Difference between revisions of "ANLY482 AY2016-17 T2 Group15 Project Overview"
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− | + | In today’s educational world, more and more educational institutions are incorporating technology into teaching and learning to enrich students’ learning experiences and improve teachers’ pedagogical practices. The dilemma that many secondary schools face is how to aptly establish the right criteria to recommend the right subject combinations to students, so as to improve their learning outcomes. For instance, it is difficult for teachers to decide whether or not to recommend students to take on the subject combinations of Double Science or Triple Science. Should schools determine the capability of students based on their overall examination grades, or should they base their decisions on their individual subject grades (such as Mathematics or Science)? Often, many parents believe that their child is capable of coping with Double or Triple Science combinations, even if their Secondary 2 results show otherwise. Without proper analytical evidence, it is difficult for teachers to recommend students the right subject combinations that would ultimately improve their 'O' Level performance. | |
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− | + | <div style="border-left: #FFFFFF solid 5px; padding: 15px;"><font face="Helvetica" color="white" size="4">Project Sponsor</font> | |
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− | + | Edufy Secondary School is a heartland neighbourhood secondary school located in the North region of Singapore. The school is committed to providing an ideal learning environment and experiences for its students. Despite having a comprehensive set of past students’ data, the school lacks the expertise to analyse the data in a way that can aid in their decision making. | |
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− | + | The role of data analytics is becoming even more relevant and important, given the rise of Learning Analytics. Learning analytics seek to improve teaching and learning through the targeted analysis of students’ academic performance data [1][2]. By analysing the past data of students’ examination results using various data analysis and visualization techniques, it enables the school to discover useful patterns and relationships within the data. These insights equip the school with the intelligence that would enable them to better understand students’ performance and make informed decisions in their curriculum to refine their pedagogical strategies and optimize student performance [3]. | |
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<div style="border-left: #FFFFFF solid 5px; padding: 15px;"><font face="Helvetica" color="white" size="4">Objectives</font> | <div style="border-left: #FFFFFF solid 5px; padding: 15px;"><font face="Helvetica" color="white" size="4">Objectives</font> | ||
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We will attempt to analyze the trends of students' academic performance by examining their past subject grades and subject combinations. | We will attempt to analyze the trends of students' academic performance by examining their past subject grades and subject combinations. | ||
− | + | To achieve the above mentioned, we have performed an in-depth analysis on the historical data | |
− | To achieve the above mentioned, we | ||
with the following aims: | with the following aims: | ||
# To help secondary schools and teachers better formulate the right streaming practices and criteria that would benefit all students | # To help secondary schools and teachers better formulate the right streaming practices and criteria that would benefit all students | ||
# To develop an application using R for the school so that they can input future data to improve the accuracy of the model in predicting students’ GCE ‘O’ Level examinations results | # To develop an application using R for the school so that they can input future data to improve the accuracy of the model in predicting students’ GCE ‘O’ Level examinations results | ||
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Revision as of 00:16, 24 April 2017
In today’s educational world, more and more educational institutions are incorporating technology into teaching and learning to enrich students’ learning experiences and improve teachers’ pedagogical practices. The dilemma that many secondary schools face is how to aptly establish the right criteria to recommend the right subject combinations to students, so as to improve their learning outcomes. For instance, it is difficult for teachers to decide whether or not to recommend students to take on the subject combinations of Double Science or Triple Science. Should schools determine the capability of students based on their overall examination grades, or should they base their decisions on their individual subject grades (such as Mathematics or Science)? Often, many parents believe that their child is capable of coping with Double or Triple Science combinations, even if their Secondary 2 results show otherwise. Without proper analytical evidence, it is difficult for teachers to recommend students the right subject combinations that would ultimately improve their 'O' Level performance.
Edufy Secondary School is a heartland neighbourhood secondary school located in the North region of Singapore. The school is committed to providing an ideal learning environment and experiences for its students. Despite having a comprehensive set of past students’ data, the school lacks the expertise to analyse the data in a way that can aid in their decision making.
