Online learning boosting academic performance Essay Example

the depth of learning that can be achieved through online education (Bali, El-Lozy and Thompson, 2007). Bartley and Golek (2004) criticize the lack of conclusive research on the effectiveness of online education. On the other hand, researchers such as Vachris (1999) have been advocating for a closer examination of performance issues related to online instruction since 1999, with recent contributions from Van der Merwe (2007).

This paper examines the relationship between academic performance and online instruction, specifically focusing on the impact of the online economics classroom. It includes a brief review of literature in Section 2, a description of the online classroom in Section 3, research design in Section 4, data presentation and analysis in Section 5, and concludes in Section 6.

Overview of the literature

A variety of factors have been identified as potential determinants of academic performance in economics. These factors include age, gender, mathematical ability, English language proficiency, class attendance and pedagogic interventions. Table 1 presents correlations between these variables and academic achievement in economics based on studies conducted in South Africa.

Table 1: Factors affecting economics academic performance.

- Relationship between economics achievement and...( variable )
- Identified correlation with economics accomplishment

Student age

- Older pupils generally perform better than younger pupils (Parker, 2006).
- No significant relationship detected between student age and economics accomplishment (Van der Merwe, 2006).

Gender

- Males generally perform better than females in multiple choice appraisals (Van Walbeek 2004, Parker 2006, and Horn and Jansen 2009).
- No significant relationship identified between gender and economics accomplishment (Van der Merwe, 2006).

Mathematical ability

- Robust and positive relationship between economic performance and mathematics scores (Edwards 2000, Smith 2004, Van Walbeek 2004, Horn and Jansen 2009).

English language proficiency
- High school English

language performance is not associated with university economics performance (Van Walbeek, 2004).
- English as a home language is significantly associated with economics performance (Edwards 2000,
Smith
(continued...)

Pedagogic intercessions Particular auxiliary faculties and tutorials have a positive impact on pupils' performance (Smith, 2009). Pedagogic devices that have received relatively little attention in the field of economic sciences management are online and blended learning (Arbaugh, Godfrey, Johnson, Pollack, Niendorf and Wresch, 2009).

The concept of blended learning

Blended learning has been defined as the combination of instructional modes and methods (Graham, 2004). Carman (2005) identifies five elements of a blended learning process. These include live events, self-paced learning, collaboration, assessment, and the availability of performance support materials. Graham (2004) believes that the combination of online and face-to-face instruction best reflects the historical emergence of blended learning systems.

Weighing the evidence in support of online learning

It is not yet established that online learning (even as part of a multimedia/blended learning technique) automatically leads to improved performance (Astleitner and Wiesner, 2004).

Multiple studies have found that there are no significant differences in academic performance between traditional face-to-face classroom instruction and online delivery methods (Vachris 1999, Anon. 2008). In fact, the literature suggests that students who only rely on online learning tend to have lower academic achievement compared to their peers who receive instruction in a traditional classroom setting (Arbaugh et al, 2009, Molae 2007, Vachris 1999, Karr, Weck, Sunal and Cook 2003). However, it is interesting to note that these studies commonly conclude that combining online and traditional modes of instruction leads to better academic performance compared to solely online or traditional classroom instruction (Molae 2007, Karr, Weck, Sunal and Cook 2003).

Various studies have

found positive links between online learning and academic performance. According to St Clair (2009), online economics courses generally yielded higher results compared to traditional classroom approaches. Similarly, Bali et Al (2007) discovered that an innovative instructional method with an online component produced better outcomes than the traditional version of the course. Snipes (2005) concluded that the US Navy's adoption of a blended learning training approach in 2004 resulted in a 44 percent improvement in knowledge retention, among other benefits. Oellerman (2009) reported increased pass rates for various management courses after implementing specific online assessment tools as part of her instructional technique.

When studying the effects of pedagogic interventions on academic performance, one common limitation is a failure to consider various intervening variables. This can potentially lead to inaccurate interpretations of results (Bali et al., 2007). Scholars have emphasized the importance of conducting a more comprehensive examination of the dynamics associated with online learning (Bali et al., 2007; Alstete and Beutell, 2004; Van der Merwe, 2006). Therefore, it is crucial to take into account different variables such as gender, ethnic/cultural background, ability, language proficiency, prior online experience, learning styles, teacher bias, learner motivation and others when assessing academic performance in a blended learning environment that includes an online component. While it may not be possible to include every potential confounding variable in every analysis of the relationship between instructional approach and performance, making some effort to do so is essential for enhancing analytical validity.

