scholarly journals Self-efficacy and behavior patterns of learners using a real-time collaboration system developed for group programming

2022 ◽  
Author(s):  
Ting-Chia Hsu ◽  
Hal Abelson ◽  
Evan Patton ◽  
Shih-Chu Chen ◽  
Hsuan-Ning Chang
Keyword(s):  
Real Time ◽  
Self Efficacy ◽  
Interface Design ◽  
Behavior Patterns ◽  
Heterogeneous Groups ◽  
Collaborative Behavior ◽  
Significant Difference ◽  
Block Based ◽  
Disciplinary Background ◽  
Future Work

AbstractIn order to promote the practice of co-creation, a real-time collaboration (RTC) version of the popular block-based programming (BBP) learning environment, MIT App Inventor (MAI), was proposed and implemented. RTC overcomes challenges related to non-collocated group work, thus lowering barriers to cross-region and multi-user collaborative software development. An empirical study probed into the differential impact on self-efficacy and collaborative behavior of learners in the environment depending upon their disciplinary background. The study serves as an example of the use of learning analytics to explore the frequent behavior patterns of adult learners, in this case specifically while performing BBP in MAI integrated with RTC. This study compares behavior patterns that are collaborative or individual that occurred on the platform, and investigates the effects of collaboration on learners working within the RTC depending on whether they were CS-majors or not. We highlight advantages of the new MAI design during multi-user programming in the online RTC based on the connections between the interface design and BBP as illustrated by two significant behavior patterns found in this instructional experiment. First, the multi-user programming in the RTC allowed multiple tasks to happen at the same time, which promoted engagement in joint behavior. For example, one user arranged components in the interface design while another dragged blocks to complete the program. Second, this study confirmed that the Computer Programming Self-Efficacy (CPSE) was similar for individual and multi-user programming overall. The CPSE of the homogeneous CS-major groups engaged in programming within the RTC was higher than that of the homogeneous non-CS-major groups and heterogeneous groups. There was no significant difference between the CPSE of the homogenous non-CS group and the CPSE of the heterogeneous groups, regardless of whether they were engaged in individual programming or collaborative programming within their groups. The results of the study support the value of engaging with MAI collaboratively, especially for CS-majors, and suggest directions for future work in RTC design.

Effect of wearable sensor-based Otago Exercise Program biofeedback in older adults with moderate to high risk of falling

2020 ◽  
Vol 30 (Supplement_2) ◽  
Author(s):  
A C Martins ◽  
D Francisco ◽  
D Guia
Keyword(s):  
Older Adults ◽  
High Risk ◽  
Real Time ◽  
Self Efficacy ◽  
Exercise Program ◽  
Step Test ◽  
Control Group ◽  
Wearable Sensor ◽  
Post Intervention ◽  
Significant Difference

Abstract Introduction Falls remain a major public health issue. The ageing process is characterized by a progressive decrease in muscle strength, reaction time, postural control and changes in sensory systems. Wearable sensor-based biofeedback systems used in physiotherapy, particularly incorporated in exercise programs, are promising strategies to enhance the learning of strength and balance exercises and improve self-efficacy. Objectives To evaluate the effect of the wearable sensor-based Otago Exercise Program (OTAGO) biofeedback in older adults with moderate to high risk. Methodology Sixty participants (84.35 years) were distributed to the experimental group (26) and a control group (34). The EG underwent the OTAGO incorporated in a technological system using pressure and inertial sensors and biofeedback in real-time, administered by a physiotherapist for 5 weeks, with a frequency of 2 times a week. The CG kept doing their regular activities. Outcome measures included handgrip strength (HG), Time Up and Go (TUG), 30 seconds Sit to Stand, 10 meters Walking Speed (10m WS), 4 Stage Balance Test “Modified”, Step test and Questionnaire of Self-efficacy for exercise. Results At baseline, significant differences were observed regarding the 10m WS (p < 0.001), TUG (p = 0.036) and HG (p = 0.001). Relatively to 4SBTM, in post-intervention was seen significant difference (p = 0.008) and in EG there was also substantial results (p < 0.001). The same happens in SEE (p = 0.013 and p = 0.020, respectively). A significant increase was found in EG so that the post-intervention 10m WS was statistically higher compared with the CG (EG: 0.42±0.29; CG: 1.10±0.51; p = 0.003). In the CG worst results were observed in some of the functional tests. Conclusion Biofeedback in real-time facilitates the self-learning of the exercise program, and it is a useful tool for training strength, balance and self-efficacy for exercise, contributing to reducing the risk of falls.

