An integrative review of computational methods for vocational curriculum, apprenticeship, labor market, and enrollment problems

Computational methods have been used extensively to solve problems in the education sector. This paper aims to explore the computational method's recent implementation in solving global Vocational education and training (VET) problems. The study used a systematic literature review to answer specific research questions by identifying, assessing, and interpreting all available research shreds of evidence. The result shows that researchers use the computational method to predict various cases in VET. The most popular methods are ANN and Naïve Bayes. It has significant potential to develop because VET has a very complex problem of (a) curriculum, (b) apprenticeship, (c) matching labor market, and (d) attracting enrollment. In the future, academics may have broad overviews of the use of the computational method in VET. A computer scientist may use this study to find more efficient and intelligent solutions for VET issues

A SURVEY ON EDUCATIONAL DATA MINING AND RESEARCH TRENDS

ABSTRAC

Identifying College Students’ Course-Taking Patterns In Stem Fields

In spite of substantial investments in science, technology, engineering, and mathematics (STEM) education, low enrollment and high attrition rate among students in these fields remain an unmitigated challenge for higher education institutions. In particular, underrepresentation of women and minority students with STEM-related college degrees replicates itself in the makeup of the workforce, adding another layer to the challenge. While most studies examine the relationship between student characteristics and their outcomes, in this study, I take a new approach to understand academic pathways as a dynamic process of student curricular experiences that influence his/her decision about subsequent course-takings and major field of the study. I leverage data mining techniques to examine the processes leading to degree completion in STEM fields. Specifically, I apply Sequential Pattern Mining and Sequential Clustering to student transcript data from a four-year university to identify frequent academic major trajectories and also the most frequent course-taking patterns in STEM fields. I also investigate whether there are any significant differences between male and female students’ academic major and course-taking patterns in these fields. The findings suggest that non-STEM majoring paths are the most frequent academic pattern among students, followed by life science trajectories. Engineering and other hard science trajectories are much less frequent. The frequency of all STEM trajectories, however, declines over time as students switch to non-STEM majors. The switching rate from non-STEM to STEM fields overtime is, however, much lower. I also find that male and female students follow different academic pathways, and these gender-based differences are even more significant within STEM fields. Students’ course-taking patterns also suggest that taking engineering and computer science courses is predominantly a male course-taking behavior, while females are more likely to pursue academic pathways in life science. I also find that STEM introductory courses - particularly Calculus I, Calculus II and Chemistry I – are gateway courses, that serve as potential barriers to pursuing degrees in STEM-related fields for a large number of students who showed an initial interest in STEM courses. Female students were more likely to switch to non-STEM fields after taking these courses, while male students were more likely to drop out of college overall. In addition to the study’s findings on students’ academic pathways toward attaining a college degree in a STEM-related field, this study also shows how data mining techniques that leverage data about the sequence of courses students take can be used by higher education leaders and researchers to better understand students’ academic progress and explore how students navigate and interact with college curriculum. In particular, this study demonstrates how these analytic approaches might be used to design and structure more effective course taking pathways and develop interventions to improve student retention in STEM fields

