Actual Scores Research Articles

Winning the second half: The perceived and actual impact of the coach's half-time speech on basketball players’ performance

This study was designed to explore coaches’ half-time speeches and whether players perceive these speeches as having an impact on their performance later in the game. A mixed-methods convergent design was used. Participants were nine male basketball players aged 19–34 (M = 27, SD = 4.76) from a team playing in the Israeli Basketball Premier League, as well as their head coach (aged 41) and two assistant coaches (aged 51 and 34). Data were collected on the coach's speeches at half-time in games during the regular season. In addition, face-to-face semi-structured interviews with the players and the two assistant coaches were conducted at midseason. The average speech duration was approximately 3 min, and messages were delivered at an average pace of one theme every 13 s. The most frequent theme was psychological, with more negative themes delivered compared to positive ones. Psychological themes appeared 50% more than informational. We discuss the differences in the features of the talks between games in which the team was trailing at half-time, and games in which they reached half-time with a leading score. A surprising gap exists between the participants’ perception regarding the contribution of the half-time speech to players’ performance, and the actual score at the end of the game.

The Development and Management Work Innovation of University Students by Artificial Intelligence and Deep Learning in the Wireless Network Era

This study is aimed at promoting the growth and development of college students from two aspects: independent student learning and school management. Firstly, deep neural networks (DNNs) are used to build a personalized learning resource recommendation model for college students based on an online education platform. The model is tested and passed the experiment. A new comprehensive quality evaluation index system for college students is established in school management based on the traditional complete quality evaluation method. The thorough fuzzy evaluation (FCE) method and the backpropagation neural network (BPNN) model are combined to construct a comprehensive quality evaluation model for college students. The students’ comprehensive quality evaluation scores obtained using this model are compared with the actual scores through experiments. The results show that the number of learners is 50 in the constructed personalized learning resource recommendation model for college students. When the number of learning resources is 180, F 1 ‐ score = 3 is more significant than other programs. The FCE method and the comprehensive quality evaluation model constructed by BPNN scored six students. The maximum error value between the evaluation result and the actual value is only 0.039. This shows that the effect of the constructed personalized learning resource recommendation model and college students’ comprehensive quality evaluation model is excellent and practical. This provides a feasible method for improving students’ independent learning ability and school management efficiency.

The Relationship Between Knowledge, Attitude, Practice, and Fall Prevention for Childhood in Shanghai, China.

BackgroundEarly childhood fall is a pressing global public health problem and one of the leading causes of child injury. China has a high proportion of children and a high burden of illness from falls. Therefore, educational interventions to prevent childhood fall would be beneficial.MethodsWe used the outcome of knowledge, attitude and practice questionnaire, which was conducted by Pudong New District of Shanghai Municipal Government, to summarize demographic and baseline characteristics grouped by intervention or not, and analyzed descriptive statistics of continuous and categorical variables. A logistic stepwise function model was established to study the influence of different covariables on the degree of injury, and AIC/BIC/AICC was used to select the optimal model. Finally, we carried out single-factor analysis and established a multifactor model by the stepwise function method.ResultsAttitude and actual behavior scores had significant differences. The intervention and control groups had 20.79 ± 3.20 and 20.39 ± 2.89 attitude scores, respectively. Compared to the control group (5.97 ± 1.32), the intervention group had higher actual behavior scores (5.75 ± 1.50). In the univariate analysis results, fathers' education level, mothers' education level, actual behavior and what cares for children had a significant influence on whether children got injured. In multivariate analysis, attitude had a positive influence on whether injured [odds ratio: 1.13 (1.05–1.21), P < 0.001].ConclusionEducational intervention for children and their guardians can effectively reduce the risk of childhood falls, and changes in behavior and attitude are the result of educational influence. Education of childhood fall prevention can be used as a public health intervention to improve children's health.

Reply to the Letter to the Editor from Dr. Seet-Lee and Colleagues.