According to studies, accessing relevant computer aided learning modules can improve the learning outcomes of repeating students, regardless of their prior ability in economics and mathematics (Thompson, 2000). However, this relationship did not show

statistical significance for non-repeating students.

A meta-analysis by the United States Department of Education (2009) found that online learning conditions generally lead to better outcomes compared to face-to-face instruction. Furthermore, incorporating elements of traditional classroom instruction with online learning enhances these benefits.

It is important to note that blended approaches always require additional learning time and instructional elements beyond what traditional face-to-face settings offer.

According to Alstete and Beutell (2004), the positive effects of blended learning cannot be attributed solely to the media used. They found that online course performance is significantly correlated with online course activity, but not with previous academic performance in synchronous instructional and learning environments. This suggests that online course performance is a distinct type of academic aptitude that is not well understood. Additionally, Alstete and Beutell (2004) concluded that age and gender have no relationship to online course performance in their undergraduate sample.

The researchers discovered that women have a greater preference for online classes compared to men among their graduate student sample. Studies like the ones mentioned here typically assume that teaching interventions can enhance academic performance by motivating learners, although these assumptions may not always be well-founded. The underlying belief behind these efforts is that an increase in student motivation leads to improved academic performance. However, only a limited number of scholarly works have investigated the direct connection between measuring student motivation and its impact on academic achievement.

Research studies on the effectiveness of technology-mediated instructional schemes in boosting motivation and performance have not extensively tested specific claims (Gabrielle 2003, Rienties and Woltjers 2004). Van der Merwe (2006) did not find evidence to support this proposition, but his results may have been influenced

by relying on self-reported data regarding respondents' motivation levels. The validity and reliability of such data have been questioned (Alstete and Beutell, 2004).

The hypothesis that increased learner motivation is significantly associated with increased accomplishment is supported by Song and Keller's (2001) study. They found that learners using motivationally adaptive computer-aided instruction (CAI) performed better than those using motivationally saturated or minimized CAI. This case study examines the impact of online learning on academic accomplishment in a first-year general economics class taught both synchronously and asynchronously. It investigates the connection between learner motivation and performance by analyzing the online learning and performance link. Based on Song and Keller's (2001) work, it is reasonable to theorize that variations in class accomplishments associated with different usage of the economics online classroom are likely influenced by levels of learner motivation.

The blended acquisition manner used in this instance survey is a combination of both Song and Keller's motivationally saturated CAI and motivationally minimized CAI. It does not claim to provide an optimal or adaptive array of motivational tactics, but it does offer a learning dimension that is not possible in a purely traditional chalk-and-talk approach. In this particular instructional blend, online learning features such as threaded discussions, self-assessments with immediate feedback, and interactive tutorials and lessons are used. These features are designed to attract learners' attention, demonstrate the relevance of course material, and increase learner confidence and satisfaction. This type of learning environment is likely to be motivating for both learners who are already motivated and those whose motivation needs to be encouraged. The frequency and format of using the economics online classroom may also affect learners' achievement in economics

grades.

Description of the Blend

Economics 1 at DUT is a compulsory minor course for students majoring in three-year accounting or management degrees.

At DUT, most students take Economics 1 in their first year of study, unless they are in the Management Studies program where it is taken in the second year. However, like other institutions, DUT has a high failure rate for Economics 1, leading to many students having to retake the course. The course is split into two parts: microeconomics in the first semester and macroeconomics in the second semester. Completing microeconomics is required before enrolling in macroeconomics.

Both faculties are taught both in person and online through the Blackboard Learning Management System. While attending in-person lectures is mandatory, students also have access to online resources such as electronic textbooks, lecture notes, interactive lessons and tutorials, tutorial guides, discussion topics, and assessment tools for self-directed learning. The classroom lectures focus on theory and practical exercises to help students achieve desired learning outcomes. It is important to take thorough notes and compare them with the online notes provided. Online tutorials, exercises, projects, past exams, and quizzes further reinforce the content covered in class.

The online quizzes are formative appraisals and can be done multiple times. They are automatically marked and also provide limited feedback. Marking ushers are available online for all other appraisals with the expectation that students will tag their own work. Unfortunately, however, many students see the online economics classroom as an "optional extra," and therefore either do not use it as recommended or do not use it at all.

