Applied Sciences

Research

21 pages, 2030 KiB

Open AccessArticle

The Clash between CLIL and TELL: Effects and Potential Solutions of Adapting TELL for Online CLIL Teaching

by Rongxin Zhu and Simon S. Y. Chan

Appl. Sci. 2023, 13(7), 4270; https://doi.org/10.3390/app13074270 - 28 Mar 2023

Cited by 2 | Viewed by 3186

The relationship between technology and society is an ever-changing dynamic, but one in which education is a key domain. In educational practice, the use of computer technology has increasingly become an inseparable part of teaching students in numerous ways across the world. The [...] Read more.

The relationship between technology and society is an ever-changing dynamic, but one in which education is a key domain. In educational practice, the use of computer technology has increasingly become an inseparable part of teaching students in numerous ways across the world. The COVID-19 global pandemic accelerated this dramatically, with online teaching environments becoming the sole way for students to access education for extended periods of time. This shift to online teaching also required that teachers learn new skills and deal with new challenges. Based on mixed-methods research conducted with 20 teachers from an established content and language integrated learning school in mainland China, this research paper investigates the different challenges and problems that were faced by content and language integrated learning teachers in their experiences of online teaching and, in tandem with wider content and language integrated learning and technology-enhanced language learning literature, develops some potential solutions for future use. Full article

(This article belongs to the Special Issue Technologies and Environments of Intelligent Education)

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10 pages, 4404 KiB

Open AccessArticle

Extraction and Analysis of Soil Salinization Information in an Alar Reclamation Area Based on Spectral Index Modeling

by Guojun Hong, Tiecheng Bai, Xingpeng Wang, Mingzhe Li, Chengcheng Liu, Lianjie Cong, Xinyi Qu and Xu Li

Appl. Sci. 2023, 13(6), 3440; https://doi.org/10.3390/app13063440 - 8 Mar 2023

Cited by 3 | Viewed by 6063

Abstract

In order to explore the optimal remote sensing salinity monitoring index model for the inversion of soil salinization in the Alar reclamation area, based on the Sentinel-2 images and field measured data, the salinity index 1 (SI1), the normalized difference vegetation index in [...] Read more.

In order to explore the optimal remote sensing salinity monitoring index model for the inversion of soil salinization in the Alar reclamation area, based on the Sentinel-2 images and field measured data, the salinity index 1 (SI1), the normalized difference vegetation index in a green–red band (GRNDVI), the normalized vegetation index of greenness (GNDVI), and the normalized difference vegetation index (NDVI) were selected to construct the remote sensing-based salinization 1 detection index (S1DI) model. Next, the cotton field soil salinization information in the Alar reclamation area was extracted, and the accuracy of the model was verified to obtain the optimal remote sensing salinity monitoring index model. The results show that the overall classification accuracy of the S1DI1 (SI1-GRNDVI), S1DI2 (SI1-GNDVI), S1DI3 (SI1-NDVI), and S1DI4 (SI1-DVI) models for salinity monitoring is 83.35%, 83.10%, 82.96%, and 80.25%, respectively. The S1DI1 model is most suitable for retrieving the degree of soil salinization in the cotton field in the Alar reclamation area, and the S1DI2, S1DI3, and S1DI4 models are also very useful for monitoring soil salinization in the Alar reclamation area. Using the S1DI1 model to invert the soil salinization level of the cotton fields in the Alar reclamation area, it was found that the cotton field in the reclamation area is dominated by non-saline soil, and the light saline soil and moderate saline soil are mainly distributed in the 9th and 12th clusters of the reclamation area. As the S1DI1 model possesses the highest accuracy in extracting the soil salinization information of the cotton fields in the Alar reclamation area, it can be used as a remote sensing salinity 1 monitoring index model for the inversion of the soil salinization of the cotton fields in the reclamation area, which is expected to provide an effective reference value for soil salinization monitoring. Full article

(This article belongs to the Special Issue Technologies and Environments of Intelligent Education)