The authors would like to thank Dr. Seet-Lee and colleagues for their comments on our recently-published manuscript in the International Journal of Sports Medicine 1. Dr. Seet-Lee and colleagues highlighted some inadequacies, specifically when using mean difference or raw mean difference as effect measure. However, when evaluating blood vessel density by different reliable and validated histological procedures, would it be wrong to assume, as we did, that all the obtained measurements from different staining always vary equally, directly and linearly, with the actual parameter under study? Are Dr. Seet-Lee and colleagues assuming that the results of histological observations of blood vessels, marked with different techniques and stains, are neither compatible nor governed by the same measurement scales? We regret, but we are firmly convinced that we proceeded properly. Moreover, we should not assume that standardized effect sizes will make comparisons meaningful 2. Particularly, as the standardized mean difference indicates the difference before and after the intervention in terms of standard deviations instead of actual scores, it assumes that different outcome scales are linear transformation of each other and the standard deviation (SD) is equal across all studies 3.

Creating Crosswalks for Knee Outcomes After ACL Reconstruction Between the KOOS and the IKDC-SKF.

Anterior cruciate ligament (ACL) registries do not all use the same patient-reported outcome measures, limiting comparisons and preventing pooling of data for meta-analysis. Our objective was to create a statistical crosswalk to convert cohort and registry mean Knee Injury and Osteoarthritis Outcome Scores (KOOS) to International Knee Documentation Committee-Subjective Knee Form (IKDC-SKF) scores and vice versa to allow these comparisons. Data from 3 ACL registries were pooled (n = 14,412) and were separated into a training data set (70% of the sample) or a validation data set (30% of the sample). The KOOS and the IKDC-SKF scores were available prior to the operation and at 1, 2, and 5 or 6 years postoperatively. We used equipercentile equating methods to create crosswalks in the training data set and examined accuracy in the validation data set as well as bootstrapping analyses to assess the impact of sample size on accuracy. Preliminary analyses suggested that crosswalks could be attempted: large correlations between scores on the 2 measures (r = 0.84 to 0.94), unidimensionality of scores, and subpopulation invariance were deemed sufficient. When comparing actual scores with crosswalked scores in the validation data set, negligible bias was observed at the group level; however, individual score deviations were variable. The crosswalks are successful for the group level only. Our crosswalks successfully convert between the KOOS and the IKDC-SKF scores to allow for a group-level comparison of registry and other cohort data. These crosswalks allow comparisons among different national ligament registries as well as other research cohorts and studies; they also allow data from different patient-reported outcome measures to be pooled for meta-analysis. These crosswalks have great potential to improve our understanding of recovery after ACL reconstruction and aid in our ongoing efforts to improve outcomes and patient satisfaction, as well as to allow the continued analysis of historical data.

Large-scale functional connectivity predicts cognitive impairment related to type 2 diabetes mellitus.

BACKGROUNDLarge-scale functional connectivity (LSFC) patterns in the brain have unique intrinsic characteristics. Abnormal LSFC patterns have been found in patients with dementia, as well as in those with mild cognitive impairment (MCI), and these patterns predicted their cognitive performance. It has been reported that patients with type 2 diabetes mellitus (T2DM) may develop MCI that could progress to dementia. We investigated whether we could adopt LSFC patterns as discriminative features to predict the cognitive function of patients with T2DM, using connectome-based predictive modeling (CPM) and a support vector machine.AIMTo investigate the utility of LSFC for predicting cognitive impairment related to T2DM more accurately and reliably.METHODSResting-state functional magnetic resonance images were derived from 42 patients with T2DM and 24 healthy controls. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Patients with T2DM were divided into two groups, according to the presence (T2DM-C; n = 16) or absence (T2DM-NC; n = 26) of MCI. Brain regions were marked using Harvard Oxford (HOA-112), automated anatomical labeling (AAL-116), and 264-region functional (Power-264) atlases. LSFC biomarkers for predicting MoCA scores were identified using a new CPM technique. Subsequently, we used a support vector machine based on LSFC patterns for among-group differentiation. The area under the receiver operating characteristic curve determined the appearance of the classification.RESULTSCPM could predict the MoCA scores in patients with T2DM (Pearson’s correlation coefficient between predicted and actual MoCA scores, r = 0.32, P=0.0066 [HOA-112 atlas]; r = 0.32, P=0.0078 [AAL-116 atlas]; r = 0.42, P=0.0038 [Power-264 atlas]), indicating that LSFC patterns represent cognition-level measures in these patients. Positive (anti-correlated) LSFC networks based on the Power-264 atlas showed the best predictive performance; moreover, we observed new brain regions of interest associated with T2DM-related cognition. The area under the receiver operating characteristic curve values (T2DM-NC group vs. T2DM-C group) were 0.65-0.70, with LSFC matrices based on HOA-112 and Power-264 atlases having the highest value (0.70). Most discriminative and attractive LSFCs were related to the default mode network, limbic system, and basal ganglia.CONCLUSIONLSFC provides neuroimaging-based information that may be useful in detecting MCI early and accurately in patients with T2DM.

