An Open-Source Intelligence-Driven Analysis of International Students’ Blended Learning in China

Yuanbo Qi^1*, Yijun Meng², Yuxin Luo¹, Jiayan Chen¹

¹School of Humanities, Donghua University, Shanghai, China.
²Accounting School, Shanghai University of International Business and Economics, Shanghai, China.

DOI: 10.4236/chnstd.2023.121001                

Abstract

This paper examines the management of international students’ online blended learning based on a comprehensive analysis through open-source intelligence and data mining of associated discourses. This work reconstructs the assessment model of the online blended learning effect for overseas students and rebalances the process of this educational experience, leading to the forecast of China’s strategic manoeuvres through the lenses of the newsboy model and game theory. This study concludes with three suggestions: boosting the entire experience via scaled growth, enhancing and preventing resilience, and reaching out to a third party for skills reinforcement.

Keywords

Online Blended Learning, International Students, Open-Source Intelligence (OSINT)

1. Introduction

A rapidly growing demographic in many countries, international students have emerged as a fiercely competitive market as countries compete to attract this group’s attention and advance national economies (Abbitt & Boone, 2021; Abrahams et al., 2019; Adascalitei et al., 2021). It integrates the need for the education system to provide blended online learning for all groups of students, including international and exchange students abroad in China (Addo-Atuah et al., 2014; Adikhanov & Sagyndykova, 2016). This encounter combines electronic information with fresh, emerging materials, a highly digital, blended experience with traditional information, and a plethora of additional cutting-edge and related visions (Ashour, 2020). There is without a doubt a sizable study gap regarding the online blended learning experiences of international students in China, and this gap might be further researched. A larger risk and reputation among foreign students, as well as the Chinese experience with online blended learning, can be attributed to the structural difficulties that are urgent technologically, the reliance on the conventional, but the teaching abilities and marginal talent scores earlier (Avgerinou et al., 2014; Baig et al., 2019). From the viewpoints of institutional technological supply, student demand, operational process improvement, instructional style, and many other views, this problem is tackled in a variety of ways. While taking into account their significance to the overall influencing elements, academic efforts have been made to integrate these viewpoints into the online blended learning management framework (Bamber & Pike, 2013; Baroni & Lazzari, 2022).

This study has collected 5546 items regarding public opinion from those international students and their comments made online from 2021 January 13 to July 13, randomly selected of a half-year range. In the process of detecting and controlling the security of the online blended learning effect, open-source intelligence (OSINT) is highly efficient and looks to be able to pinpoint specific risk indicators (Binda & Stofkova, 2017; Bolon et al., 2020). It remains essential and is a prerequisite for careful management and decision-making (Qi, 2019, 2020b, 2022). OSINT is an effective way to draw information from sizable public data sets in line with specified goals by using learning patterns for sophisticated recognition and processing (Bovill, 2020; Brahimi & Sarirete, 2015). Administrators can now utilise data mining and OSINT approaches to make detailed assessments because of the Internet’s rapid rise in terms of data gathering, sophisticated algorithms, and data-generating technologies (Bulaeva et al., 2017; Burvill et al., 2022).

Theoretical research has identified one of the most efficient methods for examining the effects of online blended learning, particularly for coordination and elaboration between multiple blended learning effect components, such as from the teachers to the students’ sides (Byrne et al., 2016). Comprehensive research utilising game theory to analyze the online blended learning effect of overseas students are not possible due to the complexity and openness of the online blended learning impact system, which has been restrained by and concurrently responds to external settings (Chatterjee et al., 2014; Cheng, 2022). Because of this, maintaining the stability of a system for the online blended learning impact and conducting research on management and optimising a system for the online blended learning effect in light of the current situation are important academic pathways (Clausen et al., 2018; Coates & Dickinson, 2012; Connolly & Hall, 2021; Corovic et al., 2016). By analysing the impact through OSINT and measuring public opinion among the student groups, given the most alarming points in the online blended learning effect field through OSINT, this paper examines the online blended learning experiences of international students who studied in China (Crosthwaite et al., 2021; Davis & Fill, 2007). As a theoretical foundation for the management of international students, this study presents optimization and response strategy recommendations for the online blended learning experience for Chinese overseas students.