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20 pages, 3608 KiB

Open AccessArticle

Recognizing Teachers’ Hand Gestures for Effective Non-Verbal Interaction

by Zhenlong Peng, Zhidan Yang, Jianbing Xiahou and Tao Xie

Appl. Sci. 2022, 12(22), 11717; https://doi.org/10.3390/app122211717 - 18 Nov 2022

Cited by 2 | Viewed by 4379

Abstract

Hand gesturing is one of the most useful non-verbal behaviors in the classroom, and can help students activate multi-sensory channels to complement teachers’ verbal behaviors and ultimately enhance teaching effectiveness. The existing mainstream detection algorithms that can be used to recognize hand gestures [...] Read more.

Hand gesturing is one of the most useful non-verbal behaviors in the classroom, and can help students activate multi-sensory channels to complement teachers’ verbal behaviors and ultimately enhance teaching effectiveness. The existing mainstream detection algorithms that can be used to recognize hand gestures suffered from low recognition accuracy under complex backgrounds and different backlight conditions. This study proposes an improved hand gesture recognition framework based on key point statistical transformation features. The proposed framework can effectively reduce the sensitivity of images to background and light conditions. We extracted key points of the image and establish a weak classifier to enhance the anti-interference ability of the algorithm in the case of noise and partial occlusion. Then, we used a deep convolutional neural network model with multi-scale feature fusion to recognize teachers’ hand gestures. A series of experiments were conducted on different human gesture datasets to verify the performance of the proposed framework. The results show that the framework proposed in this study has better detection and recognition rates compared to the you only look once (YOLO) algorithm, YOLOv3, and other counterpart algorithms. The proposed framework not only achieved 98.43%, measured by F1 score, for human gesture images in low-light conditions, but also has good robustness in complex lighting environments. We used the proposed framework to recognize teacher gestures in a case classroom setting, and found that the proposed framework outperformed YOLO and YOLOv3 algorithms on small gesture images with respect to recognition performance and robustness. Full article

(This article belongs to the Special Issue Technologies and Environments of Intelligent Education)

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17 pages, 1597 KiB

Open AccessArticle

Institutional Adoption and Implementation of Blended Learning in the Era of Intelligent Education

by Chunhui Zhang, Mingang Wen, Kuang Tong, Zexuan Chen, Qing Wen, Tingting Yang and Qijun Liu

Appl. Sci. 2022, 12(17), 8846; https://doi.org/10.3390/app12178846 - 2 Sep 2022

Cited by 5 | Viewed by 2705

Abstract

Blended learning (BL) reform is one of the vital methods to improve the teaching quality of institutions in the intelligent education era. However, institutions are always faced with many obstacles (instructors’ reluctance/inability to change, etc.) in conducting the reform. What is worse, very [...] Read more.

Blended learning (BL) reform is one of the vital methods to improve the teaching quality of institutions in the intelligent education era. However, institutions are always faced with many obstacles (instructors’ reluctance/inability to change, etc.) in conducting the reform. What is worse, very few studies have reported the design and effect of such a transformation. This study designed an intervention of institutional BL reform by making a unified deployment based on Graham et al.’s BL adoption framework: identifying strategy, structure, and support issues at three developmental stages. More than 900 courses (involving more than 14,000 students and more than 2000 instructors) within S university were taken as a sample. A quasi-experiment was designed to investigate the effect of the intervention on S university’s BL course transformation, students’ learning, instructors’ professional development, etc. Course logs, responses to students’ course evaluation forms, and instructors’ questionnaires were collected and analyzed. Results indicated that S university systematically conducted the BL transformation and gradually reached the mature implementation stage within 7 years. This study contributes to the literature by reporting a best practice of BL institutional adoption. Three implications, relating to strategy, structure, and support, were drawn to shed light for other institutions in moving forward on BL adoption. Full article

(This article belongs to the Special Issue Technologies and Environments of Intelligent Education)