scholarly journals Adapting Helping Babies Breathe into a Virtual Curriculum: Methods, Results, and Lessons Learned

2021 ◽  
Vol 8 ◽  
pp. 2333794X2110196
Author(s):  
Celia Sobelman ◽  
Kristen Richard ◽  
Patricia McQuilkin ◽  
Nisha Fahey
Keyword(s):  
Self Efficacy ◽  
Confidence Score ◽  
Lessons Learned ◽  
Knowledge Assessment ◽  
Helping Babies Breathe ◽  
Critical Elements ◽  
Significant Difference ◽  
Before And After ◽  
Assessment Skills ◽  
Future Work

Introduction. The Helping Babies Breathe (HBB) curriculum is an established, effective method to combat neonatal mortality. The COVID-19 pandemic has disrupted in-person HBB training sessions worldwide, portending deficits in the dissemination of this important intervention. Methods. A pilot study to compare in-person versus virtual HBB training among US-based pediatric and family medicine residents. Two HBB master trainers condensed the curriculum into an abbreviated course that was offered to 14 learners in-person (n = 6) and virtually via Zoom (n = 8). A standardized 10-item survey was administered before and after the session to measure reported self-efficacy of critical elements of HBB. Difference of difference analysis was performed to detect differences in post vs pre-training results among the 2 groups using STATA MP 15. Results. All learners showed improvement in preparedness, assessment, and skills subcomponents of self-efficacy with no notable differences based on the type of learning medium. At baseline, in-person learners had a 7-point higher self-efficacy score (69.7) in comparison to virtual learners (62.8; P = .26). After training, the confidence score improved significantly; by 14.3 units for in-person learners ( P = .01) and 12.9 for virtual learners ( P = .04). There was no statistically significant difference in improvement between the 2 groups ( P = .67). Furthermore, all learners passed the post-training knowledge assessment. Discussion. Virtual learning of HBB may be an alternative option in the setting of resource and travel limitations. Future work needs to assess possible differences in attainment of assessment skills and retention of the HBB curriculum among virtual learners.

scholarly journals A Study of First-Year Students’ Attitudes toward Programming in the Innovation in Educational Technology Course

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Virawan Amnouychokanant ◽  
Surapon Boonlue ◽  
Saranya Chuathong ◽  
Kuntida Thamwipat
Keyword(s):  
Educational Technology ◽  
Self Efficacy ◽  
First Year Students ◽  
Five Dimensions ◽  
Significant Difference ◽  
Students Attitudes ◽  
Block Based ◽  
Level Of Significance ◽  
Learning Performances ◽  
Technology Course

The purpose of this study was to investigate the relationship between students’ attitudes toward programming, gender, and learning performances. The survey used for measuring students’ attitudes toward programming consisted of 20 questions on a five-point Likert scale in five dimensions (meaningfulness, interest in programming, self-efficacy, creativity, and collaboration). Ninety freshmen who had basic programming experience by using block-based programming in the Innovation in Educational Technology course were asked to take the survey. The overall reliability of the survey was found to be 0.93. The results showed that there was no significant difference between male and female freshmen in attitude toward programming, but there was a significant difference among different learning performances in dimensions of interest in programming, self-efficacy, and creativity. We performed pairwise comparisons at the same level of significance by using Fisher’s least significant difference (LSD) method to test which group differs from the other groups. The results found that low-performing students’ attitudes toward programming in dimensions of interest in programming, self-efficacy, and creativity were the lowest of all types of students. This is a challenge for instructors in planning learning activities to encourage low-performing students to have a more positive attitude toward programming.

scholarly journals The Effect of Individual and Group Learning on Block-Based Programming Self-Efficacy and Robotic Programming Attitudes of Secondary School Students

2020 ◽  
Vol 9 (1) ◽  
pp. 108-121
Author(s):  
Esra ARSLAN ◽  
Onur İŞBULAN
Keyword(s):  
Secondary School ◽  
Private School ◽  
Self Efficacy ◽  
Secondary School Students ◽  
Group Learning ◽  
Control Groups ◽  
School Students ◽  
Significant Difference ◽  
Block Based ◽  
And Control

