Impact of Misinformation on Student Grades: A Research Case Study

Impact of Misinformation on Student Grades: A Research

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Introduction: Impact of Misinformation on Student Grades: A Research

Problem formulation

The correlation between students' performance and their receptivity to disinformation is a prominent area for investigation. Due to the proliferation of social media platforms and the widespread availability of online resources, hoaxes and conspiracy theories have gained traction in recent years. Particularly vulnerable are students, who may lack experience or training to judge the reliability of the material they receive. This raises questions concerning the effect of misinformation on pupils' academic achievement.

Several distinct pieces make up the problem statement for this area of study. In the first place, we need to figure out what kinds of misleading material pupils encounter and how often. This might be in the form of fabricated news reports, rumours, or even conspiracy theories. The scope of the issue may be better grasped if we know how often such misinformation is spread and by what means.

Second, looking into how kids' grades are affected by exposure to disinformation is crucial. The impact on critical thinking and decision-making skills, as well as the relationship between exposure to erroneous material and grades, might be examined. These are examples of the kind of inquiries that demand answers.

The third important consideration is figuring out why pupils are so easily misled. Age, gender, socioeconomic level, education, and experience with misinformation are all potential contributors. To lessen the negative effects of incorrect information on academic performance, it is important to understand the underlying variables that contribute to sensitivity to false information. Last but not least, studies should try to determine what treatments may be used to teach students how to think critically about information and lessen their likelihood of being misled by it. To that end, educators may choose to provide curricula or supplementary materials that equip students with the knowledge and tools necessary to identify and evaluate other viewpoints. Therefore, this research topic's problem formulation should centre on determining the nature and extent to which students are exposed to false information, studying how such exposure affects students' academic performance, probing the underlying factors that make them susceptible to such misinformation, and creating strategies to lessen its negative effects.

Project rationale

A major issue in modern culture is the proliferation of misinformation, which has spread rapidly over the Internet and, in particular, social networking sites. When it comes to success in school, the effects of spreading false information may be devastating. The purpose of this study is to investigate whether or not students' grades correlate with their propensity to believe incorrect information. Misinformation is not a new issue by any means. The widespread availability of social media, however, has made it simpler and quicker than ever to spread misinformation. Students are especially at risk since they often use the internet to complete their homework assignments. Inaccurate or misleading data might cause people to draw the wrong conclusions.

One theory as to why students with lower grades are more vulnerable to incorrect information is because they are more prone to look for information from unreliable sources (Sharma et al., 2019). It's possible that incorrect information may spread if people relied too much on these sources. Lower-level pupils may also lack the critical thinking abilities essential to determine whether or not a given source is reliable.

On the other hand, those that do well academically may approach material with more scrutiny. They may be more able to think critically and assess the reliability of the information they receive. Students with better grades may, in certain situations, be overconfident in their skills and miss the possibility of misleading information because of this. In today's information-driven culture, it is vital to understand how academic performance correlates with receptivity to disinformation. It may provide insight into instructional strategies, drawing attention to the value of both critical thinking and information literacy. It may also aid in the creation of treatments meant to counteract the impact of misinformation on students' academic outcomes. The overarching goal of this study is to clarify the intricate connection between academic performance and receptivity to disinformation. It can gain a deeper understanding of the factors that contribute to the proliferation of misinformation among students by investigating this connection.

Theory concepts

Education and psychology researchers are becoming more interested in the correlation between students' performance and their propensity to believe erroneous information. There has been rising anxiety in recent years about the effects of disinformation on people's beliefs, opinions, and actions. It's only logical to wonder whether there's a correlation between students' grades and their ability to recognise and reject erroneous information, given the significance of critical thinking abilities in assessing data.

The dual-process theory of thinking is one explanation for why certain people are more prone to believing false information as a function of their grade point average. Fast, instinctive, and intuitive System 1 thinking is contrasted with slower, more careful and analytical System 2 thinking in this idea. Students that do well academically are hypothesised to be more adept in System 2 thinking, which equips them to analyse information critically and reject propaganda. In contrast, lower-achieving pupils may be more likely to depend on System 1 thinking, leaving them open to false information (Liv and Greenbaum, 2020).

