Application of data science in career path and employability analysis from secondary and higher education students: a systematic review

doi:10.21203/rs.3.rs-3650005/v1

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Application of data science in career path and employability analysis from secondary and higher education students: a systematic review

https://doi.org/10.21203/rs.3.rs-3650005/v1

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Background

A sophisticated approach is required for career planning in the dynamic nature of the modern job market. Data science offers the capability to analyze complex factors influencing career paths and employability, providing students with tailored recommendations for success. This study aims to describe contemporary literature on the applicability of data science to students’ career paths and employability data.

Method

Multiple databases were systematically reviewed for student career and employability analysis studies up to May 2023 in both secondary and higher education. The review aimed to answer three research questions: 1) Which data science methodologies and techniques were used in predicting students’ career paths and employability? 2) What variables were identified as predictors of students' career decisions? 3) What performance metrics were used to evaluate the models?

Results

56 studies were identified involving 583,000 students. Studies mainly focused on employability outcomes (N = 30), with a significant portion in India (39.3%). Since 2016, research in this area has steadily increased. Random forest was the predominant model (N = 10), followed by other ensemble methods and Support Vector Machine (17.8%, 12.5%, and 10.7% respectively). Academic performance (N = 30) and student demographics (N = 28) were key predictors of career paths and employability. Accuracy was the primary evaluation metric (N = 48), followed by Recall (N = 26).

Conclusion

The study's results demonstrate the Random Forest algorithm outperformed other methods. The most influential factors was student's academic performance. The accuracy of predictions was the primary measure used to evaluate the models.

Career path

Data science

Employability

Higher education students

Secondary education students

Systematic review

Nowadays, students are often confronted with a plethora of career options and a fast changing job market. As the complexities of today's professional world increase, the critical decision of choosing a right career pathway has never been more difficult. Modern students are inundated with vast amounts of information about potential careers, often leading to choice overload and decision paralysis (Gati et al., 2019). Students are also urged to adapt and upskill in order to remain employable in an evolving job landscape, further complicating their options and emphasizing the critical need for effective career guidance and decision-making tools (Jagannathan et al., 2019). This stage of life can be overwhelming for students, leading to anxiety, confusion, and, in some cases, mismatched career decisions with far-reaching consequences.

While conventional career counselling is invaluable, it may lack the agility and precision required to deal with the complexities of today's job market (Maree, 2015). Students deal with an abundance of data, career insights, and options, demanding data-driven solutions that provide individualized counselling, forecast future employment trends, and equip them with job-market-required skills. In this context, the application of data science emerged as a promising solution, offering innovative approaches to tackle the issue of career decision-making and employability analysis.

Data science provides a distinct advantage in career recommendations (Wang & Orr, 2022). Through the analysis of vast datasets that include job market trends, individual preferences, and skills assessments, data-driven approaches can provide highly personalized career recommendations and anticipate employability for students to upskill. These methodologies can adapt and improve recommendations over time, ensuring their relevance in a rapidly changing job landscape. Additionally, data science can uncover hidden patterns and correlations within data, offering insights that traditional career counselling cannot provide.

This study presents a systematic review that includes students from secondary to higher education levels, bridging a gap left by existing reviews that predominantly focus on university graduates only. This study embarks on a comprehensive journey through the intersection of data science, student career development, and employability. By synthesizing existing research and identifying emerging trends, this paper aims to offer a panoramic view of the transformative potential that data science holds in career analysis. As the boundaries between academia and industry blur, and technology continues to disrupt traditional practice, understanding how data science can empower students to forge successful careers becomes not only insightful but imperative.

The main objective of this systematic literature review was to investigate how data science is applied in the analysis of students' career paths and employability. To guide this investigation, specific RQ were formulated. These include: 1) what data science methodologies and techniques that have been utilized in predicting students’ career paths and employability? 2) what are the variables that have been identified as predictors of students' career decisions and employability? 3) what are the performance metrics used to evaluate the predictive models?

