strategy and prior content knowledge, and discuss implications for learning and instruction.