MODELING AND ANALYSIS OF THE BOOK BORROWING OF STUDENTS IN THE LIBRARY USING PARTIAL DIFFERENTIAL EQUATIONS

The purpose of this paper is to control and judge the big data of students’ learning and living conditions in college education. College students’ book-borrowing data are mined deeply from three aspects, the multi-source preprocessing of students’ borrowing data from university and college libraries, the quantification of students’ book borrowing, and academic performance prediction by learning and book borrowing. The data mining technology analyzes and processes students’ primary information, score information, and book-borrowing information. Students’ book borrowing is modeled and analyzed using the backpropagation neural network (BPNN) algorithm, and the constructed BPNN book-borrowing model’s loss function is optimized based on the partial differential equations. The library access control data and book-borrowing data are used for statistics of the learning behavior frequency. Data such as students’ stay duration in the learning area and attendance rate are input into the analysis model for experiments, and the average absolute error, the mean square error, and the determination coefficient evaluate the prediction results. The results show that as students’ booking borrowing frequency decreases, their scores decrease, and students who often borrow books have strong learning motivations. In Experiment 4, when [Formula: see text] reaches its maximum value, 0.594, the predicted scores by the students’ book-borrowing model have a high correlation with students’ actual scores, indicating that the BPNN algorithm has the best prediction results. The results show that the indicator of students’ book borrowing has significantly improved the model’s prediction performance, and the borrowed book number and book-borrowing frequency are significant in the prediction model construction.

A pilot study for the prediction of liver function related scores using breath biomarkers and machine learning

Volatile organic compounds (VOCs) present in exhaled breath can help in analysing biochemical processes in the human body. Liver diseases can be traced using VOCs as biomarkers for physiological and pathophysiological conditions. In this work, we propose non-invasive and quick breath monitoring approach for early detection and progress monitoring of liver diseases using Isoprene, Limonene, and Dimethyl sulphide (DMS) as potential biomarkers. A pilot study is performed to design a dataset that includes the biomarkers concentration analysed from the breath sample before and after study subjects performed an exercise. A machine learning approach is applied for the prediction of scores for liver function diagnosis. Four regression methods are performed to predict the clinical scores using breath biomarkers data as features set by the machine learning techniques. A significant difference was observed for isoprene concentration (p < 0.01) and for DMS concentration (p < 0.0001) between liver patients and healthy subject’s breath sample. The R-square value between actual clinical score and predicted clinical score is found to be 0.78, 0.82, and 0.85 for CTP score, APRI score, and MELD score, respectively. Our results have shown a promising result with significant different breath profiles between liver patients and healthy volunteers. The use of machine learning for the prediction of scores is found very promising for use of breath biomarkers for liver function diagnosis.

GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease.

