Evaluation Of Learning Methods Research Articles

Application of Deep Learning in Financial Management Evaluation

The competition among enterprises is becoming increasingly fierce. The research on the financial management evaluation model is helpful for enterprises to find possible risks as soon as possible. This paper constructs the financial management evaluation model based on the deep belief network and applies the analytic hierarchy process to determine the weight of financial management evaluation indicators, which is compared with other classical deep learning evaluation methods, such as KNN, SVM-RBF, and SVM linear. It has achieved an accuracy of more than 81%, showing a satisfactory prediction effect, which is of great significance to formulate corresponding countermeasures, strengthen financial management, improve the capital market system, and promote high-quality economic development. In addition, aiming at the problem of abnormal financial data, this paper uses the new sample dataset obtained by principal component analysis for convolution neural network model learning, which enhances the prediction accuracy of the model and fully shows that deep learning is feasible in the research of financial management prediction, and there is still a lot of space to explore.

Outcome-based student assessment enhances academic performance in basic medical laboratory course

Basic medical laboratory courses (BMLCs) play an important role in medical educational courses helping the student acquire three important skills of surgical operating, collaborative learning, and problem solving. The outcome-based student assessment (OBSA) is a learning evaluation method that establishes specific evaluation points based on performance of students in three aspects: surgical operating, collaborative learning, and problem solving in the BMLC curriculum practices. The purpose of the present randomized controlled trial study is to explore the efficiency of OBSA program in BMLCs. The 233 students attending BMLCs were randomly divided into 2 groups, 118 in the OBSA group and 115 in the control group. We conducted multiple-choice examination questions (MCQs) test and two questionnaires with the method of two-sample t test for statistics. The results of MCQs in total eight BMLC blocks showed that the academic performance of the OBSA group was significantly better than that of the control group (P < 0.05). In addition, the average scores of direct observation of procedural skills (DOPS) and mini-experimental evaluation exercise in OBSA group were significantly higher than those in control group (P < 0.05). The majority of the medical students preferred the OBSA and considered OBSA could effectively improve their surgical operating skills (83.9%), collaborative learning skills (92.1%), and problem-solving skills (91.1%). From the above, OBSA is an effective evaluation method for the implementation of the BMLC curriculum.

基于创新人才培养的多元化课程学习评价方式的改革策略

基于创新人才培养的多元化课程学习评价方式的改革策略

Review on Evaluation Criteria of Machine Learning Based on Big Data

In machine learning in the context of big data, It is indispensable link for the evaluation results and indicators of prediction results. Although it is not as important as the core algorithm, it plays a role in related applications and research fields. The role of horizontal comparison of learning algorithms. The article briefly summarizes and reviews the research results of basic machine learning algorithm evaluation standards. Different evaluation methods are applied to different machine learning problems, and the trends and difficulties of machine learning evaluation methods in the future are analyzed.

Use of Deep Multi-Target Prediction to Identify Learning Styles

It is possible to classify students according to the manner they recognize, process, and store information. This classification should be considered when developing adaptive e-learning systems. It also creates a comprehension of the different styles students demonstrate while in the process of learning, which can help adaptive e-learning systems offer advice and instructions to students, teachers, administrators, and parents in order to optimize students’ learning processes. Moreover, e-learning systems using computational and statistical algorithms to analyze students’ learning may offer the opportunity to complement traditional learning evaluation methods with new ones based on analytical intelligence. In this work, we propose a method based on deep multi-target prediction algorithm using Felder–Silverman learning styles model to improve students’ learning evaluation using feature selection, learning styles models, and multiple target classification. As a result, we present a set of features and a model based on an artificial neural network to investigate the possibility of improving the accuracy of automatic learning styles identification. The obtained results show that learning styles allow adaptive e-learning systems to improve the learning processes of students.

Integration Multi-Model to Evaluate the Impact of Surface Water Quality on City Sustainability: A Case from Maanshan City in China

Water pollution is a worldwide problem that needs to be solved urgently and has a significant impact on the efficiency of sustainable cities. The evaluation of water pollution is a Multiple Criteria Decision-Making (MCDM) problem and using a MCDM model can help control water pollution and protect human health. However, different evaluation methods may obtain different results. How to effectively coordinate them to obtain a consensus result is the main aim of this work. The purpose of this article is to develop an ensemble learning evaluation method based on the concept of water quality to help policy-makers better evaluate surface water quality. A valid application is conducted to illustrate the use of the model for the surface water quality evaluation problem, thus demonstrating the effectiveness and feasibility of the proposed model.