This systematic review was presented consistent with the Preferred Reporting Items for Systematic Reviews and Meta Analyses guidelines (PRISMA) (Moher et al., 2009).

2.1 Eligibility criteria

Certain standards were established based on the Population, Exposure, Comparison, and Outcome (PECO) framework to determine which studies were included and excluded. This systematic literature review centered on studies that discuss student’s career path and employability. Career paths include diverse categories such as track, profession, or specialization. Employability is measured through various indicators including placement status, employment status, employment rate, probability of employability, and career opportunity status.

The population of the studies covered both secondary and higher education students. Secondary education is a level of education after primary education and before higher education, which typically includes middle and high schools. Higher education is an advance education level after secondary which takes place at colleges or universities. However, the specific structure and age range can vary from country to country.

The inclusion criteria were research articles and letters that report students’ career paths or employability analysis using data science or machine learning methods. The following types of articles were excluded: non-research letter, editorial, invited commentary, review, and abstract-only article. Additionally, studies not written in English were excluded.

2.2 Search strategy

A systematic search of the literature was conducted across various databases, including the Association for Computing Machinery (ACM), Education Resources Information Center (ERIC), IEEE Xplore, Science Direct, Scopus, and Springer, spanning from the beginning up to May 18, 2023. The search incorporated specific terms such as ("artificial intelligence" or "data science" or "machine learning" or "data analysis") AND ("student") AND ("career" or "employability") AND (prediction OR recommendation). To ensure accuracy, duplicate findings were eliminated using a reference management tool, followed by a manual screening process after the initial search.

2.3 Data Analysis

Information pertaining to the research inquiries, such as data science algorithms or methods, students’ features in the prediction, and evaluation metrics, were retrieved to conduct a mapping study to classify the outcomes for each RQ. Additionally, where available, supplementary data, such as outcome variables, education level, sample size, location, year, and data collection methods, were collected to offer a more comprehensive evaluation of the applications of data science methodologies in predicting students' career paths and employability. The gathered data from the selected studies was organized based on the following criteria:

The algorithms or methodologies utilized in the analysis of student and job data (addressing RQ1)
The students’ features taken into account in the prediction models or analyses (addressing RQ2)
The evaluation metrics used to assess prediction models or analyses (addressing RQ3)
The outcome variables
The educational level or specific attributes of the participants
The sample size in the study
The geographical location of the study
The year of the study
Data collection methods

3.1 Study Selection

Figure 1 depicts the flow used to gather included studies for this systematic review. The original search yielded 363 results. 62 articles were automatically removed due to duplication, leaving 301 items eligible to be reviewed. Out of 301 articles selected based on title and abstract screening, 101 papers were evaluated for full-text review. A total of 56 publications were removed from further analysis among 101 research because they did not present the outcome of interest. As a result, 45 papers were selected for further qualitative and quantitative evaluations. There were also 11 additional research classified as grey literature included in the review. Grey literature refers to related studies located in databases other than those initially searched during the literature search process and were found without using specific search terms.

3.2 General information of the studies

The characteristics of all included studies are available in Supplementary Table 1. There were more than 583,000 students from 56 studies included in this present systematic review. All students were on both secondary (N = 10) and higher (N = 44) education level. Students data were collected mostly through survey (N = 20) and school database (N = 19). Other data collection methods included observation (N = 2), open-source resources (N = 6), standardized tests (N = 4), official agencies (N = 4), and web scraping (N = 3).

In this systematic review, we incorporated studies that aimed to forecast both students' career paths and their employability. It was observed that the majority of these studies primarily concentrated on employability outcomes (N = 30). This observation emphasized the substantial significance of employability in the context of students' career considerations above career paths (N = 25). Notably, a limited number of studies also addressed predictions regarding career path appropriateness (N = 3).

The study's scope encompassed a diverse range of locations shown as a heatmap in Fig. 2. As shown in Table 1, the majority of these studies were conducted in India (comprising 39.3% of the total), indicating a significant focus on predicting students' career paths and employability in this region. China followed with 17.9% of the studies dedicated to this subject.

