The effect of order and co‐ordination of the problem quantities on the difficulty of missing value proportion problems

An experiment was conducted to investigate the effects of cooperative group learning on the problem solving performance of college students in a large introductory physics course. An explicit problem solving strategy was taught in the course, and students practiced using the strategy to solve problems in mixed-ability cooperative groups. A technique was developed to evaluate students’ problem solving performance and determine the difficulty of context-rich problems. It was found that better problem solutions emerged through collaboration than were achieved by individuals working alone. The instructional approach improved the problem solving performance of students at all ability levels.

The effects of two types of Intelligent Computer Aided Instruction (ICAI) and a non-intelligent tutoring system on the development of students' mental models and on performance was explored. One of the two ICAI systems provided both principle-based and task specific advice while the other provided task specific advice only. The non-intelligent CAI provided no advice. The problems presented to students increased in difficulty as the student progressed. It was hypothesized that the mental models of students who interacted with the ICAI that provided both principle-based and task specific advice would be most similar in structure to a “gold standard” model, and that these students would also exhibit the best performance. It was also hypothesized that students who were exposed to the ICAI that provided only task specific advice would perform better, and exhibit more accurate and detailed mental models than would students who interacted with the CAI that provided no advice. Study results indicated that students in both ICAI conditions achieved a higher similarity index, fewer errors, and faster task time, than did students in the no advice group. However, there was no significant difference between the two ICAIs for any of these measures. There was a significant interaction between version and problem difficulty for task time. As problem difficulty increased, task time increased for both of the two ICAI conditions, but not for the no-advice condition. These results are discussed in terms of their implications for the predictive validity of the Pathfinder measure, and in terms of the appropriate use and design of ICAI.

This study identified mathematics anxiety and its effects on students’in performance in Mathematics among Senior School Students in Ekiti State, Nigeria. The sample consisted of 238 SSSII students and 25 math-teachers that were randomly and purposively selected respectively. A questionnaire adapted from the Fennema-Sherman Mathematics Attitudes Scale (FSMAS) was used for the study. Descriptive statistics was used to answer the research questions while t-test was used to test the hypotheses. The study showed that mathematics anxiety exist among senior school students in the study area, which is characterized by feverish feelings in Mathematics class, difficulty in understanding math problem, failure to contribute in Mathematics class, truancy in Mathematics class among others. The study showed that there is a difference in the performance of Mathematics anxious and non-mathematics anxious students as revealed from the t-test. Also, the mean score of the math-anxious student and non-math anxious students were found to be 31.84% and 61.31% respectively. It is concluded from the study that Mathematics anxiety affects students’ performance in Mathematics. Concerned stakeholder should implement policies at secondary school level to extend the time of Mathematics class on time table, provide conducive environment and engage innovative teaching methods for the teaching of Mathematics.

This paper presents an extension of fuzzy-multi-objective genetic algorithm (MOGA) optimization methodology that could effectively be used to find the overall satisfaction of objective functions (selecting the design variables) in the early stages of design process. The coupling of objective functions due to design variables in an engineering design process will result in difficulties in design optimization problems. The primary application of this methodology is the design of a liquid propellant engine with the maximum specific impulse and the minimum weight. The independent design variables in this model are combustion chamber pressure, exit pressure, oxidizer to fuel mass flow rate. To handle the mentioned problems, a fuzzy-multi-objective genetic algorithm optimization methodology is developed based on Pareto optimal set. Liquid propellant engine, F-1 is modeled to illustrate accuracy and efficiency of proposed methodology.