The role of data analytics is becoming even more relevant and important, given the rise of Learning Analytics. Learning analytics seek to improve teaching and learning through the targeted analysis of students’ academic performance data [1][2]. By analysing the past data of students’ examination results using various data analysis and visualization techniques, it enables the school to discover useful patterns and relationships within the data. These insights equip the school with the intelligence that would enable them to better understand students’ performance and make informed decisions in their curriculum to refine their pedagogical strategies and optimize student performance [3].
Utilizing past data of students’ grades from the school’s database, we aim to discover useful and practical insights which will allow teachers to better decide and advise students on choosing their Secondary 2 subject combination, particularly on whether they should take one of the following subject combinations:
- Combined Science
- 1 Pure Science and 1 Combined Science
- Double Pure Sciences or
- Triple Pure Sciences
We will attempt to analyze the trends of students' academic performance by examining their past subject grades and subject combinations.
To achieve the above mentioned, we have performed an in-depth analysis on the historical data with the following aims:
- To help secondary schools and teachers better formulate the right streaming practices and criteria that would benefit all students
- To develop an application using R for the school so that they can input future data to improve the accuracy of the model in predicting students’ GCE ‘O’ Level examinations results
To measure and analyze the academic performance of students requires a multidimensional approach. Various past research have supported the hypothesis that the academic performance of students is dependent on the student’s socio-economic, psychological and environmental factors (Hijazi and Naqvi, 2006). For example, a student’s academic performance may be a result of tuition and family’s status and environment. For the purposes of our analysis, we will focus our analysis solely on the environment factors which are directly attributed to our Project Sponsor, basing it to the context of Singapore as shown in Figure 1 below.
According to a research done by the Bangladesh e-Journal of Sociology, it has pointed out three main variables in determining the academic performance of students, namely Environmental, Psychological and Socio-economic factors. The environment that a student is immersed in school affects their academic performance, which includes their subject combination, class and co-curricular activities (CCA). As such, these factors form the basis of our analysis, which we have performed an exploratory data analysis on the historical results of the students.
The psychological factors include their intellectual and emotional ability to cope with the workload. Finally, the socio-economic factors include their gender, race, family background, and parents’ marital status.
Staffolani and Bratti (2002) asserted that the most important measure of the future academic performance and achievement of students is their previous educational outcomes, as cited from Ali, Shoukat, et al. (2013). In other words, the higher the student’s past academic performance, the better their future academic performance. As such, we will be analyzing students’ historical results which forms the basis of our analysis.
- Ali, Shoukat, et al. (2013) "Factors Contributing to the Students Academic Performance: A Case Study of Islamia University Sub-Campus." American Journal of Educational Research 1.8 (2013): 283-289.
- S. T. Hijazi, and R. S. M. M. Naqvi (2006), “Factors affecting student’s performance: A Case of Private Colleges”, Bangladesh e-Journal of Sociology, Vol. 3, No. 1, 2006.
- Tanggaard, Lene, Nielsen, Klaus, & Jørgensen, Christian Helms. (2015). Students' Experiences of Ability-Based Streaming in Vocational Education.Education & Training, 57(7), 723-737.
- Factors Affecting High School Students’ Academic Motivation in Taiwan (https://selfdeterminationtheory.org/SDT/documents/2006_Hardre_et_al_APJE.pdf)
- Ali, Shoukat, et al. (2013) "Factors Contributing to the Students Academic Performance: A Case Study of Islamia University Sub-Campus." American Journal of Educational Research 1.8 (2013): 283-289. (http://pubs.sciepub.com/education/1/8/3/#)
Besides the team and our supervisor, the other stakeholders are:
Sponsor
- Mr Lee Peck Ping, Principal of Edufy Secondary School (ESS)
- Mdm Lim, Vice Principal of ESS
Other Stakeholders
- Students of ESS
- Teachers and Heads of Department (HODs) of ESS
- Parents of students studying in ESS