The correlation between students' performance in school and their propensity to believe false information might also be explained by students' levels of drive and interest. Students that are interested in their studies and want to do well could be less prone to fall for hoaxes since they seek out and assess material critically. On the other hand, pupils who aren't as invested or enthusiastic may not think as critically about what they read, leading them to accept incorrect material at face value. Individual variations in cognitive capacities, such as working memory or attention, may also play a role in the relationship between grades and sensitivity to false information. Those with larger amounts of working memory, for instance, may be better able to comprehend and analyse information, thereby making them more resistant to misleading information. In a similar vein, pupils who are able to concentrate on important details while ignoring irrelevant details may be less likely to fall for hoaxes. Overall, there is probably a complicated and nuanced link between grades and sensitivity to disinformation, with many variables contributing to individual variances in this area. More study is required to clarify the dynamics of this connection and uncover methods that can aid students in becoming more critical thinkers and resilient to disinformation.

Literature review

To assess the relationship between the grades students and false information

There has been growing concern about the spread of false information, especially in the age of social media.

According to the source Ozbay and Alatas, (2020), the author conveys Misinformation regarding health is only one example of how distributing false information may hurt people. In this light, it's important to investigate whether or not students' propensity to believe erroneous information is correlated with their academic performance. At first look, it may seem that intelligence and academic performance are unrelated. The capacity to spot fake news, on the other hand, is more general and may be impacted by skills like critical thinking and media literacy, which are seldom taken into account when assigning marks. Nonetheless, there may be a connection between academic success and the capacity to spot fake news, as suggested by a few studies. High-achieving students, for instance, are more likely to be able to spot false news than low-achieving students, according to research published in the Journal of Educational Psychology in 2020. Using a sample of 104 high school students, the researchers discovered that individuals with higher grades were better able to spot bogus information. Students who scored higher in English language arts were also more likely to recognise false information as such. Similar results were reported in another research that was published in the Journal of Educational Computing Research in 2021. A total of 174 high school students participated in the research, and the results showed that the students with higher GPAs were also better at determining the reliability of material provided online. The authors hypothesised that this is because high-achieving pupils are better equipped to assess the reliability of information because to their developed critical thinking abilities. Therefore, it may appear that there is no connection between grades and the ability to tell the difference between fact and fiction, but there may be one, according to some studies. Students who consistently perform at the highest levels may have an easier time spotting false news and judging the reliability of internet sources because of their developed critical thinking abilities. To verify these results and investigate the processes behind this connection, further study is required.

To analyse the way student grades can be affected by the use of fake information via social media

The way people communicate with one another and spread information has been profoundly altered by the advent of social media.

According to the source, Injadat et al. (2020) Facebook, Twitter, and Instagram are all great places for students to network and exchange helpful learning materials, but they may also spread false information and distract students from their studies. To begin, misinformation on scholarly topics may spread rapidly via social media networks. Students who depend on it may acquire skewed or insufficient understanding about the subject at hand, which may have a negative impact on their schoolwork. Furthermore, social media can be used to disseminate inaccurate information about exams or assignments, leading to confusion and poor performance. Second, pupils may lose concentration when using social media. It's not uncommon for students to lose track of time while wasting it on social networking sites like Facebook, Twitter, and Instagram. Students may become less focused on their work as a consequence of this disruption, which may ultimately lead to worse academic performance. Finally, social media can foster a culture of dishonesty in the classroom. Students who cheat on examinations or share their homework with others through social media may face disciplinary action and poorer scores. The use of social media also has the potential to significantly affect academic performance. The culture of academic dishonesty, the proliferation of erroneous material, and the disruption of students' concentration all have the potential to lower their grades. To mitigate these risks, students should be mindful of the sources they use for academic information, limit their social media use, and adhere to academic integrity policies. Teachers must understand the influence of social media on their kids and instruct them in appropriate behaviour.

To identify fake news affecting the performance of students and impact upon their mind

As a widespread issue in today's society, fake news can have serious consequences for students' academic and emotional well-being. It is crucial for students to understand what false news is and how it spreads in order to recognise it and protect themselves from its harmful effects.

As per the opinion of Stiawan et al. (2020), the author conveys that the term "fake news" refers to erroneous information that is widely disseminated through various media. Its harmful effects on people and societies stem from the fact that it is often intended to mislead readers or stir up controversy. Students may experience a variety of negative effects from their exposure to fake news, including lowered academic performance and emotional discomfort. The first step in spotting bogus news is verifying its credibility. Major news outlets and long-standing news organisations are good examples of reputable news sources. It is crucial to perform more investigation since many fake news reports are made to seem like actual news stories. One strategy for doing so is to consult other news outlets that have covered the same topic. It's more likely to be accurate if the same story is reported by numerous reliable sources.

