In two studies, we investigated whether a recently developed psychometric instrument can differentiate intrinsic, extraneous, and germane cognitive load. Study I revealed a similar three-factor ...solution for language learning (n = 108) and a statistics lecture (n = 174), and statistics exam scores correlated negatively with the factors assumed to represent intrinsic and extraneous cognitive load during the lecture. In Study II, university freshmen who studied applications of Bayes' theorem in example–example (n = 18) or example–problem (n = 18) condition demonstrated better posttest performance than their peers who studied the applications in problem–example (n = 18) or problem–problem (n = 20) condition, and a slightly modified version of the aforementioned psychometric instrument could help researchers to differentiate intrinsic and extraneous cognitive load. The findings provide support for a recent reconceptualization of germane cognitive load as referring to the actual working memory resources devoted to dealing with intrinsic cognitive load.
•We experimentally tested a new instrument for cognitive load measurement.•Results indicate that it could differentiate intrinsic and extraneous cognitive load.•Example/example–problem pairs enhanced learning more than problem/problem–example pairs.•The findings provide support for a recent reconceptualization of germane cognitive load.
•The paper introduces three different settings of Example-Use: Spontaneous, Evoked, and Provided.•These constructs are used to organize and categorize selected research on exemplification in ...mathematics.•The spontaneous mode of example-use sheds light on students’ thinking.•The evoked mode of example-use triggers students to deepen their understanding.•The provided example-use requires scaffolding of students’ attention to relevant features of the example.
The paper provides an overview of the broad field of research on the roles and use of examples in mathematical learning and thinking, specifically in concept learning and proving, and offers a way to organize and categorize selected research in this field. The main contribution of the paper is the distinction between three settings of example-use: spontaneous example-use, evoked example-use, and responsive example-use to a provided example. In the first two – the main source of examples comes from the student/learner, while in the third setting the main source is the teacher/researcher. The illustrations that accompany the description of each setting by research-based cases convey the affordances and challenges of each one. While these three settings are applicable to various kinds of learning, I highlight their affordances mainly for concept formation, and point to papers in this issue that provide a rich collection of cases that convey the place of these settings for conjecturing and proving.
Heat shock protein 90 (Hsp90) is an ATP-dependent molecular chaperone which is essential in eukaryotes. It is required for the activation and stabilization of a wide variety of client proteins and ...many of them are involved in important cellular pathways. Since Hsp90 affects numerous physiological processes such as signal transduction, intracellular transport, and protein degradation, it became an interesting target for cancer therapy. Structurally, Hsp90 is a flexible dimeric protein composed of three different domains which adopt structurally distinct conformations. ATP binding triggers directionality in these conformational changes and leads to a more compact state. To achieve its function, Hsp90 works together with a large group of cofactors, termed co-chaperones. Co-chaperones form defined binary or ternary complexes with Hsp90, which facilitate the maturation of client proteins. In addition, posttranslational modifications of Hsp90, such as phosphorylation and acetylation, provide another level of regulation. They influence the conformational cycle, co-chaperone interaction, and inter-domain communications. In this review, we discuss the recent progress made in understanding the Hsp90 machinery.
Complicated underwater environments bring new challenges to object detection, such as unbalanced light conditions, low contrast, occlusion, and mimicry of aquatic organisms. Under these ...circumstances, the objects captured by the underwater camera will become vague, and the generic detectors often fail on these vague objects. This work aims to solve the problem from two perspectives: uncertainty modeling and hard example mining. We propose a two-stage underwater detector named boosting R-CNN, which comprises three key components. First, a new region proposal network named RetinaRPN is proposed, which provides high-quality proposals and considers objectness and IoU prediction for uncertainty to model the object prior probability. Second, the probabilistic inference pipeline is introduced to combine the first-stage prior uncertainty and the second-stage classification score to model the final detection score. Finally, we propose a new hard example mining method named boosting reweighting. Specifically, when the region proposal network miscalculates the object prior probability for a sample, boosting reweighting will increase the classification loss of the sample in the R-CNN head during training, while reducing the loss of easy samples with accurately estimated priors. Thus, a robust detection head in the second stage can be obtained. During the inference stage, the R-CNN has the capability to rectify the error of the first stage to improve the performance. Comprehensive experiments on two underwater datasets and two generic object detection datasets demonstrate the effectiveness and robustness of our method. The link of code:https://github.com/mousecpn/Boosting-R-CNN.
Summary
This commentary reviews the evidence for nine principles for how to design effective example‐based instruction, drawing from the articles in this special issue.
•Medical image DNNs are easier to be attacked than natural non-medical image DNNs.•Complex biological textures of medical images may lead to more vulnerable regions.•State-of-the-art deep networks ...can be overparameterized for medical imaging tasks.•Medical image adversarial attacks can also be easily detected.•High detectability may be caused by perturbations outside the pathological regions.
Deep neural networks (DNNs) have become popular for medical image analysis tasks like cancer diagnosis and lesion detection. However, a recent study demonstrates that medical deep learning systems can be compromised by carefully-engineered adversarial examples/attacks with small imperceptible perturbations. This raises safety concerns about the deployment of these systems in clinical settings. In this paper, we provide a deeper understanding of adversarial examples in the context of medical images. We find that medical DNN models can be more vulnerable to adversarial attacks compared to models for natural images, according to two different viewpoints. Surprisingly, we also find that medical adversarial attacks can be easily detected, i.e., simple detectors can achieve over 98% detection AUC against state-of-the-art attacks, due to fundamental feature differences compared to normal examples. We believe these findings may be a useful basis to approach the design of more explainable and secure medical deep learning systems.
Abstract Many instructional videos in mathematics education target knowledge of procedures and algorithms. To design instructional videos that support students in a non-algorithmic domain, such as ...mathematical modeling, we developed a framework based on research on heuristic worked examples and instructional videos. Because students’ perceptions play an important role in whether they will engage with a learning resource, our research questions focus on the advantages and challenges students perceive when they work with an instructional video on mathematical modeling and solve a subsequent related modeling problem. Using a video based on the developed framework, we conducted an interview study with 14 pairs of upper-secondary students. The results of the qualitative text analysis showed that the perceived advantages and challenges were related to certain design features and to the processes of self-regulated learning with the video. The students experienced challenges regarding the video’s duration, the transfer to a subsequent related modeling problem, the absence of a teacher, and self-discipline. Hence, further learner support is necessary to guide the transition from working with a video to autonomous modeling, such as combining the video with strategic instruments and teacher support. In addition, interactive video features may be essential, as the students enjoyed how those features involved them in the process of watching the video. Overall, the results indicate that instructional videos are promising for providing heuristic worked examples, offering an innovative approach for teaching and learning mathematical modeling.
There is a need for effective methods to teach critical thinking (CT). One instructional method that seems promising is comparing correct and erroneous worked examples (i.e., contrasting examples). ...The aim of the present study, therefore, was to investigate the effect of contrasting examples on learning and transfer of CT-skills, focusing on avoiding biased reasoning. Students (
N
= 170) received instructions on CT and avoiding biases in reasoning tasks, followed by: (1) contrasting examples, (2) correct examples, (3) erroneous examples, or (4) practice problems. Performance was measured on a pretest, immediate posttest, 3-week delayed posttest, and 9-month delayed posttest. Our results revealed that participants’ reasoning task performance improved from pretest to immediate posttest, and even further after a delay (i.e., they learned to avoid biased reasoning). Surprisingly, there were no differences in learning gains or transfer performance between the four conditions. Our findings raise questions about the preconditions of contrasting examples effects. Moreover, how transfer of CT-skills can be fostered remains an important issue for future research.
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