THE ROLE OF ICT IN EDUCATION: AN EFFICIENCY ANALYSIS

Nell’ambito dell’educazione, l’utilizzo delle tecnologie dell’informazione e della comunicazione (TCI) si è notevolmente intensificato negli ultimi decenni grazie agli investimenti effettuati. Il concetto di TCI è molto ampio. In questo lavoro di tesi, TCI non si riferisce solo alle infrastrutture fisiche (ad esempio radio, telefono, video, televisione, computer), ma include anche l’uso e l’intensità di utilizzo (ad esempio l’impiego giornaliero, settimanale, ecc.), la qualità e l’ubicazione dell’infrastruttura (ad esempio, a scuola oppure a casa), il motivo del suo utilizzo (ad esempio, per svago o per motivi di studio) e la spesa relativa alle TIC. Questa dissertazione discute il ruolo delle TIC nell’istruzione concentrandosi sull’analisi dell’efficienza. La tesi comprende quattro lavori ripartiti in diversi capitoli. Il Capitolo II propone una sistematica literature review sull’argomento. Il Capitolo III esegue un’analisi transnazionale dell’efficienza dell’istruzione a livello scolastico in sei Paesi del sud-est asiatico, ossia in Brunei Darussalam, in Malesia, in Indonesia, nelle Filippine, a Singapore ed in Tailandia. L’analisi viene effettuata mediate l’approccio della stochastic frontier analysis (SFA) che consente di considerare l'eteroschedasticità. Da questo studio risulta che Singapore è comparativamente il Paese con la migliore performance. Nell’analisi condotta, le variabili TIC, ovvero (1) il rapporto tra computer a scuola e (2) il numero totale di studenti ed il rapporto tra computer connessi a Internet, sono assunte essere determinanti dell’inefficienza ed entrano come input nella funzione di produzione (istruzione). Dall’analisi condotta, emerge che il primo rapporto non influenza in modo significativo gli esiti scolastici mentre il secondo ha un significativo impatto. Come determinanti dell’inefficienza, il primo rapporto influisce sull’inefficienza della scuola in nelle aree di matematica e scienze, mentre il secondo non ha alcuna influenza. Il Capitolo IV utilizza l'approccio DEA (non-parametric data envelopment analysis) del modello di super-efficienza che consente alle scuole efficienti di avere punteggi di efficienza superiori a uno (nell’approccio DEA tradizionale, il punteggio di efficienza è limitato da zero a uno). Per studiare i fattori che potenzialmente influenzano l’efficienza, questo studio include anche una seconda analisi basata sull’approccio bootstrapped quantile regression. I risultati suggeriscono una serie di implicazioni politiche per le scuole del sud-est asiatico, indicando diverse linee d’azione per le scuole sia con livelli di efficienza più alti sia per quelle con efficienza minore. Il Capitolo V estende l'analisi condotta nel Capitolo III sia dal punto di vista metodologico che empirico. L’analisi, basata sull’approccio SFA, non include solo le infrastrutture TCI nel modello, ma aggiunge anche l’uso delle TCI (compreso l’indice del tempo trascorso dagli studenti nell’uso delle TCI a scuola, fuori dalla scuola per scopi di intrattenimento e a casa per compiti scolastici). Ciò viene fatto utilizzando il “modello di frontiera stocastica a quattro componenti” in cui le TCI sono modellate sia come input che come determinanti di inefficienza variabile nel tempo. Inoltre, questo modello viene testato utilizzando un set di dati di 24 Paesi OCSE. I risultati mostrano che tutte e tre le variabili che appartengono all’uso delle TIC influenzano i risultati sul livello di istruzione degli studenti, mentre come determinanti di inefficienza, queste variabili hanno solo un effetto marginale. Questo studio dovrebbe quindi fornire una visione più olistica del ruolo delle TIC nell’efficienza dei processi educativi.In education sector, the application of information and communication technology (ICT) has increased substantially over the last decades as many countries have been investing their resources in ICT for educational purposes. The ICT is a broad concept. In this dissertation, ICT does not only refer to physical infrastructure (e.g., radio, telephone, video, television, computer), but it also includes the use and the intensity of use (e.g., every day, one a week, twice a week), the quality and location of the infrastructure (e.g., at school, at home), the reason for using it (e.g., for entertainment or for study purposes), and the expenditure related to the ICT. This dissertation then discusses the role of ICT in education focusing on the efficiency analysis. It comprises four studies starting with a systematic literature review presented in Chapter II, which offers a clear overview of what has and has not been done in the literature towards this particular topic. Chapter III performs cross-country analysis of efficiency of education at school level in six countries in South-East Asia (i.e., Brunei Darussalam, Malaysia, Indonesia, the Philippines, Singapore, and Thailand). The stochastic frontier analysis (SFA) allowing for heteroscedasticity is used. The result reveals that Singapore has the (relatively) best performance among other countries. The ICT infrastructure variables, i.e., the ratio of computers at school to the total number of students and the ratio of computers connected to the internet, are modeled as inputs in the (education) production function and determinants of inefficiency. The first ratio is found to be not significant influencing education outcomes while the second one does influence. As determinants of inefficiency, the first ratio affects school’s inefficiency in terms of mathematics and science, while the second one has no influence. Relying the finding of Chapter III that there are many higher efficiency level schools, Chapter IV uses the non-parametric data envelopment analysis (DEA) approach of the super-efficiency model which has the ability to differentiate among the higher efficiency level schools. This model allows the efficient schools to have efficiency scores of more than one (in the traditional DEA approach, the efficiency score is bounded from zero to one). To investigate factors that potentially influence efficiency, this study performs the “second-stage” analysis by using bootstrapped quantile regression. The results suggest a number of policy implications for South-East Asian schools, indicating different courses of action for schools with higher and lower efficiency levels. Chapter V extends the analysis conducted in Chapter III both from methodological and empirical point of views. The analysis, based on the SFA approach, not only includes the ICT infrastructure in the model, but it also adds the ICT use (including the index of time spent by students in using ICT at school, outside school for entertainment purposes, and at home for school-related tasks). This is done by using the “four-component stochastic frontier model” where ICT is modeled both as inputs and determinants of time-varying inefficiency. In addition, this model is tested using a dataset of 24 OECD countries. Results show that all three variables belong to ICT use influence education outcomes, while as the determinants of time-varying inefficiency, these variables have only marginal effect on inefficiency. This study is then expected to provide a more holistic view of the role of ICT in the efficiency of education measurement as the previous studies only addressed the ICT infrastructure