2. Open-Source Data Mining

2.1. Research Methods

NLP algorithm: The accuracy and recall rate of existing language models are updated through the use of self-supervised learning cognition of artificial intelligence models such as BERT, LSTM, and CRF, as well as through the construction of large-scale machine learning datasets and manually labelling sample data (Qi, 2020a; Qi et al., 2022; Zhao et al., 2021). As a result, significant heterogeneous text data processing and risk information mining are made visible (Di Marco et al., 2020; Dias & Diniz, 2012; Ding et al., 2021).

Risk identification model: By integrating the learning of several sub-models, this combined model for risk identification not only has the final classification effect but also evaluates various aspects of the online blended learning effect influencing factors for international students (Zhao et al., 2021). The time-series heat model, the Bert language model, the topic word popularity model, the communication models, etc. are some of these sub-models (Dorsett et al., 2019; Marcillo-Gómez & Desilus, 2016; Stewart & Lowenthal, 2022).

Tools for assessment: Based on our previously developed online public opinion analytic system and blended learning big data system created and tested by our team for the web crawler, semantic text analysis, algorithm, and factor identification model calculation, the analytical results are visualised as time series trend and word cloud chart, along with other objects (Baig et al., 2019; Memon & Rathore, 2018).

2.2. Net Density Analysis in Online Blended Learning for International Students

Net density is the entire amount of network information in certain domains as reflected by the demands and expressions of network entities in cyberspace through network channels. Positive and negative net density are separated; the higher the volume in the negative, the higher the potential risk. This section defines the keywords of the international students’ online blended learning effect and contrasts, using OSINT, the network voice of the positive and negative international students’ online blended learning effect. The negative volume makes up a greater percentage of the total than the positive volume, and this trend may continue for the foreseeable future. From the end of October to the beginning of November 2022, a clear culmination of negative views from the international students toward online blended learning is shown. A sharp rise in negative opinions has been noted, signalling the full release of the previously aroused unpleasant emotion. In contrast, overseas students’ favourable opinions of blended learning remained largely steady over time. This shows a gradual decline in the favourable perceptions of a blended learning environment. Regarding the whole amount of information, the chain’s overall risk is high, and its risk elements are still potentially present.

2.3. Hostile Public Opinion in Online Blended Learning for International Students

This study suggests the POI index, a thorough evaluation of the across-platforms spreading of unfavourable public opinion, since it is universally associated with a greater risk predictability capacity. The maximum monitoring date index is over 400, and the POI index is over 100. The result shows a faster ratio of wide-spreading in the chain risks at a much broader range, which denotes a higher likelihood of running into problems in the future. This is due to the more frequent emergences of the orange warning and the recent culmination of the POI index.

2.4. Word Cloud Analysis in Online Blended Learning for International Students

The setting scheme inputs the keyword “international students”, with the most frequent words being Teaching, School, WeChat, Team, Versions, Channels, and Imagine. This is in accordance with some widely acknowledged pragmatic issues of the Chinese international students online blended learning effect. This procedure creates the word cloud graphic for the online blended learning experience for overseas students (see Figure 1).

The most often used words are “China”, “Teaching”, “WeChat”, “systems”, and “School”, as shown in Figure 1. It demonstrates the significant influence that national strategies, technological integration, and teaching methods have on blended learning in China. All observable macroenvironmental influences alter the outcome, and fundamental technological advancements, data, continue to be significant determinants of who will prevail in the fight for the best future pedagogy.

2.5. Emotional Distribution of International Student’s Towards Online Blended Learning in China

Figure 2 depicts the overall emotional distribution of seven distinct emotional distributions of overseas students and their perceptions of online blended learning in China, including praise, astonishment, disgust, anger, joy, and fear. The distribution of the seven emotions is as follows: praise makes up the majority, while fear accounts for only 8% of the total. The second most prevalent online emotion measured by the public opinion poll is sadness, which may be related to homesickness and many other concerns that may be traced back to the study experience. The remaining emotions stay equal and balanced.