Table 1

Geographical location of the study
Country	Number of Studies	Percentage
India	22	39,29%
China	10	17,86%
Philippines	3	5,36%
Multi-center	3	5,36%
Malaysia	2	3,57%
Saudi Arabia	2	3,57%
United States	2	3,57%
United Kingdom	2	3,57%
Turkey	1	1,79%
Bangladesh	1	1,79%
Egypt	1	1,79%
Indonesia	1	1,79%
Colombia	1	1,79%
Nigeria	1	1,79%
Taiwan	1	1,79%
Thailand	1	1,79%
Dubai	1	1,79%
Chile	1	1,79%
Total	56	100%

In terms of the publication year, Fig. 2 illustrates how many studies were selected for the literature review in each respective year of publication. It demonstrated an upward trend in studies focusing on the application of data science in predicting students' careers and employability over the years. Since 2016, the number of research projects in this area has increased on an annual basis. It's important to note that this review was conducted in mid-2023, which may explain the drop in studies for that year (marked with an asterisk in Fig. 3).

3.3 Data science algorithms and methodologies

This section addresses the first RQ, which explores the data science algorithms and techniques used in predicting students' career paths and employability. In cases where studies discussed multiple algorithms or methodologies, only the best-performing ones were selected. We categorized the data science approaches from each study into four main groups: supervised learning, unsupervised learning, semi-supervised learning, as well as exploratory data analysis (EDA) and data visualization. The number of studies from each data science technique is broken down in Table 2 as follows:

Table 2

Data science algorithms and methods used and the number of studies
Algorithm/Method		Number of Studies
Supervised Learning	BP Neural Network	2
	Logistic Regression	4
	Support Vector Machine	6
	Random Forest	10
	XGBoost	5
	Decision Tree	5
	K-Nearest Neighbors	4
	Stochastic Gradient Descent	1
	Adaptive Boosting (AdaBoost)	1
	Gradient Boosted Tree	1
	Naive Bayes	3
	Deep Neural Network	1
	Deep Belief Neural Network (COA-DBNN)	1
	Artificial Neural Network	4
	Light Gradient Boosted Machine (LGBM)	1
	Multilayer Perceptron Neural Network	2
Unsupervised Learning	K-Means Clustering	1
	Partitional Clustering	1
	Louvain	1
Semi-Supervised / Hybrid	Other Ensemble	7
	Fuzzy Clustering	1
	Bayes (WAODE)	1
	DT-RT (Random Tree)	1
EDA/data visualization		1

It was evident from our analysis that among supervised models, random forest stood out as the predominant method with a total of 10 studies. Furthermore, other ensemble methods also demonstrated substantial usage, appearing in 7 studies.

3.4 Student’s features

In this section, the second RQ focusing on the attributes of students that have been recognized as indicators of their career paths and employability was examined. As shown in Fig. 4, the most frequently considered factor in predicting students' career paths and employability was their academic performance, such as CGPA and marks/scores. Students’ demography was the second most common feature included, which covered variables such as age, gender, and socio-economic background.

Additionally, features that played a major role in the analysis were collected, which were mostly calculated using the feature importance method. However, only a few studies (N = 14) specifically mentioned their most important or significant features. These commonly important attributes were academic performance, achievements (such as scholarships, awards, and certifications), work experience, and performance or behavior on online learning platforms.

3.5 Evaluation metrics

This section tackles the third RQ that addressed evaluation metrics that were used in the prediction process. Figure 5 shows that the majority of the reviewed studies used accuracy as their primary evaluation metric. Precision, recall, and F-1 measure were commonly, but not always, used together in most studies. Area under the receiver operating characteristic (ROC) curve (AUC) and root mean square error (RMSE) were also popular metrics. However, the array of evaluation metrics was not limited to these mentioned methods. In the reviewed studies, a diverse set of metrics was observed, including user feedback, mean absolute error (MAE), time, error rate, Matthews Correlation Coefficient, specificity, Kappa, hit ratio, positive predicted values (PPV), negative predicted values (NPV), true positive rate, false positive rate, and log-loss.

