student performance dataset

The first row of the code below uses method the corr() to calculate correlations between different columns and the final_target feature. Table 4 Questions asked in the survey of competition participants. Taking part in the data competition contributed a lot to my engagement with the subject. Focus is on the difference in median between the groups. Several years ago they released a simplified service that is ideal for instructors to run competitions in a classroom setting. Question: In python without deep learning models . (2) Academic background features such as educational stage, grade Level and section. In Dremio, everything that you did finds its reflection in SQL code. Algorithm i used for this is logistic regression Accuracy of my Algorithm is 76.388%. In the case of University-level education [] and [] have designed machine learning models, based on different datasets, performing analysis similar to ours even though they use different features and assumptions.In [] a balanced dataset, including features mainly about the . (Note that these were not the same between the two classes, but similar in content and rigor.) Besides, data analysis and visualization can be done as standalone tasks if there is no need to dig deeper into the data. The purpose is to predict students' end-of-term performances using ML techniques. 1-10 of the data are the personal questions, 11-16. questions include family questions, and the remaining questions include education habits. Performance is plotted against type of question, separately for the competition they completed. The purpose of this study is to examine the relationships among affective characteristics-related variables at the student level, the aggregated school-level variables, and mathematics performance by using the Programme for International Student Assessment (PISA) 2012 dataset. It is reasonable that if the student has bad marks in the past, he/she may continue to study poorly in the future as well. Parent participation feature have two sub features: Parent Answering Survey and Parent School Satisfaction. In this post, we will explore the student performance dataset available on Kaggle. Figure 1 shows the data collected in CSDM. These competitions can be private, limited to members of a university course, and are easy to setup. Attributes for both student-mat.csv (Math course) and student-por.csv (Portuguese language course) datasets: 1 school - student's school (binary: 'GP' - Gabriel Pereira or 'MS' - Mousinho da Silveira) Understanding one topic better than another will result in higher success rate for questions asking about the better understood topic compared to the scores for other topics. Student performance will be categorized as Fail, Fair, Good, Excellent the definition will be made by you. Being able to make multiple submissions over a several week time frame enables them to try out approaches to improve their models. One can expect that, on average, a students success rate for each question will be about the same as their success rate in the total exam. Students who participated in the Kaggle challenge for classification scored higher than those that did the regression competition, on the classification problem. Each point corresponds to one student, and accuracy or error of the best predictions submitted is used. The dataset is useful for researchers who want to explore students' academic performance in online learning environments, and will help them to model their educational datamining models. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7. The dataset consists of 480 student records and 16 features. The distribution of the performance scores by group is shown as a boxplot. These are not suitable for use in a class challenge, because all the data is available, and solutions are also provided. These statistics are consistent with historic scores for the class, that the undergraduates tend to have a wider range than post-graduates but generally quite similar averages. In addition, students may invest a disproportionate amount of time and effort into competition. State of the current arts is explained with conclusive-related work. Each scatter plot shows the interrelation between two of the specified columns. It can be helpful if you want to look not only at the beginning or end of the table but also to display different rows from different parts of the dataframe: To inspect what columns your dataframe has, you may use columns attribute: If you need to write code for doing something with a column name, you can do this easily using Pythons native lists. The code below is used to import the port_final and mat_final tables into Python as pandas dataframes. Before this, we tune the size of the plot using Matplotlib. Here is the SQL code for implementing this idea: On the following image, you can see that the column famsize_int_bin appears in the dataframe after clicking on the button: Finally, we want to sort the values in the dataframe based on the final_target column. These are not suitable for use in a class challenge, because all the data is available, and solutions are also provided. Taking part in the data competition improved my confidence in my understanding of the covered material. Abstract: The data was collected from the Faculty of Engineering and Faculty of Educational Sciences students in 2019. You signed in with another tab or window. (2020) Student Performance Classification Using Artificial Intelligence Techniques. Scores for the relevant questions were summed, and converted into percentage of the possible score. Data cleaning was conducted using tidyr (Wickham and Henry Citation2018), dplyr (Wickham etal. Also, we drop famsize_bin_int column since it was not numeric originally. Lucio Daza 26 Followers Sr. Director of Technical Product Marketing. 