Figure 1. Word cloud diagram for online blended learning with multinational participants.

Figure 2. Emotional distribution of international student’s towards blended learning in China.

2.6. Region of Online Blended Learning Mentioned for International Students in China

This study’s findings show how overseas students who spoke about their opinions of blended learning were distributed across the aforementioned regions and provinces in China. Beijing and the province of Guangdong are the two regions that receive the most referrals. Shanghai is also commonly highlighted by overseas students while discussing the mixed learning environment in China.

3. Implications on Blended Learning for International Students in China

Based on data mining and analysis combined with the encountered issues of China’s international blended learning effect, this part offers three suggestions for the safety management and optimization of the Chinese international students’ online blended learning effect.

3.1. Increase Overall experience via Scaled Expansion

The reasonable decision regarding whether to import or self-develop important technology components depends on various categories; among them, reducers remain fundamentally essential, and it is recommended that they be produced in-house. Regarding the international student structure, there are opportunities for Chinese businesses to improve their inventive capacity, so that industrial large-scale automation can replace low-end production (Holmner & Bothma, 2018; Kurek & Mueller-Hartmann, 2019; Woo-Seok et al., 2006). However, China benefits from system integration. The application area for international students is expansive, and the cross-domain operating capability is suitably scalable (Jahn et al., 2016; Townley et al., 2003; Worley et al., 2016).

3.2. Enhance the Online Blended Learning Resilience

China is dependent on reducing vulnerability and boosting risk prevention capacity (Erlich et al., 2021; Harden & Hart, 2002; Jiang, 2022). To do this, R&D expenditures on autonomous technology require a greater budget. Self-developed technologies would ideally be increasing their home market share, if not their worldwide market share, as part of the online blended learning experience for international students (Garner et al., 2009; Jonas & Burns, 2010; Stephens & Hennefer, 2013).

3.3. Enhancing Capability through Third-Party Cooperation

In many sectors, China’s competition is still weak in contrast, and the online blended learning in the education sector is only a small portion of the whole technology front (Kelly et al., 2009; McNally et al., 2019; Young & Randall, 2014). It will be desirable to seek outside assistance if China hopes to win this competition. The first step might be to assess the impact of international students’ online blended learning on other countries and look for diversified channels to rebuild the system while easing the restrictive Covid policy (Baroni & Lazzari, 2022; Bolon et al., 2020; Bulaeva et al., 2017; Gupta, 2021). Alternatively, it might be to increase trade cooperation scale through financial power with ASEAN countries in order to attract more international students and maintain global competition (Ashour, 2020; Lomer & Anthony-Okeke, 2019; Paez et al., 2009).

4. Conclusion

Open-source intelligence plays an important role in effect measurement and public opinion measurement, identification, and management (Jahn et al., 2016; Young & Randall, 2014). This paper uses web crawler technology, text analysis, machine learning algorithms, and textual visualisation maps to archive and examine open-source information through artificial intelligence-based NLP algorithms and influence factors identification and appraisal models (Baig et al., 2019; Bolon et al., 2020; Msweli, 2012). This paper improves the use of open-source information and rigorously manages the online blended learning effect of international students. This study also objectively investigates potential risk factors and thoroughly assesses the online blended learning experience for overseas students in China. By discussing the technical components, the impact, and the outcome, the findings from this study indicate how global higher education rivalry may be viewed (Jonas & Burns, 2010; Mary et al., 2014; Stephens & Hennefer, 2013). Finally, this study makes recommendations for improving the online blended learning opportunities for Chinese overseas students, making strategic moves, and, most importantly, anticipating the near future.

Funding Information

The research for this article was supported by grants from Donghua University’s International Communication Programme [N19] and the Applied Linguistic Research Committee [Y2022-3].

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

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