The DUT introductory microeconomics assessments mainly consist of multiple-choice questions (70-85%) and some short inquiry questions. Before

starting the classes, all students need to attend an orientation session in one of the institution's computer research labs. During this session, they are introduced to the Blackboard Learning Management System interface. In this session, students learn how to log into the online classroom and familiarize themselves with its layout and features, such as chat, discussion, calendar, announcement tools, and other facilities. The expectation is that after being introduced to the online classroom, microeconomics students will regularly use it alongside their traditional classroom discussions. Since DUT provides equal access to computer research labs for all students, this requirement is not unreasonable.

Methodology

This analysis was conducted using a case study of the 2007 cohort of DUT Riverside campus students who were enrolled in the microeconomics department for the introductory economics course. The sample size for this study was the entire population of 250 students enrolled in the microeconomics department. After data cleansing, the sample size consisted of 174 students or cases, which accounted for 69.6% of the total population. The data cleansing process primarily focused on removing cases (outliers) where the number of online classroom engagements significantly deviated from the mean.

Therefore, if the number of hits per instance was too low (; 15), it is likely that the students had forgotten their passwords early on and started using their friends' passes to access the online economics classroom, resulting in unusually high numbers of hits per instance (> 250). Due to its large size, the sample can be considered representative of the population. For example, the average age of females in the sample is 23.3 years (population = 23.4 years), while the average age of males is

23.8 years (population = 23.8 years). The proportions of first, second, and third year-and-older students in the sample are also similar to that of the population (36.8%, 33.6%, and 29.9% respectively), as are the gender proportions (sample males = 41.4%, population males = 42%). The analysis was conducted using the Statistical Package for Social Sciences (SPSS) program. Cases with missing relevant variables were excluded listwise as per its default setting.

The information is briefly analyzed, followed by an Ordinary Least Squares additive regression to test for expected relationships between economic academic achievement and various factors. The study uses the student's initial microeconomics class grade (MicDP) as a measure of academic performance instead of their final grade to ensure a larger sample size and minimize selection bias. Typically, a student's final grade determines their pass or fail status in the module or course.

The final grade is a weighted average of the student's class grade and final exam grade, but institutional rules prevent students from taking the final exam if they have not achieved a class grade of at least 40%. As a result, many students are unable to receive both a final exam grade and a final grade. Using the class grade as the primary measure of academic performance allows for testing the impact of online learning on achievement for a larger group of students, rather than just those who may already be predisposed to perform well for unrelated reasons. However, there is a risk of selection bias if the sampled individuals using the online economics classroom have higher abilities or more motivation than other members of the population who also use the online platform, as any

improvement in academic performance for the sampled individuals may have occurred regardless.

In this case, it cannot be concluded that online activity alone is significantly associated with improved performance. This study uses various measures to minimize sample selection bias. These measures include having a large sample that is representative of the population and using the microeconomics class grade as the metric for academic performance. This choice of metric allows for a wider range of academic abilities in the sample compared to using final marks as the only measurement. Additionally, the study utilizes various control variables in the linear regression analysis to identify strong correlations.

Findings and treatment

Descriptive analysis

Data on gender, age, academic record in the final year of high school, repeating of class, and academic performance in introductory microeconomics at DUT in 2007 were obtained from DUT pupil records. Additional student-specific information was collected from the Blackboard system, which records online classroom activity. The two sets of data were then combined and included in an SPSS database. The majority of individuals in the sample are mainly second language English speakers (66.9%) and most (89%) took mathematics during their final year of high school. A majority also chose economics as a major (68.9%) at an advanced level/higher grade (57%).

It is clear that economic sciences is a popular subject for students, as shown by the fact that a majority of students in the sample (54%) and in the population (52.4%) took the class. Table 2 presents the descriptive statistics for students' online activity, specifically the pedagogic intervention. On average, each student had approximately 91 hits over a period of 4-5

months leading up to the final exam in late May or early June. During this same period, students read an average of 3 specially posted online articles, with very few average responses posted using the online discussion tool. Students attempted an average of about 8 online multiple choice quizzes (which make up around 80% of economics assessments), and accumulated an average of 20 marks from these quizzes (OnlineMicperfT).

The mean duration of online activity was about 5 months.

