How MOOC Students Behave And How Students Score On Exams

Introduction to Studying How MOOC Students Behave And How Students Score On Exams: Assignment Sample

It analyzes what can affect students’ marks at the end of their university studies. It looks at how performance in school relates to a student’s age, how much they engage with what they study, their grades on tests and their general course assignment results.

Research Aim

The primary aim of the project is attempting to discover if these strategies make it easier for students to perform well in their EOD programs.

Research Objectives

• To check the scores for exams, assignments and the final marks of those attending.
• To look at whether the results for students in different groups are different.
• To examine if there is a clear link between course lessons, exams, amount of engagement and how students perform in the end.
• To know which factor plays the greatest role in predicting how students will do in exams.
• To explain how students can boast better academic performance.

Research Question

• Q1: How do they usually do in tests, assignments and final exams?
• Q2: Are the outcomes for older students usually better than for younger students?
• Q3: Is there any correlation between doing assignments, taking tests, being more involved and getting good marks?
• Q4: Which aspect most strongly affects how much a student earns in exams?
• Q5: In what ways can this help students achieve more success in their studies?

Hypothesis

• H0 States that, on average, every student scores the same on exams, coursework and the final marks.
• H1: The average scores students get on exams, coursework and final marks are not the same.
• H0: Older students and younger students are predicted to have the same final marks.
• H1: Older students are predicted to get better final marks than younger students do.
• H0: Suggests that there is not a link between coursework, exam marks, engagement and final marks.
• H1: Claims that there is a strong relationship between coursework, test scores, effort and final grades.
• H0: Coursework, exam grades, engagement rate or age have no strong influence on the final marks.
• H1: One of the factors (coursework, exam, engagement or age) clearly affects how students perform in their final marks.
• H0: Explaining the results does not help students do better.
• H1: Improving student performance is possible by understanding these results.

This MOOC Students Assignment Sample examines the factors influencing university students’ academic performance, including age, learning engagement, test results, and coursework grades. An Assignment helper can support students in understanding academic performance patterns and evaluating how different factors affect final examination scores.

Data Set Overview

This data is about 12 students. Their age, social media involvement, how often they turn up for class, marks in assessments, exam grades and final grade are listed. 18–24, 24–30 and 30+ are the age groups under consideration. About two-thirds (8 out of 12) of the students are 18 to 24 years old. The online engagement score is between 44 and 106 and attendance is between 44.1% and 99.9%.

Every student has 4 exam points and 8 points for coursework. Marking for exams covers the range of 34 to 87 and coursework is from 29 to 92 (Lee et al. 2021). Tests results vary widely from 49.5 to 81.7 in their raw form and after moderation, they are between 48 and 80. The most common grades were 2:1s (for 6 students), then followed by 4 1st class honors and 2 2:2s.

It allows to see the connection between age, subjects studied and tests with students’ final marks.

Analysis and Discussion

Descriptive Statistics Analysis

The first test shown in the report is called descriptive statistics. It provides easy facts about the data. It shows the usual score, the highest score and the lowest score for student activities and outcomes. It clarifies the big point being made.

For example:

• Most students got about 58% as their final grade.
• There was a tendency for students to get about 55% in exams.
• Students did better overall in their coursework than in their exam.
• Students had very different levels of involvement with online classes.

It allows the teacher to see the general level of performance by students.

Contingency Table and Test

The value of the Pearson Chi-Square is present at 92.729, Likelihood ratio is 94.433, and N of Valid Cases are present at 1624 in terms of Chi-Square. The age group moderated mark is present at 1624 which is 100%.

After that, the report employs what is known as a contingency table. Table can be used to see the similarities between two groups (Alexandron et al. 2023). Here, it studies how age group relates to the final numbers.

Older students tend to get better scores at the end than those who are younger. This shows that ageing may make students more successful in school. Students in higher grades might perform better since they tend to be better at managing their time.

Correlation Analysis

Between the online engagement the pearson correlation is present at 1, and moderated mark and online engagement pearson correlation is present at 0.371. The sig (2-tailed) is present at <.001, and N value is present 1624.

It checks the connection between the two different things (Li et al. 2022). Correlation is the test it uses. It demonstrates if there is a strong or weak link between what students do and their final grades.

Studies conclude:

• The more effort put into coursework shows up positively in the finals.
• Final marks are connected to how students do in the exams.
• Online presence is less closely connected, but it also has a positive effect.
• Data shows a minor link in which older teens perform a little better.

Generally, students who do well in coursework and exams receive higher final grades.

Regression Analysis

The sum of squares value in terms of regression is present at 97339.010. The discounting factor is present at 6. At 16223.168 mean square value is present, and the value F is present at 1820.487.

The following step is to use regression analysis (Chi et al. 2023). It tells which factor or combination of factors, has the biggest effect on the final grade. It would mean asking, “If can see the marks for the exams and coursework, will it help predict the final result?”

According to the results:

• The main thing that determines acceptance is coursework.
• Still, examination marks play a big role.
• Age and online activity have some effect, but studying and testing for the certificate is most important.

