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Roles of gender, study major, and origins in accounting learning: A case in Thailand
نقش جنسیت، مطالعه اصلی و ریشه های یادگیری حسابداری: مورد در تایلند-2021 Management students are required to pass several quantitative subjects, such as Accounting,
Business Finance, and Mathematics, during their study at the undergraduate level. There are
limited studies conducted in Thailand that explored students’ learning achievement in accounting
courses. This paper explored the learning achievement of undergraduate management students in
the introductory accounting course at a public university in Thailand. It examined whether the
achievement differs across the students’ gender, study major, and origins. Data from 906 man-
agement students were taken as samples. This study relied on the independent samples t-test and
one-way ANOVA to analyze the data. The results suggested that the performance of undergrad-
uate management students in the accounting course differs significantly across genders, majors,
and origins of the students. keywords: عملکرد یادگیری | دانش آموزان مدیریت | آموزش حسابداری | جنسیت | مطالعه اصلی | ریشه | Learning performance | Management students | Accounting education | Gender | Study major | Origins |
مقاله انگلیسی |
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The effect of online review exercises on student course engagement and learning performance: A case study of an introductory financial accounting course at an international joint venture university
تأثیر تمرینات بررسی آنلاین در تعامل دوره دانشجویی و عملکرد یادگیری: مطالعه موردی یک دوره حسابداری مقدماتی مالی در یک دانشگاه سرمایه گذاری مشترک بین المللی-2021 Prior literature suggests that Chinese students studying in Western Higher Education
Institutions (HEIs) tend to underperform compared to local students. Yet few studies have
explored the effect of learning and assessment tasks on the engagement and performance
of Chinese students who are undergoing a transition into the Western learning environment. We design two online review exercises, which are summative assessments with a formative aspect, for an introductory financial accounting subject and study the effect of
these tasks on a group of business students enrolled in the course at an international joint
venture university based in China. We find that the online review exercises increase student engagement. Students spent a significant amount of time preparing for the online
review exercises both before making their initial attempt and between each attempt.
Students undertook a variety of learning activities in completing the online review exercises and their understanding of the subject improved as a result of going through the process. Student performance in the midterm and final exams is positively related to their
efforts in completing the online review exercises. The findings are of relevance to accounting educators in both the Western HEIs and traditional Chinese universities who are interested in enhancing the learning performance of Chinese business students.
keywords: ارزیابی آنلاین | دانش آموز چینی | نامزدی | عملکرد یادگیری | آموزش حسابداری | Online assessment | Chinese student | Engagement | Learning performance | Accounting education |
مقاله انگلیسی |
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Improving students’ satisfaction and learning performance using flipped classroom
بهبود رضایت و عملکرد یادگیری دانش آموزان با استفاده از کلاس واگردی-2020 Flipped learning have emerged in the last decade to make students’ learning more active and to enhance learning performance and students’ motivation and engagement. These new approaches have been applied to different subjects at different educational levels. Specifically, in higher education most studies have focused on STEM subjects (see Lundin et al., 2018). In this paper, we present an experience about flipped classroom in two courses related to business management in a Spanish university. Results show that students are very satisfied with the experience and with the summative-formative assessment that has been used. In addition, students consider that their learning process has been better with this new methodology. Certainly, academic results have been improved with flipping courses, compared with traditional lectures. However, this type of methodology implies several challenges for both professors and students (some of them demonstrate resistance) and some limitations must also be considered. Keywords: Flipped classroom | Student satisfaction | Learning performance | Formative assessment | Higher education |
مقاله انگلیسی |
4 |
XCS with opponent modelling for concurrent reinforcement learners
XCS با مدل سازی حریف برای یادگیرنده تقویتی همزمان-2020 Reinforcement learning (RL) of optimal policies against an opponent agent also with learning capabil- ity is still challenging in Markov games. A variety of algorithms have been proposed for solving this problem such as the traditional Q-learning-based RL (QbRL) algorithms as well as the state-of-the-art neural-network-based RL (NNbRL) algorithms. However, the QbRL approaches have poor generalization capability for complex problems with non-stationary opponents, while the learned policies by NNbRL al- gorithms are lack of explainability and transparency. In this paper, we propose an algorithm X-OMQ( λ) that integrates eXtended Classifier System (XCS) with opponent modelling for concurrent reinforcement learners in zero-sum Markov Games. The algorithm can learn general, accurate, and interpretable action selection rules and allow policy optimization using the genetic algorithm (GA). Besides, the X-OMQ( λ) agent optimizes the established opponent’s model while simultaneously learning to select actions in a goal-directed manner. In addition, we use the eligibility trace mechanism to further speed up the learn- ing process. In the reinforcement component, not only the classifiers in the action set are updated, but other relevant classifiers are also updated in a certain proportion. We demonstrate the performance of the proposed algorithm in the hunter prey problem and two adversarial soccer scenarios where the op- ponent is allowed to learn with several benchmark QbRL and NNbRL algorithms. The results show that our method has similar learning performance with the NNbRL algorithms while our method requires no prior knowledge of the opponent or the environment. Moreover, the learned action selection rules are also interpretable while having generalization capability. Keywords: Opponent modelling | XCS | Markov games | Reinforcement learning |