In this research, the effects of individual and group learning activities on perceptions of block-based programming self-efficacy and attitudes towards robotic programming were tried to be determined. The research was conducted in a private school located in Sariyer, district of Istanbul province in the 2nd academic year of 2018-2019 with 32 students from 7th Grade. The study fulfilled as a semi-experimental pattern on the experimental and control groups using the pretest-posttest design. According to the research results, individual and group learning did not affect secondary school students’ perceptions of block-based programming self-efficacy and attitudes towards robotic programming. In addition to this, an increase was found in the perceptions of block-based programming self-efficacy in the experimental and control groups, and the attitudes towards programming of programming of the experimental group students. It was concluded that there was no significant difference between age, gender, taking aScratch lesson, writing a computer program with Scratch, and taking a robotic-programming lessons before. While robotic attitudes of students did not differ significantly according to their age and previous status of taking robotic coding courses, however, they differed significantly according to gender and male students had higher attitudes towards robotic programming than female students.

INITIAL STEP OF SPECIALIZED CORPORA BUILDING: CLEANING PROCEDURES

2020 ◽  
Author(s):  
Vera Yakubson ◽  
Victor Zakharov
Keyword(s):  
Text Processing ◽  
Research Question ◽  
Initial Step ◽  
Main Research ◽  
Academic Texts ◽  
Linguistic Annotation ◽  
Significant Difference ◽  
Cleaning Procedures ◽  
Unique Source ◽  
Future Work

This paper deals with the specialized corpora building, specifically academic language corpus in the biotechnology field. Being a part of larger research devoted to creation and usage of specialized parallel corpus, this piece aims to analyze the initial step of corpus building. Our main research question was what procedures we need to implement to the texts before using them to develop the corpus. Analysis of previous research showed the significant quantity of papers devoted to corpora creation, including academic specialized corpora. Different sides of the process were analyzed in these researches, including the types of texts used, the principles of crawling, the recommended length of texts etc. As to the text processing for the needs of corpora creation, only the linguistic annotation issues were examined earlier. At the same time, the preliminary cleaning of texts before their usage in corpora may have significant influence on the corpus quality and its utility for the linguistic research. In this paper, we considered three small corpora derived from the same set of academic texts in the biotechnology field: “raw” corpus without any preliminary cleaning and two corpora with different level of cleaning. Using different Sketch Engine tools, we analyzed these corpora from the position of their future users, predominantly as sources for academic wordlists and specialized multi-word units. The conducted research showed very little difference between two cleaned corpora, meaning that only basic cleaning procedures such as removal of reference lists are can be useful in corpora design. At the same time, we found a significant difference between raw and cleaned corpora and argue that this difference can affect the quality of wordlists and multi-word terms extraction, therefore these cleaning procedures are meaningful. The main limitation of the study is that all texts were taken from the unique source, so the conclusions could be affected by this specific journal’s peculiarities. Therefore, the future work should be the verification of results on different text collections

Supporting Youth With Autism Learning Social Competence: A Comparison of Game- and Nongame-Based Activities in 3D Virtual World

2021 ◽  
pp. 073563312110220
Author(s):  
Xianhui Wang ◽  
Wanli Xing
Keyword(s):  
Social Interaction ◽  
Social Competence ◽  
Central Element ◽  
Autism Spectrum ◽  
Virtual Learning Environment ◽  
Learning Activities ◽  
Behavior Patterns ◽  
Interaction Behavior ◽  
Significant Difference ◽  
The Impact

This study explored youth with Autism Spectrum Disorder (ASD) learning social competence in the context of innovative 3D virtual learning environment and the effects of gaming as a central element of the learning experience. The empirical study retrospectively compared the social interactions of 11 adolescents with ASD in game-and nongame-based 3D collaborative learning activities in the same social competence training curriculum. We employed a learning analytics approach - association rule mining to uncover the associative rules of verbal social interaction and nonverbal social interaction contributors from the large dataset of the coded social behaviors. By comparing the rules across the game and nongame activities, we found a significant difference in youth with ASD’s social performance. The results of the group comparison study indicated that the co-occurrence of verbal and nonverbal behaviors is much stronger in the game-based learning activities. The game activities also yielded more diverse social interaction behavior patterns. On the other hand, in the nongame activities, students’ social interaction behavior patterns are much more limited. Furthermore, the impact of game design principles on learning is then discussed in this paper.