Therefore, to reach a better grade, a student should make sure to do well on both coursework and exams.

T-test analysis

In one sample statistics the mean value at 67.17164, standard deviation at 8.297785, and standard error mean at 0.205906 is present. The t statistics and discounting factor value is present at 326.255 and 1623 respectively with the mean difference value at 67.171644 (Tzeng et al. 2022). In this regard, Cohen’s d at 8.297785 and Hedges correction is present at 8.301622 with 95% confidence interval.

A t-test helps to compare the differences between two groups. The report uses a t-test to find out if the results change much between age groups.

The study discovered that students who are older tend to do better in their final marks than younger students. The effect is not very big but is still worth noticing. It confirms what was found in the earlier contingency table.

Data Visualization

The report shows data using charts to makes things simpler to grasp. The charts are made by using Excel spreadsheets.

This chart illustrates what scores students achieved by age group. On average, older students do better in science than younger students.

The graph shows the grade distribution for the exam. It shows what scores each student received in his or her exams (Mair et al. 2022). Few students made scores over 60 and the majority got marks in the range of 50 to 60.

This graph displays how students did in their coursework (Yoo et al. 2022). Students generally did better on their coursework than they did on exams.

This chart represents online involvement. There was a big difference in participation between students. Engagement does not have a strong impact, but it provides some help. Trends are clearly visible in charts (Jansen et al. 2022). They indicate that how well a student does in courses and their age may be the biggest influences on success.

Discussion

All the results and charts from the tests are gathered into the report to discuss their meanings. It explains that:

• Those who do well in their classes end up with good overall performance.
• Teachers often find that the older children have better results.
• While engagement is great, mostly what matters is the relationship.
• Although exams play a role, coursework is more important.

Looking at coursework allows foreseeing how a student will score in the final outcome (Zhu et al. 2022). Growing up and maturing make it easier for students to learn.

The report further notes that although being online helps, it does not meet all the needs by itself. To obtain high marks, students must spend effort on exams and coursework.

Conclusion

Based on the findings, there are certain things that play a bigger role in student success than others. Among them, the most important are:

• Scores from Graded Assignments
• Scores on tests
• Gender
• Interacting through the internet

The analysis indicates that coursework produces the strongest impact. Therefore, students need to give careful attention to the lessons. Helping students become better at coursework is a task for teachers too. Because they are more focused or organized, older students usually find studying to be less of a challenge.

The report recommends that universities should help younger students more so they can compete with the older ones. Engagement into learning should be made more useful to make sure all participants improve.

In summary, wanting better results involves:

• Give the studies serious attention
• Get prepared for each assessment.
• Be involved in lessons and the virtual discussions.
• Be as efficient as can be with the studies, as more experienced students usually are.

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Reference List

Journals

• Alexandron, G., Wiltrout, M.E., Berg, A., Gershon, S.A.K. and Ruipérez‐Valiente, J.A., 2023. The effects of assessment design on academic dishonesty, learner engagement, and certification rates in MOOCs. Journal of computer assisted learning, 39(1), pp.141-153.
• Chi, Z., Zhang, S. and Shi, L., 2023. Analysis and prediction of MOOC learners’ dropout behavior. Applied Sciences, 13(2), p.1068.
• Jansen, R.S., van Leeuwen, A., Janssen, J. and Kester, L., 2022. Exploring the link between self‐regulated learning and learner behaviour in a massive open online course. Journal of Computer Assisted Learning, 38(4), pp.993-1004.
• Lee, C.A., Tzeng, J.W., Huang, N.F. and Su, Y.S., 2021. Prediction of student performance in massive open online courses using deep learning system based on learning behaviors. Educational Technology & Society, 24(3), pp.130-146.
• Li, S., Du, J. and Sun, J., 2022. Unfolding the learning behaviour patterns of MOOC learners with different levels of achievement. International Journal of Educational Technology in Higher Education, 19(1), p.22.
• Mair, B., Schön, S., Ebner, M., Edelsbrunner, S., Leitner, P., Schlager, A., Teufel, M. and Thurner, S., 2022, November. Patterns of quiz attempts in a MOOC. The full-points-pattern and other patterns on the way to a successful MOOC in a lecture setting. In EdMedia+ Innovate Learning (pp. 1169-1179). Association for the Advancement of Computing in Education (AACE).
• Tzeng, J.W., Lee, C.A., Huang, N.F., Huang, H.H. and Lai, C.F., 2022. MOOC evaluation system based on deep learning. International Review of Research in Open and Distributed Learning, 23(1), pp.21-40.
• Yoo, J.E., Rho, M. and Lee, Y., 2022. Online students’ learning behaviors and academic success: An analysis of LMS log data from flipped classrooms via regularization. IEEE Access, 10, pp.10740-10753.
• Zhu, J., Yuan, H., Zhang, Q., Huang, P.H., Wang, Y., Duan, S., Lei, M., Lim, E.G. and Song, P., 2022. The impact of short videos on student performance in an online-flipped college engineering course. Humanities and Social Sciences Communications, 9(1), pp.1-10.