The scores of the cognitive function of patients with end-stage renal disease (ESRD) are highly subjective, which tend to affect the results of clinical diagnosis. To overcome this issue, we proposed a novel model to explore the relationship between functional magnetic resonance imaging (fMRI) data and clinical scores, thereby predicting cognitive function scores of patients with ESRD. The model incorporated three parts, namely, graph theoretic algorithm (GTA), whale optimization algorithm (WOA), and least squares support vector regression machine (LSSVRM). It was called GTA-WOA-LSSVRM or GWLS for short. GTA was adopted to calculate the area under the curve (AUC) of topological parameters, which were extracted as the features from the functional networks of the brain. Then, the statistical method and Pearson correlation analysis were used to select the features. Finally, the LSSVRM was built according to the selected features to predict the cognitive function scores of patients with ESRD. Besides, WOA was introduced to optimize the parameters in the LSSVRM kernel function to improve the prediction accuracy. The results validated that the prediction accuracy obtained by GTA-WOA-LSSVRM was higher than several comparable models, such as GTA-SVRM, GTA-LSSVRM, and GTA-WOA-SVRM. In particular, the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of patients with ESRD were 0.92, 0.88, and 4.14%, respectively. The proposed method can more accurately predict the cognitive function scores of ESRD patients and thus helps to understand the pathophysiological mechanism of cognitive dysfunction associated with ESRD.

A New Decision Process for Choosing the Wind Resource Assessment Workflow with the Best Compromise between Accuracy and Costs for a Given Project in Complex Terrain

In wind energy, the accuracy of the estimation of the wind resource has an enormous effect on the expected rate of return of a project. For a given project, the wind resource assessor is faced with a difficult choice of a wide range of simulation tools and workflows with varying accuracies (or “skill”) and costs. There is currently no guideline or process available in the industry for helping with the decision of the most “optimal” choice—and this is particularly challenging in mountainous (or “complex”) terrain. In this work, a new decision process for selecting the Wind Resource Assessment (WRA) workflow that would expect to deliver the best compromise between skill and costs for a given wind energy project is developed, with a focus on complex terrain. This involves estimating the expected skill and cost scores using a set of pre-defined weighted parameters. The new process is designed and tested by applying seven different WRA workflows to five different complex terrain sites. The quality of the decision process is then assessed for all the sites by comparing the decision made (i.e., choice of optimal workflow) using the expected skill and cost scores with the decision made using the actual skill and cost scores (obtained by comparing measurements and simulations at a validation location). The results show that the decision process works well, but the accuracy decreases as the site complexity increases. It is therefore concluded that some of the parameter weightings should be dependent on site complexity. On-going work involves collecting more data from a large range of sites, implementing measures to reduce the subjectivity of the process and developing a reliable and robust automated decision tool for the industry.

THE INFLUENCE OF COLLABORATIVE TEACHING TOWARD GRADUATE STUDENTS’ TEAMWORK CAPACITY

The objective of this research is to reveal the influence of collaborative teaching toward graduate students’ teamwork capacity, including&nbsp; four aspects, which are: sharing, collaboration, concern to rights and welfare of others, and caring to others. This quasi experimental study was conducted with one group pretest-posttest design. There are 38 graduate students of guidance and counseling department as research subjects. Collecting data was conducted by following a Collaborative Teamwork Inventory. The profile of graduate students’ teamwork capacity was analyzed by comparing between actual score and maximal ideal score based on the nomal curve. The effect of collaborative teaching toward students’ capacity was analyzed by a t-test for paired samples. The findings indicated that collaborative teaching was effective to increase students’ teamwork capacity. It was proven that pretest score tend to move towards the “lower level” whereas the posttest score tend to shift to a “higher level.” The analysis of t-test for paired samples indicated that t = -26.69 and p= .000. It does mean that collaborative teaching influenced significantly towards the improvement of students’ teamwork capacity.

Online Learning and Self-Regulation Strategies: Learning Guides Matter

For master’s degree students, self-regulated learning research is limited, even though the number of online learners has exploded in recent years, especially after the international COVID-19 pandemic. This study investigated the effect of a self-regulated learning guide to help students act somewhat more autonomously. To collect the data, a self-regulated learning guide along with a questionnaire and an achievement test was used. Forty master’s degree students (20 students in each group) participated in this study; they were distributed into two groups (A and B). Group A attended online classes with their instructor, while Group B students attended online classes and received the learning guide to help them pinpoint specific strategies in the given learning context. This paper presents the results obtained from the questionnaire distributed to the students and their end-of-course test results by comparing estimated with the actual performance scores. The study concluded that providing students with a learning guide helps them regulate their learning effectively.