4.2 Data preprocessing There are 270 of the parents answered survey and 210 are not, 292 of the parents are satisfied from the school and 188 are not. The lecturer allowed participants to create groups towards the end of the competition to illustrate the advantages of group work and ensemble models. Let's start by reading the dataset into a pandas dataframe. This setup mimics randomized control trials, which are the gold standard, in experiment design (Shelley, Yore, and Hand Citation2009a, chap. Types of data are accessible via the dtypes attribute of the dataframe: All columns in our dataset are either numerical (integers) or categorical (object). It brings the game feeling, increases the interest level among students, and motivates for higher performance (Shindler Citation2009, p. 105). If in some topic, say regression, the student has better knowledge, she will perform better on the regression questions. Very often, the so-called EDA (exploratory data analysis) is a required part of the machine learning pipeline. The materials to reproduce the work are available at https://github.com/dicook/paper-quoll. In CSDM, the group sizes were relatively small, approximately 30 students per group. The competition performance relative to number of submissions is shown in plots (d)(f). You are not required to obtain permission to reuse this article in part or whole. Readme Stars. Another reason for this approach was the university policy, requiring a strategy to assess students individually in group assignments. Kaggle is a data modeling competition service, where participants compete to build a model with lower predictive error than other participants. We should do type conversion for all numeric columns which are strings: age, Medu, Fedu, traveltime, studytime, failures, famrel, freetime, goout, Dalc, Walc, health, absences. [Web Link]. Increasing student awareness of the association between the knowledge obtained from the data competition, better understanding of the material, and better marks might increase all students engagement with the competition. Then select the option from the menu: Through the same drop-down menu, we can rename the G3 column to final_target column: Next, we have noticed that all our numeric values are of the string data type. The dataset we will work with is the Student Performance Data Set. Abstract: The data was collected from the Faculty of Engineering and Faculty of Educational Sciences students in 2019. The magnitude of the effect of different approaches, though, varies. Sr. Director of Technical Product Marketing. The variables correspond to the student's personal information (categorical) and the result obtained in the assessments (numerical). Among interesting insights you can derive from the graphs above is the fact that if the father or mother of the student is a teacher, it is more probable that the student will get a high final grade. 68 ( 6 ) ( 2018 ) 394 - 424 . This is an opportunity for educators to provide a vehicle for students to objectively test their learning of predictive modeling. In 2015, Kaggle InClass was introduced, as a self-service platform to conduct competitions. The dataset contains some personal information about students and their performance on certain tests. The most interesting information is in the top left and bottom right quarters, where student outperform on one type of questions but not on the other type. Download. Generally the results support that competition improved performance. Seaborn package has the distplot() method for this purpose. They should be properly rewarded and most important, feel that they have a reasonable chance to win or achieve high mark (Shindler Citation2009). For example, we would expect from a student with a 70% exam mark to get 70% marks on each of the questions in the exam, if she has similar knowledge level on all the exam topics. For all questions in the exam, difficulty and discrimination scores were computed, using the mean and standard deviations. Refresh the page, check Medium 's site status, or find something interesting to read. It allows understanding which features may be useful, which are redundant, and which new features can be created artificially. When ready, press the button. Probably every EDA starts from exploring the shape of the dataset and from taking a glance at the data. Further in this tutorial, we will work only with Portuguese dataframe, in order not to overload the text. It also provides all the scores from all past submissions (under Raw Data on Public Leaderboard). Citation2017) and plots were made with ggplot2 (Wickham Citation2016). The parameters which we have specified are color (green) and the number of bins (10). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. The response rate for CSDM was 55%, with 34 of 61 students completing the survey. Whats more, Freeman etal. 1 watching Forks. Figure 4 (top row) shows performance on the classification and regression questions, respectively, against their frequency of prediction submissions for the three student groups (CSDM classification and regression, ST-PG regression) competitions. Students in CSDM and ST-PG were invited to give feedback about the course, in particular about the data competitions, before the final exam. Participants will submit their solutions in the same format. Prediction of student's performance became an urgent desire in most of educational entities and institutes. Your home for data science. The reason for this strategy was first to motivate each of the students to think about modeling and be actively engaged in the competitions through individual submission. Fig. the data should be relatively clean, to the point where the instructor has tested that a model can be fitted. This point was emphasized in the instructions to the students at the beginning of the survey. Start the discussion. Kaggle will then split your test set into two, a public set that is used to provide ongoing scores to participants, and a private set, on which performance is revealed only after the competition closes. import matplotlib.pyplot as plt import seaborn as sns. With Pandas, this can be done without any sophisticated code. 1 Boxplots of performance on regression and classification questions in the final exam, by type of data competition completed in CSDM. We will use Python 3.6 and Pandas, Seaborn, and Matplotlib packages. To examine whether engagement improved performance, scores on the questions related to the competition normalized by total exam score (as computed in the performance section) are examined in relation to frequency of submissions during the competition. The competition ran for one month. In our case, this column is called final_target (it represents the final grade of a student). For the CSDM and ST-PG regression competitions, a clear pattern is that predictions improved substantially with more submissions. The regression competition seemed to engage students more than the classification challenge. The relationships with exam performance are weak. We can see that there are more girls (roughly 60%) in the dataset than boys (roughly 40%). We want to see students with the lowest grades at the top of the table, so we choose Sort Ascending option from the drop-down menu: In the end, we save the curated dataframe under the port_final name in the student_performance_space. The dataset contains 7 course modules (AAA GGG), 22 courses, e-learning behaviour data and learning performance data of 32,593 students. It is often useful to know basic statistics about the dataset. This will use Matplotlib to build a graph. When doing real preparation for machine learning model training, a scientist should encode categorical variables and work with them as with numeric columns. The tail() method returns rows from the end of the table. The purpose is to predict students' end-of-term performances using ML techniques. You can also specify the number of rows as a parameter of this method. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. You can download the data set you need for this project from here: StudentsPerformance Download Let's start with importing the libraries : We want to see how the range of final_target column varies depending on the job of mother and father of students. Table 3 shows the results of permutation testing of median difference between the groups. It works better for continuous features, not integers. A student who is more engaged in the competition may learn more about the material, and consequently perform better on the exam. We drop the last record because it is the final_target (we are not interested in the fact that the final_target has the perfect correlation with itself). This job is being addressed by educational data mining. Prior and post testing of students might improve the experimental design. Then we use PyODBC objects method connect() to establish a connection. The dataset consists of 305 males and 175 females. (3) Behavioral features such as raised hand on class, opening resources, answering survey by parents, and school satisfaction. Record the student names in Kaggle to match with your class records. about each numerical column of the dataframe. This is an open access article distributed under the terms of the Creative Commons CC BY license, which permits unrestricted use, distribution, reproduction in any medium, provided the original work is properly cited. Table 2 shows the summary statistics of the exam scores and in-semester quiz scores for the 34 postgraduate (ST-PG) students and for the 141 undergraduate (ST-UG) students. Perhaps the link between the two could be emphasized by instructors when the competition is presented to students. Researchers from the University of Southern Queensland and UNSW Sydney looked at the association between internet use other than for schoolwork and electronic gaming, and the NAPLAN performance . More evidence needs to be collected from other STEM courses to explore consistent positive influence. Along with the competition, students were expected to submit a report that explained their modeling strategy and what they had learned about the data beyond the modeling. Actually, before the machine learning era, all data science was about the interpretation and visualization of data with different tools and making conclusions about the nature of data. When creating SQL queries, we used the full paths to tables (name_of_the_space.name_of_the_dataframe). 1 Gender - student's gender (nominal: 'Male' or 'Female), 2 Nationality- student's nationality (nominal: Kuwait, Lebanon, Egypt, SaudiArabia, USA, Jordan, Venezuela, Iran, Tunis, Morocco, Syria, Palestine, Iraq, Lybia), 3 Place of birth- student's Place of birth (nominal: Kuwait, Lebanon, Egypt, SaudiArabia, USA, Jordan, Venezuela, Iran, Tunis, Morocco, Syria, Palestine, Iraq, Lybia), 4 Educational Stages- educational level student belongs (nominal: lowerlevel,MiddleSchool,HighSchool), 5 Grade Levels- grade student belongs (nominal: G-01, G-02, G-03, G-04, G-05, G-06, G-07, G-08, G-09, G-10, G-11, G-12 ), 6 Section ID- classroom student belongs (nominal:A,B,C), 7 Topic- course topic (nominal: English, Spanish, French, Arabic, IT, Math, Chemistry, Biology, Science, History, Quran, Geology), 8 Semester- school year semester (nominal: First, Second), 9 Parent responsible for student (nominal:mom,father), 10 Raised hand- how many times the student raises his/her hand on classroom (numeric:0-100), 11- Visited resources- how many times the student visits a course content(numeric:0-100), 12 Viewing announcements-how many times the student checks the new announcements(numeric:0-100), 13 Discussion groups- how many times the student participate on discussion groups (numeric:0-100), 14 Parent Answering Survey- parent answered the surveys which are provided from school or not (nominal:Yes,No), 15 Parent School Satisfaction- the Degree of parent satisfaction from school(nominal:Yes,No), 16 Student Absence Days-the number of absence days for each student (nominal: above-7, under-7). The frequency of submissions, and the accuracy (or error) of their predictions, made by individual students, is recorded as a part of the Kaggle system. After collecting the survey from the students we realized that the questions about student engagement were positively worded, which has the potential to bias the response. Click on the arrow near the name of each column to evoke the context menu. Of the questions preidentified as being relevant to the data challenges, only the parts that corresponded to high level of difficulty and high discrimination were included in the comparison of performance. The evidence suggests it does. Two main factors affect the identification of students at risk using ML: the dataset and delivery mode and the type of ML algorithm used. This dataset includes also a new category of features; this feature is parent parturition in the educational process. For example, show the existing buckets in S3: In the code above, we import the library boto3, and then create the client object. Packages 0. administrative or police), 'at_home' or 'other') 10 Fjob - father's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. But these dataframes are absolutely identical, and if you want, you can do the same operations with the Mathematics dataframe and compare the results. Also, visualization is recommended to present the results of the machine learning work to different stakeholders. in S3: Now everything is ready for coding! We acknowledge that the differences in the engagement levels may not necessarily be a result of participation in the competition but it is still an interesting aspect.

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