The comprehensive analysis of the reviewed studies shows a focus on higher education students, constituting a substantial majority at 78.6%, in contrast to the secondary education students, which accounted for 17.9%. This discrepancy highlights the emphasis on career analysis within the realm of higher education. However, it is imperative to recognize the potential for extending career planning services to students at an earlier stage, particularly those in secondary-level education. Enabling career planning at this earlier year holds significant promise in providing students with more time and resources for informed decision-making (Magnuson & Starr, 2000). Moreover, career analysis could also be particularly beneficial for special needs students (Sobnath et al., 2020a). This suggests a promising opportunity for inclusive career guidance strategies that cater to a diverse range of students, ultimately fostering a more equitable and effective educational experience.

The observed increasing trend in the number of studies on career prediction employing data analysis, as revealed in this systematic review (Fig. 3), signifies a burgeoning interest and recognition of the potential of data-driven approaches in shaping the future of career prediction. The continuous increase in research of this topic since 2016 highlights a sustained dedication towards leveraging advanced computational methods in understanding and enhancing career outcomes for students. This trend not only reflects a maturing field of study but also suggests that the integration of data science techniques in career analysis is becoming more ingrained within academic and professional communities.

Furthermore, the prevalence of studies centered in Asia (Fig. 2) sheds light on geographical trend. The heightened interest in this topic within Asian contexts may stem from a combination of factors, including rapid technological advancement, a strong emphasis on education, and a burgeoning job market (Kurniawati, 2021; Woetzel & Seong, 2021; Zhao, 2017). In contrast, the relative scarcity of similar studies in Western regions may be attributed to differing educational and career development paradigms. This regional disparity highlights the need for a more comprehensive and global perspective in understanding the application of data science in career analysis. It presents an opportunity for cross-cultural insights and collaboration to further refine and tailor data-driven approaches in career planning on a global scale.

The Random Forest method was the most commonly used data science algorithm in the studies examined (RQ1). Its popularity can be attributed to several reasons (Fawagreh et al., 2014; Y. Liu et al., 2012). Random Forest Classifier outperforms single classifiers in terms of classification rate and accuracy. Random Forest Classifier has shorter training time, making it faster than bagging or boosting. It is highly effective in cases of large data sets and also avoids the overfitting by dealing with noise in data sets. Internal estimates of inaccuracy, strength, correlation, and variable relevance are also provided. It is straightforward and easily parallelized. These collective advantages position Random Forest as a preferred algorithm, resulting in widespread adoption in studies aimed at predicting students' career pathways and employability. Random Forest belongs to ensemble learning, which is also the most commonly used method (Table 2). This indicates that in machine learning, ensemble approaches produce more accurate results than a single model (Dietterich, 2000).

In student career analyses, academic performance emerged as the most commonly included feature, followed by student’s demography (RQ2). It is important for educational institutions, to document these variables properly. An accurate record of academic achievements and a comprehensive understanding of each student's demography not only aids in tailoring educational experiences but also serves as a cornerstone for effective career guidance. This suggestion is also strengthened by the fact that, besides surveys, most of the data collected in the studies are from schools’ or universities’ databases. Additionally, it is crucial for future studies to incorporate feature importance analysis. This allows identification of key predictors influencing career predictions. Academic performance, achievements, work experience, as well as performance and behavior on online learning platforms emerged as the most significant predictors in this systematic review. As such, it is also necessary for educational institutions to prioritize the systematic documentation of these variables. Teachers, armed with this information, can offer more targeted guidance to students, nurturing their strengths and addressing areas of development. For students, recognizing the weight of these variables in career predictions provides valuable insight, encouraging them to actively engage in their academic pursuits and seek out additional opportunities for growth and recognition. This symbiotic relationship between schools, teachers, and students, fortified by comprehensive data collection and analysis, lays a solid foundation for informed career planning and attainment.