Validity and Reliability of Pre-matriculation and Institutional Assessments in Predicting USMLE STEP 1 Success: Lessons From a Traditional 2 x 2 Curricular Model.

PurposeWe have observed that students' performance in our pre-clerkship curriculum does not align well with their United States Medical Licensing Examination (USMLE) STEP1 scores. Students at-risk of failing or underperforming on STEP1 have often excelled on our institutional assessments. We sought to test the validity and reliability of our course assessments in predicting STEP1 scores, and in the process, generate and validate a more accurate prediction model for STEP1 performance.MethodsStudent pre-matriculation and course assessment data of the Class of 2020 (n = 76) is used to generate a stepwise STEP1 prediction model, which is tested with the students of the Class of 2021 (n = 71). Predictions are developed at the time of matriculation and subsequently at the end of each course in the programing language R. For the Class of 2021, the predicted STEP1 score is correlated with their actual STEP1 scores, and data agreement is tested with means-difference plots. A similar model is generated and tested for the Class of 2022.ResultsSTEP1 predictions based on pre-matriculation data are unreliable and fail to identify at-risk students (R2 = 0.02). STEP1 predictions for most year one courses (anatomy, biochemistry, physiology) correlate poorly with students' actual STEP1 scores (R2 = 0.30). STEP1 predictions improve for year two courses (microbiology, pathology, and pharmacology). But integrated courses with customized NBMEs provide more reliable predictions (R2 = 0.66). Predictions based on these integrated courses are reproducible for the Class of 2022.ConclusionMCAT and undergraduate GPA are poor predictors of student's STEP1 scores. Partially integrated courses with biweekly assessments do not promote problem-solving skills and leave students' at-risk of failing STEP1. Only courses with integrated and comprehensive assessments are reliable indicators of students' STEP1 preparation.

Crosswalks between the Oxford hip and knee scores and the HOOS-12 and KOOS-12 instruments

To develop and validate bi-directional crosswalks between the Oxford Hip Score (OHS) and HOOS-12 summary impact score, and between the Oxford Knee Score (OKS) and KOOS-12 summary impact score. Data were sourced from the Australian Orthopaedic Association National Joint Replacement Registry Patient-Reported Outcome Measures Program. Patients undergoing primary joint replacement for osteoarthritis who completed the OHS and HOOS-12 or OKS and KOOS-12 instruments were included in the analysis. An equipercentile method was used to create four crosswalks, with the distribution of scores smoothed using log-linear models prior to equating. Crosswalk validity was assessed through comparison of actual vs derived scores, Pearson correlation coefficients, root mean square errors (RMSE) and Bland-Altman plots. Paired OHS/HOOS-12 data and paired OKS/KOOS-12 data were available for 4,513 patients undergoing total hip replacement and 5,942 patients undergoing total knee replacement, respectively. Minimal differences were observed between actual and crosswalk-derived mean scores (actual OHS 27.55 vs derived OHS 27.56; actual HOOS-12 53.28 vs derived HOOS-12 53.31; actual OKS 27.34 vs derived OKS 27.34; actual KOOS-12 50.51 vs derived KOOS-12 50.58). High correlation was observed between actual and derived scores (Pearson's r for hip-specific instruments: 0.943-0.946; Pearson's r for knee-specific instruments: 0.925-0.931). Plotted actual vs mean derived scores also indicated robust concordance across the breadth of the instrument scales. These crosswalks provide close approximations of actual OHS, OKS, HOOS-12 and KOOS-12 scores, as indicated by multiple validation metrics. They offer a resource for clinicians, researchers and arthroplasty registries to support PROMs score conversion and data harmonisation efforts.

Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach.