This study aimed to examine the effect of the learning process to the understanding of taxation and accounting student performance. This research is a quantitative research. Data was collected through questionnaires spread directly to the students who are taking courses accounting taxation. This research completed independent sample test to each variable in terms of gender and level. In addition, it tested the effect of the learning process variables on the understanding of tax accounting and accounting understanding of the influence of taxation on student performance. This study was conducted on 62 students. Statistical tests showed that students’ perceptions of understanding of accounting and tax accounting understanding in terms of gender have the same perception, but in terms of students' perceptions of force seems to have different perception. Students' perceptions on taxation and understanding the learning process both in terms of gender and class showed that they have the same perception. Regression testing results showed that the learning process based on the learning case has no significant impact on the understanding of tax accounting, while accounting understanding has a significant influence on student performance.

A neural network‐based model is proposed to estimate missing values of incomplete datasets to augment their size. An autoassociative neural network (AANN), for which the output vector is identical to the input vector, was built for a styrene production process dataset. The proposed model was used to investigate the ability of an AANN to estimate one to three missing variables, evaluating the impact of the size of the datasets used and the level of correlation of the missing values with other process variables. Results show that the proposed AANN model can predict the process data even when the number of records used is relatively small. Moreover, the AANN method is suitable for estimating missing variables with an accuracy that depends on the correlation coefficient of the missing values with other process variables, keeping acceptable estimation for weakly‐correlated variables. Moreover, the model was tested on noisy data, and it is shown that the model trained on noisy data can also predict missing values in an acceptable estimation range.

The quantitative strategy of asset pricing risk management is one of the financial theory research hot spots. In this paper, we use the BEKK-GARCH model based on the relative economic theory to predict the daily volatility of gold and bitcoin; Secondly, we establish a multi-factor model with 16 factors to predict the return rate of the two assets; Finally, we establish WR model to determine the optimal position proportion of investors every day. In building the model, we use the data in the previous period to continuously slide and train our model to prevent future information leakage. In data processing, we choose the base point (0.01%) as the measurement unit, and the missing value is filled in by the average of the data of two days before and after it.

In industrial process, the method based on principal component analysis (PCA) for data reconstruction is popular during dealing with missing data. In the method, the objective for data reconstruction is to make minimum square prediction error (SPE), an index measuring the relationship among process variables. However, PCA is a special case of factor analysis (FA), it has more limitations than FA, above all, SPE is a Euclidian distance, which is a suitable measurement for variables meeting uniform distribution rather than normal distribution, while process variables often satisfy the latter distribution. Due to the extensive sense of FA, the paper proposes a Mahalanobis distance as the index to measure the degree that the sample accords with the FA model, and then introduces FA into data reconstruction. The proposed index can more reflect the relationship among variables and the estimation of missing data with more precision can be achieved by making the new index minimum than by SPE.

Although many studies have reported on the vocabulary level and syntactical structures that cause difficulty for hearing-impaired students, only recently have researchers interested in the problems of assessing the classroom learning of deaf students evidenced an interest in the item format used on the evaluation instrument. Students enrolled in a course offered through the Physics Learning Center at the National Technical Institute for the Deaf were given a 100-item final examination in which six items appeared in both a true-false and a multiple-choice version. Results of this investigation indicate that item format alone may be a factor affecting the performance of deaf students and that different conclusions may be possible concerning specific course objectives mastered. A need for more research in this important area is indicated.

This research investigates the development of an adaptive blended teaching model (ABTM) that employs customized instructional strategies to optimize learning outcomes. The approach harnesses data-driven insights derived from student performance, behavior, and engagement to provide a personalized educational experience tailored to each student’s requirements. The integration of big data analytics and machine learning (ML) in education presents significant potential to transform traditional teaching methodologies. Data is collected from open sources, including engagement scores, assessment results, forum participation, attendance, and study hours. Preprocessing steps include data cleaning, normalization, and handling missing values to ensure data reliability. The term frequency-inverse document frequency (TF-IDF) text mining technique is utilized to extract features from student-generated content, highlighting essential phrases. TF-IDF enables the identification of critical learning themes and areas requiring additional support. A hybrid method, namely, the snow leopard optimized-tuned intelligent CatBoost (SLO-ICatBoost), is deployed for predicting student grades, assessing performance, and enhancing the educational process. The SLO improves the selection of relevant features, while the ICatBoost algorithm classifies students based on their performance patterns and learning behaviors. When compared to conventional teaching techniques, the proposed SLO-ICatBoost method significantly improves precision (0.990), accuracy (0.991), F1-score (0.990), and recall (0.991). Due to its flexibility in accommodating various learning environments and individual requirements, this approach can be applied in diverse educational settings.