The most frequently used evaluation metrics in the prediction models is accuracy (RQ3). Accuracy represents the proportion of correctly predicted instances out of the total number of instances. While it provides a straightforward and easily interpretable assessment of model performance, it may not always be the most suitable metric, particularly with imbalanced or multiclass datasets (Ranawana & Palade, 2006; Wilson, 2001). Consequently, alternative evaluation metrics are also often employed to complement the prediction performance assessment, such as Recall as the second most frequently used evaluation metric, Precision, F1-Measure, time, AUC, RMSE, MAE, time, Matthews Correlation Coefficient, specificity, Kappa, hit ratio, negative predicted values (NPV), and log-loss.

Several limitations were observed across the studies reviewed in this systematic analysis. One of them was the issue of insufficient sample size (ElSharkawy et al., 2022; Guleria & Sood, 2023; José-García et al., 2022; Y. Wang et al., 2022; Yeung & Yeung, 2019). This limitation can potentially hinder the robustness and generalizability of the findings, pointing the importance of adequate sample representation for accurate predictions. Moreover, a prevalent challenge identified was the restricted number of features considered as predictors (Guleria & Sood, 2023; Maaliw et al., 2022; Mandalapu & Gong, 2019; Saidani et al., 2022; Yang & Chang, 2023). While many studies exhibited commendable efforts in utilizing available data, the omission of potentially influential variables may lead to an incomplete understanding of the complex interplay between various factors influencing career trajectories. Thus, it is imperative for future research endeavors to comprehensively evaluate and incorporate a broader array of features to enhance the accuracy and effectiveness of career prediction models.

4.1 Limitations

This study, like any other literature review, is limited by the search terms used. To overcome this limitation, we used synonymous and interchangeable phrases as well as performed an additional search within various journals. However, it is probable that some relevant works did not match our search criteria.

This systematic review examines the contemporary literature on data science on students' career paths and employability, and highlights its applicability. The majority of the studies still focus on higher education students. Asian countries dominated the studies, with a noticeable upward trend in the number of reports since 2016. The Random Forest method, closely followed by Support Vector Machine, emerged as the most frequently employed data science algorithm in the studies. The results demonstrated the academic performance as the most commonly included feature in the prediction process, followed by student’s demography. Furthermore, accuracy stood out as the predominant evaluation metric, with recall and precision also being commonly used. By harnessing the power of data-driven approaches, more personalized and effective career guidance, benefiting both students and educational institutions can be implemented. It is imperative for future studies to further explore the potential of data science in this context, considering a broader range of features and encompassing a diverse student demographic.

Funding

This research was conducted without any specific financial support or funding from external sources. All expenses related to this study were covered by the authors personally.

Competing Interests

The authors declare that they have no conflicts of interest relevant to this study. There are no financial or personal relationships that could inappropriately influence or bias the content of this manuscript.

Author Contributions

Savira Dwia Nadela contributed to the conception and design of the study, data collection and analysis, and drafting of the manuscript. Kaiqian Yin conducted data analysis, provided critical revisions, and approved the final version for submission. All authors read and approved the final manuscript.

Acknowledgement

We would like to express our sincere gratitude to Professor Benjamin Domingue and Professor Jo Boaler at Stanford University for their expert guidance and insightful feedback throughout the research process, which greatly enriched the quality of this work. We would also like to thank Fachreza Aryo Damara, MD at Cleveland Clinic for his technical expertise and assistance with data analysis, without which this study would not have been possible.

While this research did not receive external funding, the contributions from the aforementioned individuals and institutions played a pivotal role in the successful completion of this endeavor.

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No competing interests reported.

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Application of data science in career path and employability analysis from secondary and higher education students: a systematic review

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Abstract

Background

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1. Introduction

2. Methods

2.1 Eligibility criteria

2.2 Search strategy

2.3 Data Analysis

3. Results

3.1 Study Selection

3.2 General information of the studies

3.3 Data science algorithms and methodologies

3.4 Student’s features

3.5 Evaluation metrics

4. Discussion

4.1 Limitations

5. Conclusion

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