Background: Healthcare workers are at high risk for developing mental health problems during the COVID-19 pandemic. There is an urgent need to identify vulnerability and protective factors related to the severity of psychiatric symptoms among healthcare workers to implement targeted prevention and intervention programs to reduce the mental health burden worldwide during COVID-19.Objective: The present study aimed to apply a machine learning approach to predict depression and PTSD symptoms based on psychometric questions that assessed: (1) the level of stress due to being isolated from one's family; (2) professional recognition before and during the pandemic; and (3) altruistic acceptance of risk during the COVID-19 pandemic among healthcare workers.Methods: A total of 437 healthcare workers who experienced some level of isolation at the time of the pandemic participated in the study. Data were collected using a web survey conducted between June 12, 2020, and September 19, 2020. We trained two regression models to predict PTSD and depression symptoms. Pattern regression analyses consisted of a linear epsilon-insensitive support vector machine (ε-SVM). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r), the coefficient of determination (r2), and the normalized mean squared error (NMSE) to evaluate the model performance. A permutation test was applied to estimate significance levels.Results: Results were significant using two different cross-validation strategies to significantly decode both PTSD and depression symptoms. For all of the models, the stress due to social isolation and professional recognition were the variables with the greatest contributions to the predictive function. Interestingly, professional recognition had a negative predictive value, indicating an inverse relationship with PTSD and depression symptoms.Conclusions: Our findings emphasize the protective role of professional recognition and the vulnerability role of the level of stress due to social isolation in the severity of posttraumatic stress and depression symptoms. The insights gleaned from the current study will advance efforts in terms of intervention programs and public health messaging.

A Positive Psychology Perspective on Positive Emotion and Foreign Language Enjoyment Among Chinese as a Second Language Learners Attending Virtual Online Classes in the Emergency Remote Teaching Context Amid the COVID-19 Pandemic.

This study adopted a positive psychology perspective to investigate positive emotion and foreign language enjoyment among Chinese as a second language (CSL) learners in an emergency remote teaching (ERT) context amid the COVID-19 pandemic. A set of 90 preparatory Chinese language students (40 males and 50 females) was assessed for their level of foreign language enjoyment using the Foreign Language Enjoyment Scale (FLES). Participles' scores on self-perceived language achievement and actual test scores were adopted as the measurement of their Chinese language proficiency. The results revealed that: (1) CSL learners experienced high level of FL enjoyment in an online learning context, (2) no significant correlation was found between FLE and leaners' actual language achievement nor between FLE and their self-perceived achievement, (3) female learners showed higher FLE than male learners and gender was found to have a significant effect on FLE-Private, (4) participants' geological location, i.e., whether in China or at home countries, significantly influenced their FLE, (5) participants' regional group was not a significant predictor of FLE, and (6) teacher-related variables and learner self-perceptions of achievement were strong predictors of FLE among CSL learners. The findings highlight the importance of teacher's role in an online learning environment and suggest that FLE may not boost performance in the short term for language beginners but is still conducive in the long run. Implications for both teachers and learners, and suggestions for future researches are provided.

Prognostication in Acute Neurological Emergencies

BackgroundFor patients with acute, serious neurological conditions presenting to the emergency department (ED), prognostication is typically based on clinical experience, scoring systems and patient co-morbidities. Because estimating a poor prognosis influences caregiver decisions to withdraw life-sustaining therapy, we investigated the consistency of prognostication across a spectrum of neurology physicians. MethodsFive acute neurological presentations (2 with large hemispheric infarction; 1 with brainstem infarction, 1 with lobar hemorrhage, and 1 with hypoxic-ischemic encephalopathy) were selected for a department-wide prognostication simulation exercise. All had presented to our tertiary care hospital's ED, where a poor outcome was predicted by the ED neurology team within 24 hours of onset. Relevant clinical, laboratory and imaging data available before ED prognostication were presented on a web-based platform to 120 providers blinded to the actual outcome. The provider was requested to rank-order, from most to least likely, the predicted 90-day modified Rankin Scale (mRS) score. To determine the accuracy of individual outcome predictions we compared the patient's the actual 90-day mRS score to highest ranked predicted mRS score. Additionally, the group's “weighted” outcomes, accounting for the entire spectrum of mRS scores ranked by all respondents, were compared to the actual outcome for each case. Consistency was compared between pre-specified provider roles: neurology trainees versus faculty; non-vascular versus vascular faculty. ResultsResponses ranged from 106-110 per case. Individual predictions were highly variable, with predictions matching the actual mRS scores in as low as 2% of respondents in one case and 95% in another case. However, as a group, the weighted outcome matched the actual mRS score in 3 of 5 cases (60%). There was no significant difference between subgroups based on expertise (stroke/neurocritical care versus other) or experience (faculty versus trainee) in 4 of 5 cases. ConclusionAcute neuro-prognostication is highly variable and often inaccurate among neurology providers. Significant differences are not attributable to experience or subspecialty expertise. The mean outcome prediction from group of providers (“the wisdom of the crowd”) may be superior to that of individual providers.