مقاله انگلیسی |
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Determinants of the management learning performance in ERP context
عوامل تعیین کننده عملکرد یادگیری مدیریت در زمینه ERP-2020 Management learning poses some challenges, firstly students should identify all administration areas and sec- ondly, they should understand the big picture of an organizational context, by integrating all the studied areas. Enterprise Resource Planning (ERP) systems are the backbone of any organization, in terms of information management systems integration. The usage of these systems is important in terms of management in any or- ganization, and ERPs can facilitate the management learning process. The main objectives of this study are to understand if the ERP usage supports management learning, and to identify the main determinants of individual performance. This study presents a success model of ERP usage for learning management context. The model was validated empirically through a survey answered by university management students. The results show that system quality, process quality, and training play a determinant role in the students performance. Keywords: Social networking sites | Human resource management | Selection | Cyberbetting | Mixed-methods | Information science | Social media | Information management | Business | Business management | Strategic management | Psychology | Organizational psychology | Digital media |
مقاله انگلیسی |
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Big data analytics as an operational excellence approach to enhance sustainable supply chain performance
تجزیه و تحلیل داده های بزرگ به عنوان یک رویکرد برتری عملیاتی برای افزایش عملکرد پایدار زنجیره تأمین-2020 Operations management is a core organizational function involved in the management of activities to produce
and deliver products and services. Appropriate operations decisions rely on assessing and using information; a
task made more challenging in the Big Data era. Effective management of data (big data analytics; BDA), along
with staff capabilities (the talent capability in the use of big data) support firms to leverage big data analytics
and organizational learning in support of sustainable supply chain management outcomes. The current study
uses dynamic capability theory as a foundation for evaluating the role of BDA capability as an operational
excellence approach in improving sustainable supply chain performance. We surveyed mining executives in the
emerging economy of South Africa and received 520 valid responses (47% response rate). We used Partial Least
Squares Structural Equation Modelling (PLS-SEM) to analyze the data. The findings show that big data analytics
management capabilities have a strong and significant effect on innovative green product development and
sustainable supply chain outcomes. Big data analytics talent capabilities have a weaker but still significant effect
on employee development and sustainable supply chain outcomes. Innovation and learning performance affect
sustainable supply chain performance, and supply chain innovativeness has an important moderating role. A
contribution of the study is identifying two pathways that managers can use to improve sustainable supply chain
outcomes in the mining industry, based on big data analytics capabilities. Keywords: Big data analytics | Operational excellence | Dynamic capability view | Supply chain sustainability | Learning performance |
مقاله انگلیسی |
7 |
Progressive Operational Perceptrons with Memory
ادراک عملیاتی تصاعدی با حافظه-2020 Generalized Operational Perceptron (GOP) was proposed to generalize the linear neuron model used in the traditional Multilayer Perceptron (MLP) by mimicking the synaptic connections of biological neu- rons showing nonlinear neurochemical behaviours. Previously, Progressive Operational Perceptron (POP) was proposed to train a multilayer network of GOPs which is formed layer-wise in a progressive man- ner. While achieving superior learning performance over other types of networks, POP has a high com- putational complexity. In this work, we propose POPfast, an improved variant of POP that signicantly reduces the computational complexity of POP, thus accelerating the training time of GOP networks. In addition, we also propose major architectural modications of POPfast that can augment the progressive learning process of POP by incorporating an information preserving, linear projection path from the input to the output layer at each progressive step. The proposed extensions can be interpreted as a mechanism that provides direct information extracted from the previously learned layers to the network, hence the term “memory”. This allows the network to learn deeper architectures and better data representations. An extensive set of experiments in human action, object, facial identity and scene recognition problems demonstrates that the proposed algorithms can train GOP networks much faster than POPs while achiev- ing better performance compared to original POPs and other related algorithms. Keywords: Generalized operational perceptron | Progressive learning | Neural architecture learning |
مقاله انگلیسی |
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Auditory learning in an operant task with social reinforcement is dependent on neuroestrogen synthesis in the male songbird auditory cortex
یادگیری شنیداری در یک کار عملی با تقویت اجتماعی وابسته به سنتز نورواستروژن در قشر شنوایی پرنده آوازخوان-2020 Animals continually assess their environment for cues associated with threats, competitors, allies, mates or prey,
and experience is crucial for those associations. The auditory cortex is important for these computations to
enable valence assignment and associative learning. The caudomedial nidopallium (NCM) is part of the songbird
auditory association cortex and it is implicated in juvenile song learning, song memorization, and song perception.