Exploring Relations Between Motivation, Metacognition, and Academic Achievement Through Variable-Centered, Person-Centered and Learning Analytic Methodologies

The three studies that comprise this dissertation examine relations between student characteristics, motivations, metacognitive learning processes, and academic achievement. Methodologically, the dissertation demonstrated the potential of multiple types of approaches and data resource types. By employing multiple approaches including variable-centered, person-centered, and learning analytics, researchers can understand learning processes from various angles. In addition, through this triangulation by multiple types of methodological approaches, educational theories could be more thoroughly verified and supported by various empirical findings. Multiple types of data resources are related to analytical methods. The purpose of the first paper was to examine relations between achievement goals and metacognitive learning behaviors using a clustering analysis and visualization. A clustering analysis conducted with achievement goals produced three goal profiles; 1) mastery-approach, 2) performance-approach, and 3) performance-avoidance identified three goal profiles. The profiles include High Approach, High Mastery, and High Goal Endorsement groups. The finding demonstrated that students in the High Mastery group, who had greater use of the self-assessment tool, obtained higher final grades than other groups could be explained from the perspective of SRL. In addition, learners motivated by mastery approach goals engaged in the greater use of self-assessment quizzes. Students in the High Mastery group also used the tools earlier than other two groups for exam 2. As the most frequently used pattern, sequential pattern mining discovered the repeated use of self-assessment quizzes to monitor their learning. More students in the High Mastery group employ this pattern of metacognitive events than students in the High Performance and High-Goal endorsement groups, particularly during sessions in weeks before exams. A subsequent analysis revealed that for all exams, students who conducted a repeated behavior pattern indicative of metacognitive monitoring and control outperformed those who did not. From the research, it is confirmed that the person-centered analysis provided authentic and generalizable groups and afforded observation of the learning behaviors of learners with typical combinations of goals. In addition, sequential patterns provide instructor more interesting information on learning processes than the frequency of accesses. The purpose of the second research was to identify motivational profiles based on multiple types of motivations including self-efficacy, achievement goals, and expectancy-value from an integrative perspective. For this research, a LPA was conducted with ten types of motivational constructs and three kinds of metacognitive learning processes. The LPA identified four motivational profiles; 1) High Cost, 2) High Performance Goals, 3) High Goals and Values, and 4) Low Performance Goals, and three metacognitive profiles; 1) Infrequent metacognitive processing. 2) Checking performance and planning, and 3) Self-assessment. Student demographic information significantly influenced the membership of motivational profiles. Older students tend to have higher self-efficacy, mastery-approach, and values, but low cost than younger ones. In addition, compared to Caucasian and Asian students, underrepresented students tend to be more motivated by higher goals and values than high cost or high performance goals. Lastly, female students are more likely to be members of High performance goals and High goals and values than High cost oriented and Low performance goals and cost than males. In terms of the relations profiles with academic achievement, Low Performance Goals group showed the best performance. Among metacognitive profile groups, students in Checking performance and planning, and Self-assessment demonstrated similar academic performance. The investigation of relations between two profile groups demonstrated that students in the High cost group are more likely to be a member of self-assessment group than checking performance and planning as well as of a member of an infrequent metacognitive process than checking performance and planning. In addition, students in high performance and goals and high goals and values groups relative to the low performance goals group more likely to be a member of the infrequent metacognitive process than checking performance and planning. The findings of this research provide authentic motivation status and metacognition learning process as well as their relations. Addition, this research figured out specific motivational profiles through the multiple types of motivations from the integrative perspective. Therefore, instructors can provide more effective and specific interventions to students who have difficulty utilizing metacognitive learning processes, considering motivational status based on multiple motivations. In addition, instructors can understand motivational profiles by demographics so at the beginning of the semester in which the information on students is not enough to identify students learning processes, they intervene students based on demographic information. The purpose of the third paper was to consider the relative importance of capturing demographic, motivational and metacognitive processes as potential predictors of learning outcomes, and appraises them alongside both traditional prediction modeling approaches in higher education, and emergent methods, sequence pattern mining, arising from the field of educational data mining. The sequence pattern mining discovered the repeated use of self-assessment quizzes in Biology and repeated use of planning contents in Math. A regression model with combined resource types demonstrated the improved predictive power than models with individual resource types. Also, theory-aligned behaviors designed based on metacognitive learning processes better improved the accuracy of the model than non-theory-aligned behaviors automatically provided by the system. Lastly, when applying the same prediction model, the model better explained the variance of academic achievement in Biology in which metacognitive supporting tools designed based on an educational theory than that in Math that has few theory-aligned behavior variables. Therefore, this study emphasizes the importance of existing ambient data from university systems. Also, log data generated by systems such as LMS allows researchers to examine the same data in different ways with no need for additional data collection. Lastly, educational theory and contexts should be taken into consideration in designing courses and developing the prediction models. Therefore, instructors and researchers, in designing courses, the consideration of educational theories and contexts is the essential process. This dissertation provides insight regarding authentic relations between motivation, metacognition, and academic achievement. Specifically, instructors can understand how multiple types of motivations work together, and the motivational profiles influence metacognitive learning strategies. In courses, by examining motivational profiles, instructors can provide more effective intervention with which students change their resolve their weak learning easier. Practically, by investigating each type of predictor from data resources including demographic, motivation, and behavioral variables, findings from this dissertation can enable researchers to prioritize development of prediction models to identify students who are more likely to experience failure in courses. Additionally, instructors can figure out the importance of interpreting variables through educational theories and in context through the comparison of courses with differing instructional designs. Further, by appraising these results in light of theory, instructors can take action to improve student’s learning outcomes by adjusting the design of their courses