Study on MOOC scoring algorithm based on Chinese University MOOC learning behavior data

Existing online learning evaluation methods do not accurately reflect learning effects, which only considers test and assignment scores. A comprehensive evaluation algorithm is proposed in this paper based on the big data of learning behavior. The conversion ratio is taken into account, which is defined by information entropy theory. The algorithm comprehensively considers the learner's multiple learning behaviors, such as viewing videos, doing exercises, taking exams, participating in discussions. The new evaluation algorithm can help learners understand the learning state and maintain their interest.

Fuzzy comprehensive evaluation model of interuniversity collaborative learning based on network

Abstract Learning evaluation is an effective method, which plays an important role in the network education evaluation system. But most of the current network learning evaluation methods still use traditional university education evaluation system, which do not take into account of web-based learning characteristics, and they are difficult to fit the rapid development of interuniversity collaborative learning based on network. Fuzzy comprehensive evaluation method is used to evaluate interuniversity collaborative learning based on the combination of fuzzy theory and analytic hierarchy process. Analytic hierarchy process is used to determine the weight of evaluation factors of each layer and to carry out the consistency check. According to the fuzzy comprehensive evaluation method, we establish interuniversity collaborative learning evaluation mathematical model. The proposed scheme provides a new thought for interuniversity collaborative learning evaluation based on network.

Modeling Bayesian Networks from a conceptual framework for occupational risk analysis

Abstract Occupational risk is the possibility that some element included in a particular work environment can cause damage to someone’s health. Thereby, the risk is understood as the product of probability and consequences. In this sense, risk analysis through probabilistic stochastic techniques, such as Bayesian networks (BN), becomes an important tool to analyze occupational risks. Thus, this article aims to show how BNs are being used in the field of occupational risk analysis, and to develop a conceptual framework for the construction of the BNs. Therefore, a systematic review analogous to the Statement for Reporting Systematic Reviews (PRISMA) protocol was performed, which allowed the evaluation of learning methods with the BN, building models and also for us propose a conceptual framework for the implementation of BNs in the analysis of occupational risks.

Penerapan Media Pembelajaran di Sekolah Praktek

The background of this research come from of the result of research faculty beginners which have been raised in 2011 by title “The Evaluation of Learning Method in Education Media Subject at FKIP UMMY”. Based on that result, researcher interest to develop research about how applying of instructional media in school practice by student field practice of FKIP. This type of the research is qualitative research. Means of collecting data in this researchis the researchers them selves and the team, and comes with interview, observation guidelines, and documentation to assure the validity ofthe dataisbased on standardsof credibility as the standard fit and easier to analyze that data and research would be moreaccurate. Guarantor ofthe validity ofthe datais done byan extension ofthe standardof participation, a more diligen to bservation and triangulation. The results of this research is student field practicehavethe ability and skill in using the media to implement the learning, in spite of the media used simple. The problems in the use of media in school practice is a)a short learning period, b) student field practice in comprehensi on factor in using the media, c) the electricity has not reached the classroom, d) lack of understanding of students field practice in using the media and e) classroom managementnot good, not supportfor the use of instructional media.

Prediction of Genetic Interactions Using Machine Learning and Network Properties.

A genetic interaction (GI) is a type of interaction where the effect of one gene is modified by the effect of one or several other genes. These interactions are important for delineating functional relationships among genes and their corresponding proteins, as well as elucidating complex biological processes and diseases. An important type of GI – synthetic sickness or synthetic lethality – involves two or more genes, where the loss of either gene alone has little impact on cell viability, but the combined loss of all genes leads to a severe decrease in fitness (sickness) or cell death (lethality). The identification of GIs is an important problem for it can help delineate pathways, protein complexes, and regulatory dependencies. Synthetic lethal interactions have important clinical and biological significance, such as providing therapeutically exploitable weaknesses in tumors. While near systematic high-content screening for GIs is possible in single cell organisms such as yeast, the systematic discovery of GIs is extremely difficult in mammalian cells. Therefore, there is a great need for computational approaches to reliably predict GIs, including synthetic lethal interactions, in these organisms. Here, we review the state-of-the-art approaches, strategies, and rigorous evaluation methods for learning and predicting GIs, both under general (healthy/standard laboratory) conditions and under specific contexts, such as diseases.

A Novel Learning Evaluation Method Based on RBF Neural Network

There are many learning evaluation methods, but most of them are subjective, which contains a lot of man-made factors. This paper presents a new learning evaluation method based on radial basis function (RBF) neutral network. By analysis the orthogonal least squares for RBF and determines the center of the basis functions, the model of RBF neural network was constructed. Experimental studies show that the Method Based on RBF Neural Network is effective for learning Evaluation.