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19 pages, 1954 KiB

Open AccessArticle

Online Peer-Tutoring for Programming Languages Based on Programming Ability and Teaching Skill

by Yu-Chen Kuo, Ching-Bang Yao and Zhe-Yu Wu

Appl. Sci. 2022, 12(17), 8513; https://doi.org/10.3390/app12178513 - 25 Aug 2022

Cited by 1 | Viewed by 2372

Abstract

Web-based cooperative learning could enhance students’ learning motivation; however, learning activities in this process are rather confusing because of the lack of structured learning strategies, resulting in unfavorable learning achievements. With the peer tutoring learning environment to encourage students’ mutual learning and development, [...] Read more.

Web-based cooperative learning could enhance students’ learning motivation; however, learning activities in this process are rather confusing because of the lack of structured learning strategies, resulting in unfavorable learning achievements. With the peer tutoring learning environment to encourage students’ mutual learning and development, an online peer-tutoring platform for programming languages with peer mentoring is established herein for one-to-one peer tutoring activities. With students with higher learning ability as tutors and those with lower learning ability as tutees, tutors can provide online peer tutoring for programming languages via demonstrations and flowcharts to discuss the effects of using different teaching methods for learning activities on the learning achievement of tutees. Based on these teaching methods for peer learning, 52 undergraduates were divided into experimental groups A and B; each group was further divided into peer mentoring group and non-peer mentoring group based on the ability levels. The results show that learning activities with the online peer-tutoring platform for programming languages could assist both groups in enhancing their learning achievement and ensure positive attitudes toward programming languages. In the analyses, the peer mentoring group was preferable in peer tutoring for programming languages with demonstration, while the non-peer mentoring group did not appear significant. Full article

(This article belongs to the Special Issue Technologies and Environments of Intelligent Education)

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16 pages, 838 KiB

Open AccessEditor’s ChoiceArticle

Using Chatbots as AI Conversational Partners in Language Learning

by Jose Belda-Medina and José Ramón Calvo-Ferrer

Appl. Sci. 2022, 12(17), 8427; https://doi.org/10.3390/app12178427 - 24 Aug 2022

Cited by 72 | Viewed by 22348

Abstract

Recent advances in Artificial Intelligence (AI) and machine learning have paved the way for the increasing adoption of chatbots in language learning. Research published to date has mostly focused on chatbot accuracy and chatbot–human communication from students’ or in-service teachers’ perspectives. This study [...] Read more.

Recent advances in Artificial Intelligence (AI) and machine learning have paved the way for the increasing adoption of chatbots in language learning. Research published to date has mostly focused on chatbot accuracy and chatbot–human communication from students’ or in-service teachers’ perspectives. This study aims to examine the knowledge, level of satisfaction and perceptions concerning the integration of conversational AI in language learning among future educators. In this mixed method research based on convenience sampling, 176 undergraduates from two educational settings, Spain (n = 115) and Poland (n = 61), interacted autonomously with three conversational agents (Replika, Kuki, Wysa) over a four-week period. A learning module about Artificial Intelligence and language learning was specifically designed for this research, including an ad hoc model named the Chatbot–Human Interaction Satisfaction Model (CHISM), which was used by teacher candidates to evaluate different linguistic and technological features of the three conversational agents. Quantitative and qualitative data were gathered through a pre-post-survey based on the CHISM and the TAM2 (technology acceptance) models and a template analysis (TA), and analyzed through IBM SPSS 22 and QDA Miner software. The analysis yielded positive results regarding perceptions concerning the integration of conversational agents in language learning, particularly in relation to perceived ease of use (PeU) and attitudes (AT), but the scores for behavioral intention (BI) were more moderate. The findings also unveiled some gender-related differences regarding participants’ satisfaction with chatbot design and topics of interaction. Full article

(This article belongs to the Special Issue Technologies and Environments of Intelligent Education)