Utilization of Location-Based Services Using Augmented Reality Perception Method For Android Based School Search

Data technology has become the main means for activities in various sectors of life, one of which is location-based services for SMA/SMK schools in the Bangka Belitung area. By using a location-based service application, it is expected to be able to overcome the problem of finding the position of SMA/SMK in the Bangka Belitung area. In this study, we would like to discuss the search for SMA/SMK positions. Through the push, Location-Based Service will be displayed in the smartphone, which will help identify the presence of the school's position on the smartphone. The result of the application that is formed is an Android-based application that can recognize the presence of SMA/SMK positions. This study aims to create a location-based service application that is combined with the concept of overlaying geographic position information from augmented reality perception, which is one type of augmented reality. The overall application quality test results include 1). Based on the Functionality aspect, the percentage of the actual score got a total score of 91.8%. 2). Based on the aspect of reliability, the percentage of the actual score got a total score of 97.6%. 3). Based on the Usability aspect, the percentage of the actual score gets a total score of 92.2%. 4). Based on the aspect of efficiency, the percentage of the actual score gets a total value of 76.5%. 5). From the calculation of the total score in percent is 89.5%. So based on the total score in percent, it can be concluded that the overall application quality test results are 89.5%, so the prototypes produced in this study are included in very good criteria.

Are high school students accurate in predicting their AP exam scores?: Examining inaccuracy and overconfidence of students’ predictions

ABSTRACT We examined whether students were accurate in predicting their test performance in both low-stakes and high-stakes testing contexts. The sample comprised U.S. high school students enrolled in an advanced placement (AP) statistics course during the 2017–2018 academic year (N = 209; Mage = 16.6 years). We found that even two months before taking the AP exam, a high stakes summative assessment, students were moderately accurate in predicting their actual scores (κweighted = .62). When the same variables were entered into models predicting inaccuracy and overconfidence bias, results did not provide evidence that age, gender, parental education, number of mathematics classes previously taken, or course engagement accounted for variation in accuracy. Overconfidence bias differed between students enrolled at different schools. Results indicated that students’ predictions of performance were positively associated with performance in both low- and high-stakes testing contexts. The findings shed light on ways to leverage students’ self-assessment for learning.

Implementasi Sistem Mobile Learning pada MI Taufiqul Athfal Bogor

The application of technology in education is being developed very intensively today, especially the use of technology to support the teaching and learning process. The transfer of conventional learning methods such as face-to-face in the classroom towards a modern direction such as distance learning using computer-based systems or smartphones (Mobile Learning) has become a common thing implemented in today's education world. But not all educational institutions implement the same thing as MI Taufiqul Athfal Bogor. The purpose of this study was to design and implement a distance learning system based on android with smartphone media (Mobile Learning) to help MI Taufiqul Athfal Bogor in the teaching and learning process. The research method used is the Research &amp; Development (R&amp;D) method. System testing was carried out using the ISO 9126 method by distributing questionnaires to 20 respondents who were teachers at MI Taufiqul Athfal. The test results with four aspects of ISO 9126, namely aspects of Fuctionality, Reliability, Usability, and Efficiency produce an overall Actual Total score, namely Total% Actual of 90%, thus concluding that the quality of the system is Very Good to implement. The final result of this research is an Andiroid-based Mobile Learning system designed by researchers which is implemented very well at MI Taufiqul Athfal and helps support the distance teaching and learning process.