Arrested Development Analysis

Utilizing multiple arrested development analyses, it is possible to determine if a set of independent variables explains the expected difference in the dependent variable, which in this case is academic performance in introductory microeconomics. Drawing on existing literature, certain predictors of economics performance were chosen to be included in a significant linear regression model (Fa‚‡, a‚†a‚? = 6.051, P; lt; 0.0005 and Adjusted R square = 0.342). This model specified performance ("MicDP") as a function of gender (dummy variable "genderscale", male = 0/female = 1), high school math marks ("mathmarksct2"), high school English marks ("engmarksct2"), high school economics marks ("econmarksct2"), duration of online activity in months ("durationhalf"), total number of online quizzes attempted ("tASS"), and total marks accumulated from completed online quizzes ("OnlineMicperfT"). The last three variables represent various aspects of the online intervention. Student age was excluded from the model since it is considered an insignificant factor and including it would reduce the variation in economics performance it could potentially account for.

Table 3 presents the coefficients of the arrested development theoretical account and Table 4 displays its descriptive statistics. The final year of high school exhibits a significant and direct relationship (p < 5%) between mathematical

proficiency (mathmarksct2), economic sciences (econmarksct2), and English performance (engmarksct2) with microeconomics performance (MicDP) at the first-year university level. This finding aligns with previous literature. Likewise, the duration of use of the online classroom facility (Duration), the number of online assessments attempted (tantalum), and performance in these assessments (OnlineMicperfT) show a significant association with microeconomics achievement (p < 0.05).

The longer students engage with the online installation and perform well in voluntary online assessments, the better their performance in microeconomics. Surprisingly, there is a negative correlation between microeconomics performance and the number of attempts students make in these assessments. This may be due to less proficient but motivated students trying each assessment multiple times, thereby strengthening the opposite relationship. Gender and age do not have a significant association with microeconomics performance, which aligns with existing literature. The proficiency in English as a first or second language (englishscale) is not found to be significantly linked to economics performance, but this finding may have been captured by students' English marks. Similarly, the lack of significance between higher/standard grade in school economics could be explained by the direct relationship between school economics marks and microeconomics performance.The fact that the entire figure of hits per pupil on the online economics classroom was not identified as a significant predictor of change in economics performance is not surprising. This is because students may have accessed the facility for various purposes such as checking on notes or announcements, reading articles, engaging in chat or discussions, marking tutorial work, and taking online quizzes.

One might expect that certain activities, such as completed evaluations or lessons, could yield a greater performance benefit than others, like reading or

note-taking. The main finding of this study is that students' usage of the online component in the blended teaching and learning approach used to deliver the microeconomics module is significantly associated with student achievement, both in terms of duration and format. This finding remains strong even when controlling for factors such as gender, age, English proficiency, mathematical ability, and previous experience with high school economics. While this study provides evidence of the potential performance advantage of investing in online learning, it is important to consider the nature of the mechanism that effectively delivers learning through online content delivery. An explanation might be derived through deduction and by drawing upon existing literature. Thus, a possible reason for different grades attained by two students of similar ability could be their different levels of motivation to engage with the topic or subject.

Pupils who fail to make sufficient use of the online economics classroom are more likely to lack the necessary motivation to engage with the subject matter. However, those who utilize both traditional classroom lectures and the online economics platform gain an additional dimension of learning and greater opportunities to contextualize and reinforce the content. Therefore, students who regularly utilize both the traditional classroom and the online economics platform are likely to have a performance advantage over those who do not.

Limitations of the Study

The study's case research design means that its findings can only be confidently applied to the 2007 population of DUT students enrolled in introductory microeconomics. Future studies could expand on this by examining larger student populations at both engineering and traditional universities, across various subjects. Another limitation is the study's narrow definition

of "performance" as measured by an average summative assessment grade.

This device potentially fails to achieve success in other countries of acquisition. Finally, although the survey aimed to minimize bias by controlling for ability, it did not consider the impact of learner motivation on performance. Future studies could further investigate the link between learner motivation and performance.

Conclusion

Considering the lack of conclusive research on the effectiveness of online instruction, this case study aimed to examine the relationship between academic performance and online direction (as part of a blended learning approach). To manage potential bias, a large sample size was used and factors such as gender, age, and academic ability (measured through high school English proficiency, mathematical skills, and economics knowledge) were controlled for.

Theoretical ac of arrested development