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18 pages, 10070 KiB

Open AccessArticle

Classroom Behavior Detection Based on Improved YOLOv5 Algorithm Combining Multi-Scale Feature Fusion and Attention Mechanism

by Longyu Tang, Tao Xie, Yunong Yang and Hong Wang

Appl. Sci. 2022, 12(13), 6790; https://doi.org/10.3390/app12136790 - 5 Jul 2022

Cited by 26 | Viewed by 4921

Abstract

The detection of students’ behaviors in classroom can provide a guideline for assessing the effectiveness of classroom teaching. This study proposes a classroom behavior detection algorithm using an improved object detection model (i.e., YOLOv5). First, the feature pyramid structure (FPN+PAN) in the neck [...] Read more.

The detection of students’ behaviors in classroom can provide a guideline for assessing the effectiveness of classroom teaching. This study proposes a classroom behavior detection algorithm using an improved object detection model (i.e., YOLOv5). First, the feature pyramid structure (FPN+PAN) in the neck network of the original YOLOv5 model is combined with a weighted bidirectional feature pyramid network (BiFPN). They are subsequently processed with feature fusion of different scales of the object to mine the fine-grained features of different behaviors. Second, a spatial and channel convolutional attention mechanism (CBAM) is added between the neck network and the prediction network to make the model focus on the object information to improve the detection accuracy. Finally, the original non-maximum suppression is improved using the distance-based intersection ratio (DIoU) to improve the discrimination of occluded objects. A series of experiments were conducted on our new established dataset which includes four types of behaviors: listening, looking down, lying down, and standing. The results demonstrated that the algorithm proposed in this study can accurately detect various student behaviors, and the accuracy was higher than that of the YOLOv5 model. By comparing the effects of student behavior detection in different scenarios, the improved algorithm had an average accuracy of 89.8% and a recall of 90.4%, both of which were better than the compared detection algorithms. Full article

(This article belongs to the Special Issue Technologies and Environments of Intelligent Education)

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19 pages, 1216 KiB

Open AccessArticle

Web Applications for Teaching the Respiratory System: Content Validation

by Susana Mejía, Isabel Cristina Muñoz, Leidy Yanet Serna, Carlos Andrés Sarmiento, Carlos Leonardo Bravo and Alher Mauricio Hernández

Appl. Sci. 2022, 12(9), 4289; https://doi.org/10.3390/app12094289 - 24 Apr 2022

Cited by 3 | Viewed by 3064

Abstract

The subject of respiratory mechanics has complex characteristics, functions, and interactions that can be difficult to understand in training and medical education contexts. As such, education strategies based on computational simulations comprise useful tools, but their application in the medical area requires stricter [...] Read more.

The subject of respiratory mechanics has complex characteristics, functions, and interactions that can be difficult to understand in training and medical education contexts. As such, education strategies based on computational simulations comprise useful tools, but their application in the medical area requires stricter validation processes. This paper shows a statistical and a Delphi validation for two modules of a web application used for respiratory system learning: (I) “Anatomy and Physiology” and (II) “Work of Breathing Indexes”. For statistical validation, population and individual analyses were made using a database of healthy men to compare experimental and model-predicted data. For both modules, the predicted values followed the trend marked by the experimental data in the population analysis, while in the individual analysis, the predicted errors were 9.54% and 25.38% for maximal tidal volume and airflow, respectively, and 6.55%, 9.33%, and 11.77% for rapid shallow breathing index, work of breathing, and maximal inspiratory pressure, respectively. For the Delphi validation, an average higher than 4 was obtained after health professionals evaluated both modules from 1 to 5. In conclusion, both modules are good tools for respiratory system learning processes. The studied parameters behaved consistently with the expressions that describe ventilatory dynamics and were correlated with experimental data; furthermore, they had great acceptance by specialists. Full article

(This article belongs to the Special Issue Technologies and Environments of Intelligent Education)

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14 pages, 3101 KiB

Open AccessArticle

Graph Neural Network for Senior High Student’s Grade Prediction

by Yang Yu, Jinfu Fan, Yuanqing Xian and Zhongjie Wang

Appl. Sci. 2022, 12(8), 3881; https://doi.org/10.3390/app12083881 - 12 Apr 2022

Cited by 2 | Viewed by 2385

Abstract

Senior high school education (SHSE) forms a connecting link between the preceding junior high school education and the following college education. Through SHSE, a student not only completes k-12 education, but also lays a foundation for subsequent higher education. The grade of the [...] Read more.