scholarly journals Engineering students’ attitudinal beliefs by gender and student division: a methodological comparison of changes over time

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Madison E. Andrews ◽  
Anita D. Patrick ◽  
Maura Borrego
Keyword(s):  
Self Efficacy ◽  
Engineering Students ◽  
Focal Point ◽  
Value Engineering ◽  
Longitudinal Analyses ◽  
Utility Value ◽  
Cross Sectional ◽  
Targeted Interventions ◽  
Significant Difference ◽  
Over Time

Abstract Background Students’ attitudinal beliefs related to how they see themselves in STEM have been a focal point of recent research, given their well-documented links to retention and persistence. These beliefs are most often assessed cross-sectionally, and as such, we lack a thorough understanding of how they may fluctuate over time. Using matched survey responses from undergraduate engineering students (n = 278), we evaluate if, and to what extent, students’ engineering attitudinal beliefs (attainment value, utility value, self-efficacy, interest, and identity) change over a 1-year period. Further, we examine whether there are differences based on gender and student division, and then compare results between cross-sectional and longitudinal analyses to illustrate weaknesses in our current understanding of these constructs. Results Our study revealed inconsistencies between cross-sectional and longitudinal analyses of the same dataset. Cross-sectional analyses indicated a significant difference by student division for engineering utility value and engineering interest, but no significant differences by gender for any variable. However, longitudinal analyses revealed statistically significant decreases in engineering utility value, engineering self-efficacy, and engineering interest for lower division students and significant decreases in engineering attainment value for upper division students over a one-year period. Further, longitudinal analyses revealed a gender gap in engineering self-efficacy for upper division students, where men reported higher means than women. Conclusions Our analyses make several contributions. First, we explore attitudinal differences by student division not previously documented. Second, by comparing across methodologies, we illustrate that different conclusions can be drawn from the same data. Since the literature around these variables is largely cross-sectional, our understanding of students’ engineering attitudes is limited. Our longitudinal analyses show variation in engineering attitudinal beliefs that are obscured when data is only examined cross-sectionally. These analyses revealed an overall downward trend within students for all beliefs that changed significantly—losses which may foreshadow attrition out of engineering. These findings provide an opportunity to introduce targeted interventions to build engineering utility value, engineering self-efficacy, and engineering interest for student groups whose means were lower than average.

scholarly journals Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3956
Author(s):  
Youngsun Kong ◽  
Hugo F. Posada-Quintero ◽  
Ki H. Chon
Keyword(s):  
Real Time ◽  
Electrodermal Activity ◽  
Smartphone Application ◽  
Real Time Processing ◽  
Pain Stimulation ◽  
Significant Difference ◽  
Before And After ◽  
Electrical Pulses ◽  
Pain Detection ◽  
High Level

The subjectiveness of pain can lead to inaccurate prescribing of pain medication, which can exacerbate drug addiction and overdose. Given that pain is often experienced in patients’ homes, there is an urgent need for ambulatory devices that can quantify pain in real-time. We implemented three time- and frequency-domain electrodermal activity (EDA) indices in our smartphone application that collects EDA signals using a wrist-worn device. We then evaluated our computational algorithms using thermal grill data from ten subjects. The thermal grill delivered a level of pain that was calibrated for each subject to be 8 out of 10 on a visual analog scale (VAS). Furthermore, we simulated the real-time processing of the smartphone application using a dataset pre-collected from another group of fifteen subjects who underwent pain stimulation using electrical pulses, which elicited a VAS pain score level 7 out of 10. All EDA features showed significant difference between painless and pain segments, termed for the 5-s segments before and after each pain stimulus. Random forest showed the highest accuracy in detecting pain, 81.5%, with 78.9% sensitivity and 84.2% specificity with leave-one-subject-out cross-validation approach. Our results show the potential of a smartphone application to provide near real-time objective pain detection.

scholarly journals Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Song-Quan Ong ◽  
Hamdan Ahmad ◽  
Gomesh Nair ◽  
Pradeep Isawasan ◽  
Abdul Hafiz Ab Majid
Keyword(s):  
Deep Learning ◽  
Aedes Aegypti ◽  
Real Time ◽  
Aedes Albopictus ◽  
Automated Classification ◽  
Expert Performance ◽  
Deep Convolutional Neural Networks ◽  
Significant Difference ◽  
Set Up

AbstractClassification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

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