Like human auditory cortex, NCM is a site of action of estradiol (E2) and is enriched with the enzyme
aromatase (E2-synthase). However, it is unclear how E2 modulates auditory learning and perception in the
vertebrate auditory cortex. In this study we employ a novel, auditory-dependent operant task governed by social
reinforcement to test the hypothesis that neuro-E2 synthesis supports auditory learning in adult male zebra
finches. We show that local suppression of aromatase activity in NCM disrupts auditory association learning. By
contrast, post-learning performance is unaffected by either NCM aromatase blockade or NCM pharmacological
inactivation, suggesting that NCM E2 production and even NCM itself are not required for post-learning auditory
discrimination or memory retrieval. Therefore, neuroestrogen synthesis in auditory cortex supports the association
between sounds and behaviorally relevant consequences. Keywords: Audition | Estradiol | Zebra finch | Neuroestrogen | Nongenomic | Vocal learning |
مقاله انگلیسی |
9 |
A tradeoff relationship between internal monitoring and external feedback during the dynamic process of reinforcement learning
رابطه مبادله ای بین نظارت داخلی و بازخورد خارجی در طی فرآیند پویای یادگیری تقویتی-2020 Effective behavior monitoring, including internal monitoring/error detection and external monitoring/feedback,
is very pivotal for reinforcement learning. However, less attention has been paid to internal monitoring and the
dynamic learning performance in reinforcement learning, and there is still a heated debate on which kind of
external feedback is relied on in the reinforcement learning. In order to address these questions, an adaption
probabilistic selection task was used to examine the effect of the internal monitoring, external feedback and the
relationship between them for approach learners and avoidance learners during dynamic learning process of
reinforcement learning and behavior adaption. Error-related negativity (ERN), feedback-related negativity
(FRN) and feedback-related P300 are three ERPs components, which can be used as the indexes of internal
monitoring, external feedback and behavior adaption. For our results, the ERN effect of avoidance learners
become large in block 3, which is earlier than approach learners (block 4). This phenomenon suggests that
avoidance learners learned faster than approach learners. In addition, the FRN amplitude of avoidance learners
in block 4 was significantly smaller than the other three blocks. The aforementioned results demonstrated a
tradeoff relationship between the ERN and FRN effects. Keywords: Reinforcement learning | ERN | FRN | Avoidance learner | Approach learner |
مقاله انگلیسی |
10 |
On initial population generation in feature subset selection
تولید جمعیت اولیه در انتخاب زیر مجموعه ویژگی-2019 Performance of evolutionary algorithms depends on many factors such as population size, number of generations, crossover or mutation probability, etc. Generating the initial population is one of the impor- tant steps in evolutionary algorithms. A poor initial population may unnecessarily increase the number of searches or it may cause the algorithm to converge at local optima. In this study, we aim to find a promis- ing method for generating the initial population, in the Feature Subset Selection (FSS) domain. FSS is not considered as an expert system by itself, yet it constitutes a significant step in many expert systems. It eliminates redundancy in data, which decreases training time and improves solution quality. To achieve our goal, we compare a total of five different initial population generation methods; Information Gain Ranking (IGR), greedy approach and three types of random approaches. We evaluate these methods using a specialized Teaching Learning Based Optimization searching algorithm (MTLBO-MD), and three super- vised learning classifiers: Logistic Regression, Support Vector Machines, and Extreme Learning Machine. In our experiments, we employ 12 publicly available datasets, mostly obtained from the well-known UCI Machine Learning Repository. According to their feature sizes and instance counts, we manually classify these datasets as small, medium, or large-sized. Experimental results indicate that all tested methods achieve similar solutions on small-sized datasets. For medium-sized and large-sized datasets, however, the IGR method provides a better starting point in terms of execution time and learning performance. Finally, when compared with other studies in literature, the IGR method proves to be a viable option for initial population generation. Keywords: Feature subset selection | Initial population | Multiobjective optimization |
مقاله انگلیسی |