Senior high school education (SHSE) forms a connecting link between the preceding junior high school education and the following college education. Through SHSE, a student not only completes k-12 education, but also lays a foundation for subsequent higher education. The grade of the student in SHSE plays a critical role in college application and admission. Therefore, utilizing the grade of the student as an indicator is a reasonable method to instruct and ensure the effect of SHSE. However, due to the complexity and nonlinearity of the grade prediction problem, it is hard to predict the grade accurately. In this paper, a novel grade prediction model aiming to handle the complexity and nonlinearity is proposed to accurately predict the grade of the senior high student. To deal with the complexity, a graph structure is employed to represent the students’ grades in all subjects. To handle the nonlinearity, the multi-layer perceptron (MLP) is used to learn (or fit) the inner relation of the subject grades. The proposed grade prediction model based on graph neural network is tested on the dataset of Ningbo Xiaoshi High School. The results show that the proposed method performs well in the prediction of senior high school student grades. Full article

(This article belongs to the Special Issue Technologies and Environments of Intelligent Education)

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16 pages, 525 KiB

Open AccessArticle

A2BCF: An Automated ABC-Based Feature Selection Algorithm for Classification Models in an Education Application

by Leila Zahedi, Farid Ghareh Mohammadi and Mohammad Hadi Amini

Appl. Sci. 2022, 12(7), 3553; https://doi.org/10.3390/app12073553 - 31 Mar 2022

Cited by 5 | Viewed by 2330

Abstract

Feature selection is an essential step of preprocessing in Machine Learning (ML) algorithms that can significantly impact the performance of ML models. It is considered one of the most crucial phases of automated ML (AutoML). Feature selection aims to find the optimal subset [...] Read more.

Feature selection is an essential step of preprocessing in Machine Learning (ML) algorithms that can significantly impact the performance of ML models. It is considered one of the most crucial phases of automated ML (AutoML). Feature selection aims to find the optimal subset of features and remove the noninformative features from the dataset. Feature selection also reduces the computational time and makes the data more understandable to the learning model. There are various heuristic search strategies to address combinatorial optimization challenges. This paper develops an Automated Artificial Bee Colony-based algorithm for Feature Selection (A2BCF) to solve a classification problem. The application domain evaluating our proposed algorithm is education science, which solves a binary classification problem, namely, undergraduate student success. The modifications made to the original Artificial Bee Colony algorithm make the algorithm a well-performed approach. Full article

(This article belongs to the Special Issue Technologies and Environments of Intelligent Education)

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17 pages, 17284 KiB

Open AccessArticle

A Study on the Teaching Design of a Hybrid Civics Course Based on the Improved Attention Mechanism

by Wenwu Miao

Appl. Sci. 2022, 12(3), 1243; https://doi.org/10.3390/app12031243 - 25 Jan 2022

Cited by 2 | Viewed by 1680

Abstract

As an important vehicle for moral education, the moral indicators of civics and political science textbooks are naturally some of the most important criteria for revising textbooks. However, the textbook text dataset has too much textual information, ambiguous features, unbalanced sample distributions, etc. [...] Read more.

