Cognitive style and computer‐assisted learning: Problems and a possible solution1

Although the notion of cognitive style has been around for some time, only in relatively recent times has there been a research interest in examining its effect on the performance of Computer‐Assisted Learning (CAL) users. There are a number of practical difficulties associated with catering for different cognitive styles of CAL users. This paper identifies not only a style which influences CAL‐user performance and overcomes many of the difficulties, but also a possible suitable measure of that style. Data on the reliability of this measure is reported, along with preliminary work on its use to cater for CAL users with different cognitive styles. Future work will focus on the development of the package and the predictive validity of the style measure

Computer‐based interactive tutorial versus traditional lecture for teaching introductory aspects of pain

In the health sciences, a wide range of computer‐based courseware is now available. The aim of the study described in this paper has been to compare the effectiveness of a computer‐based learning (CBL) software package and a traditional lecture (TL) for the delivery, of introductory material on pain. Nineteen undergraduate nursing students were divided into two groups to attend a one‐hour learning session which introduced clinical aspects of pain and which was delivered by either CBL or TL. Students were assessed for prior knowledge by a pre‐session test, and for knowledge gain by an identical post‐session test. In addition, a multiple‐choice question paper was used to examine differences in pain knowledge between the two groups, and a questionnaire was used to examine the students’ views on their experience during the learning session. The results demonstrated that both groups showed significant knowledge gain after their respective learning sessions. No significant differences between the groups in the magnitude of knowledge gain were found for clinical aspects of pain delivered during the learning sessions. The attitude questionnaire revealed that students attending CBL reported similar learning experiences to those attending the lecture

Robot Autonomy for Surgery

Autonomous surgery involves having surgical tasks performed by a robot operating under its own will, with partial or no human involvement. There are several important advantages of automation in surgery, which include increasing precision of care due to sub-millimeter robot control, real-time utilization of biosignals for interventional care, improvements to surgical efficiency and execution, and computer-aided guidance under various medical imaging and sensing modalities. While these methods may displace some tasks of surgical teams and individual surgeons, they also present new capabilities in interventions that are too difficult or go beyond the skills of a human. In this chapter, we provide an overview of robot autonomy in commercial use and in research, and present some of the challenges faced in developing autonomous surgical robots

Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI

We propose a new method for breast cancer screening from DCE-MRI based on a post-hoc approach that is trained using weakly annotated data (i.e., labels are available only at the image level without any lesion delineation). Our proposed post-hoc method automatically diagnosis the whole volume and, for positive cases, it localizes the malignant lesions that led to such diagnosis. Conversely, traditional approaches follow a pre-hoc approach that initially localises suspicious areas that are subsequently classified to establish the breast malignancy -- this approach is trained using strongly annotated data (i.e., it needs a delineation and classification of all lesions in an image). Another goal of this paper is to establish the advantages and disadvantages of both approaches when applied to breast screening from DCE-MRI. Relying on experiments on a breast DCE-MRI dataset that contains scans of 117 patients, our results show that the post-hoc method is more accurate for diagnosing the whole volume per patient, achieving an AUC of 0.91, while the pre-hoc method achieves an AUC of 0.81. However, the performance for localising the malignant lesions remains challenging for the post-hoc method due to the weakly labelled dataset employed during training.Comment: Submitted to Medical Image Analysi

The Effect of Varied Gender Groupings on Argumentation Skills among Middle School Students in Different Cultures

The purpose of this mixed-methods study was to explore the effect of varied gender groupings on argumentation skills among middle school students in Taiwan and the United States in a project-based learning environment that incorporated a graph-oriented computer-assisted application (GOCAA). A total of 43 students comprised the treatment condition and were engaged in the collaborative argumentation process in same-gender groupings. Of these 43 students, 20 were located in the U.S. and 23 were located in Taiwan. A total of 40 students comprised the control condition and were engaged in the collaborative argumentation process in mixed-gender groupings. Of these 40 students, 19 were in the U.S. and 21 were in Taiwan. In each country, verbal collaborative argumentation was recorded and the students’ post essays were collected. Among females in Taiwan, one-way analysis of variance (ANOVA) indicated that statistically a significant gender-grouping effect was evident on the total argumentation skills outcome, while MANOVA indicated no significant gender-grouping effect on the combined set of skill outcomes. Among females in the U.S., MANOVA indicated statistically significant gender-grouping effect on the combined set of argumentation skills outcomes Specifically, U.S. female students in mixed-gender groupings (the control condition) significantly outperformed female students in single-gender groupings (the treatment condition) in the counterargument and rebuttal skills. No significant group differences were observed among males. A qualitative analysis was conducted to examine how the graph-oriented computer-assisted application supported students’ development of argumentation skills in different gender groupings in both countries. In each country, all teams in both conditions demonstrated a similar pattern of collaborative argumentation with the exception of three female teams in the U.S. Female teams, male teams, (the treatment condition) and mixed-gender teams (the control condition) demonstrated metacognition regulation skills in different degrees and with different scaffolding

Courseware in academic library user education: A literature review from the GAELS Joint Electronic Library Project

The use of courseware for information skills teaching in academic libraries has been growing for a number of years. In order to create effective courseware packages to support joint electronic library activity at Glasgow and Strathclyde Universities, the GAELS project conducted a literature review of the subject. This review discovered a range of factors common to successful library courseware implementations, such as the need for practitioners to feel a sense of ownership of the medium, a need for courseware customization to local information environments, and an emphasis on training packages for large bodies of undergraduates. However, we also noted underdeveloped aspects worthy of further attention, such as treatment of pedagogic issues in library computer‐aided learning (CAL) implementations and use of hypertextual learning materials for more advanced information skills training. We describe how these findings shaped the packages produced by the project and suggest ways forward for similar types of implementation

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table