Central Washington University 1991/93 Undergraduate/Graduate Catalog

https://digitalcommons.cwu.edu/catalogs/1187/thumbnail.jp

Optimized Machine Learning Models Towards Intelligent Systems

The rapid growth of the Internet and related technologies has led to the collection of large amounts of data by individuals, organizations, and society in general [1]. However, this often leads to information overload which occurs when the amount of input (e.g. data) a human is trying to process exceeds their cognitive capacities [2]. Machine learning (ML) has been proposed as one potential methodology capable of extracting useful information from large sets of data [1]. This thesis focuses on two applications. The first is education, namely e-Learning environments. Within this field, this thesis proposes different optimized ML ensemble models to predict students’ performance at earlier stages of the course delivery. Experimental results showed that the proposed optimized ML ensemble models accurately identified the weak students who needed help. More specifically, these models achieved an accuracy of up to 96% in the binary case and 93.1% in the multi-class case. The second application is network security intrusion detection. Within this application field, this thesis proposes different optimized ML classification frameworks using a variety of optimization modeling algorithms and heuristics to improve the performance of the IDSs through anomaly detection while maintaining or reducing their time complexity. Experimental results showed that the developed models reduced the training sample size by up to 74%, reduced the feature set size by almost 60%, and improved the detection accuracy by up to 2%. This thesis can be divided into two main parts. The first part analyzes different educational datasets and proposes different optimized ML classification ensemble models that accurately predict weak students who may need help. The second part proposes optimized ML classification frameworks that accurately detect network attacks while maintaining a low false alarm rate and time complexity. It is noteworthy that the developed models and frameworks could be generalized as follows: Optimized ML ensemble models proposed in the first part of this thesis can be generalized to many applications such as finance, network security, social media, and healthcare systems. Optimized ML classification models proposed in the second part of this thesis can be generalized to other applications that typically generate large datasets in terms of instances and feature set

Undergraduate Bulletin, 2016-2017

https://red.mnstate.edu/bulletins/1100/thumbnail.jp

The Bulletin, Undergraduate Catalog 2013-2014 (2013)

https://red.mnstate.edu/bulletins/1096/thumbnail.jp