As an important vehicle for moral education, the moral indicators of civics and political science textbooks are naturally some of the most important criteria for revising textbooks. However, the textbook text dataset has too much textual information, ambiguous features, unbalanced sample distributions, etc. To address these problems, this paper combines a novel data enhancement method to obtain classification results based on word vectors. Additionally, for the problem of unbalanced sample sizes, this paper proposes a network model based on the attention mechanism, which combines the ideas of SMOTE and EDA, and uses a self-built stop word list and synonym word forest to conduct synonym queries, achieve a few categories of oversampling, and randomly disrupt the sentence order and intra-sentence word order to build a balanced dataset. The experimental results also show that the data augmentation method used in this paper’s model can effectively improve the performance of the model, resulting in a higher boost in the F1-measure of the model. The model incorporating the attention mechanism has better model generalization compared to the one without the attention mechanism, as well as a significant advantage compared to the reference model in other settings. The experimental results show that, compared with the original text classifier, the scheme of this paper effectively improves the evaluation effect and the reliability design for teaching a civics course. Full article

(This article belongs to the Special Issue Technologies and Environments of Intelligent Education)

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21 pages, 3230 KiB

Open AccessArticle

Chinese Neural Question Generation: Augmenting Knowledge into Multiple Neural Encoders

by Ming Liu and Jinxu Zhang

Appl. Sci. 2022, 12(3), 1032; https://doi.org/10.3390/app12031032 - 19 Jan 2022

Cited by 3 | Viewed by 2191

Abstract

Neural question generation (NQG) is the task of automatically generating a question from a given passage and answering it with sequence-to-sequence neural models. Passage compression has been proposed to address the challenge of generating questions from a long passage text by only extracting [...] Read more.

Neural question generation (NQG) is the task of automatically generating a question from a given passage and answering it with sequence-to-sequence neural models. Passage compression has been proposed to address the challenge of generating questions from a long passage text by only extracting relevant sentences containing the answer. However, it may not work well if the discarded irrelevant sentences contain the contextual information for the target question. Therefore, this study investigated how to incorporate knowledge triples into the sequence-to-sequence neural model to reduce such contextual information loss and proposed a multi-encoder neural model for Chinese question generation. This approach has been extensively evaluated in a large Chinese question and answer dataset. The study results showed that our approach outperformed the state-of-the-art NQG models by 5.938 points on the BLEU score and 7.120 points on the ROUGE-L score on the average since the proposed model is answer focused, which is helpful to produce an interrogative word matching the answer type. In addition, augmenting the information from the knowledge graph improves the BLEU score by 10.884 points. Finally, we discuss the challenges remaining for Chinese NQG. Full article

(This article belongs to the Special Issue Technologies and Environments of Intelligent Education)

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13 pages, 585 KiB

Open AccessArticle

Intelligent Educational Evaluation of Research Performance between Digital Library and Open Government Data

by Tao-Ming Cheng and Hsing-Yu Hou

Appl. Sci. 2022, 12(2), 791; https://doi.org/10.3390/app12020791 - 13 Jan 2022

Cited by 2 | Viewed by 2353 | Correction

Abstract

This study evaluates institutional research performance in benchmark technological universities in Taiwan through intelligent research databases (SciVal) in digital libraries with Ministry of Education open data to explore the performance of research indicators and the research trend of topic clusters to ascertain accountability [...] Read more.

This study evaluates institutional research performance in benchmark technological universities in Taiwan through intelligent research databases (SciVal) in digital libraries with Ministry of Education open data to explore the performance of research indicators and the research trend of topic clusters to ascertain accountability for decision makers. The research performance of eight benchmark technological universities in Taiwan is compared in this study. In addition, the trends in research topics in the top 10% of journals are explored. Descriptive statistics, correlation, ANOVA, and the Boston Consulting Group matrix were used in this study. Research personnel, publications, productivity, total citations, number of international collaborations, and academic research income in 2018 significantly positively correlated with each other. From 719 records of research topics, topic clusters and school types are the significant factors in research outputs. Biosensors, electrodes, and voltammetry are the leading topic clusters in the research trend. The topic cluster of decision-making, fuzzy sets, and models has the best growth rate in the SciVal results. This analysis provides useful insights to policymakers to improve institutional administration and research resource allocation. Full article

(This article belongs to the Special Issue Technologies and Environments of Intelligent Education)

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