The ever increasing importance of education has drivenresearchers and educators to seek innovative methods forenhancing student performance and understanding the factorsthat contribute to academic success. This paper presents a methodology for predicting CGPA SGPA that leverages machine learning techniques to forecast students'academic achievements based on a variety of features, such asdemographic information, academic history, and behavioural patterns. The proposed students academic performance method utilizes a real-world collected dataset from multiple educational institutions toensure an accurate and comprehensive analysis. The proposed methodology starts with a data preparationstage, where the data is cleansed and organized for analysis. This process encompasses tasks such as handling missing values, scaling the data, and transforming variables ifnecessary. The feature analysis technique was used to select the most important features for the students academic performance model. A number ofmachine learning classifiers were tested, and the feature analysis was found to be the best performer. The results of this study demonstrate the potential of algorithms in predicting student performance andidentifying key factors that influence academic success. This information can be leveraged by educators and academicinstitutions to develop targeted intervention strategies, tailoredlearning experiences, and personalized recommendations forstudents, ultimately fostering a more effective learningenvironment and improving overall educational outcomes.

This work presents a sequential data analysis path, which was successfully applied to identify important patterns (fingerprints) in mammalian cell culture process data regarding process variables, time evolution and process response. The data set incorporates 116 fed-batch cultivation experiments for the production of a Fc-Fusion protein. Having precharacterized the evolutions of the investigated variables and manipulated parameters with univariate analysis, principal component analysis (PCA) and partial least squares regression (PLSR) are used for further investigation. The first major objective is to capture and understand the interaction structure and dynamic behavior of the process variables and the titer (process response) using different models. The second major objective is to evaluate those models regarding their capability to characterize and predict the titer production. Moreover, the effects of data unfolding, imputation of missing data, phase separation, and variable transformation on the performance of the models are evaluated.

A PMU data recovering method based on preferred selection strategy

Duocentered networks

and Introduction: Physical rehabilitation is vital for patients to regain maximum function. Approximately 80% of people with a disability live in developing countries, where they face multiple challenges in rehabilitation. The goal of the study was to conduct an analysis of indoor rehabilitation programs based on the demographics and medical conditions of the admitted patients and to relate to the available basic health and rehabilitation facilities. This was a mixed method study conducted in an inpatient rehabilitation ward of a tertiary level academic university hospital in a developing country. All admitted patients who stayed for a period of minimum two weeks were included in the study. Demographic and clinical data were obtained by means of a retrospective medical record review utilizing a standardized data extraction form. The study was further strengthened by an online literature search for the available documents for analysis, relation, and discussion. Among the 1,309 admitted patients was male- female ratio was 10:7, with the majority (31.4%) cases falling between the ages of 46 and 60yrs. Rehabilitation outpatient department was the principal mode of admission (78%), and musculoskeletal and neurological conditions represented the maximum number (79.8%). Majority of patients (60.8%) were discharged home on completion of the rehabilitation program with a large number of patients who were absconded. Poor health budget allocation and lack of prioritization of the rehabilitation sector face multiple challenges, including the rehabilitation team functioning resources, space crisis for expansion which was further impacted by the COVID-19 pandemic. The country's current health-related rehabilitation process and socio-demographic variables have a negative relationship. There was a large number of missing data in the medical records and many patients were lost prematurely from the indoor rehabilitation program. Musculoskeletal disorders were common, and the majority of patients were discharged home once the program was completed.