Looking for bias or manipulation is another strategy for spotting false news. Inflammatory language, sensationalist headlines, and edited or out-of-context photographs and videos are commonplace in fake news articles. Understanding the story's motivations is equally crucial. To forward a goal or foment dissension among the populace, fake news articles are routinely fabricated. Finally, be wary about spreading information that has not been independently confirmed. There are major implications for people and communities when they propagate incorrect information by sharing fake news reports. Furthermore, the prevalence of fake news is a major concern that has the potential to adversely affect students' academic performance and emotional well-being. Avoid spreading unverified information and always investigate into the credibility of a source before believing what you read. Students may avoid the negative impacts of false news by being knowledgeable and cautious.

Research questions, aims and objectives

The aim of this research is to analyse the impact of fake information on students' grades and in their overall performance.

Objectives

• To assess the relationship between the grades students and false information
• To analyse the way student grades can be affected by the use of fake information via social media
• To identify fake news affecting the performance of students and impact upon their mind

Questions

• What is the relationship between the grades of students and false information?
• How student grades can be affected by the use of fake information via social media?
• How does fake news affect the performance of students and impact their minds?

Research methods (Methodologies)

Research design

The term "research design" is used to describe the comprehensive approach used by researchers to test their hypotheses and answer their questions. It describes the processes, resources, and methodologies that will be used throughout data collection and analysis. The study topic, the data collected, and the available time and money all go into the final decision of research design. A quasi-experimental study design may be used to examine the effects of misleading information or detection on students' academic performance (Gaozhao, 2021). With this setup, a researcher can evaluate the impact of exposure to either false information or detection on students' academic performance. This method is used when a study's experimental and control groups cannot be assigned at random. The researcher has the option of choosing two cohorts of students who are comparable in terms of academic achievement and demographics. False information or detection will be made available to one group while the other group will not. Before and after exposure, both groups' academic performance will be evaluated. Next, we'll see whether there's a statistically significant difference in grades between the two groups. In sum, a study of how students' grades change after being exposed to or exposed to erroneous information might greatly benefit from a quasi-experimental research design, which can shed light on the phenomenon's impacts.

Data collection strategy

The method used to gather data has a major role in determining the reliability and validity of the study's findings. The purpose of this research is to determine whether or not students' grades and performance suffer as a result of exposure to false information. To do so, it will use a thorough data-collection method informed by ethical concerns (Albreiki et al., 2021). To begin, the group will use a mixed-methods strategy, during which quantitative and qualitative data will be gathered. Students at several universities will be polled using questionnaires and surveys to compile the quantitative data. Grades and prior academic achievement will also be gathered, along with other relevant academic data of pupils. Students and teachers will be interviewed individually and in focus groups to compile qualitative data. With this method, we can learn as much as possible about the phenomenon of interest.

Ethical considerations

All volunteers will provide their informed permission, and ethical concerns have been taken into account. They will be briefed about the study's goals, its methods of data gathering, and the advantages and disadvantages of taking part. If at any point throughout the research a participant feels uneasy or as like their privacy is being invaded, they will be free to discontinue participation. It will also protect the privacy of the respondents by concealing their s. Finally, because this is a delicate topic, the research will collect and analyse data without prejudice or judgement (Guskey and Brookhart, 2019). This ensures that neither the participants nor the data will be subjected to our own preconceived notions or biases. Instead, we shall make an effort to take no side and instead let the evidence guide our decisions. In conclusion, this research aims to illuminate how students' academic performance and grades are affected by exposure to false information. A mixed-methods approach, including both quantitative and qualitative data, will be used for the data gathering technique that is ultimately chosen. Obtaining informed permission, protecting participants' identity and confidentiality, and using objective criteria in data analysis are all steps that will be taken to ensure the study's ethical rigour.

Technical solution approach

In today's digital age, the proliferation of false information is of increasing concern. Students have been hit especially hard by this problem, since the proliferation of false information may have a devastating effect on their ability to learn and their final scores. To find a solution, we need to think about the technological approaches and assessment methods that may be used to lessen the impact of false information (Khan et al., 2019).

Developing reliable fact-checking algorithms to assess the veracity of published material online is one technical approach. Algorithms like this might examine the content, origins, and trustworthiness of content throughout the web and social media. The analysis' findings may subsequently be used to identify any material as questionable or unverified, protecting students from being duped by fake news. Artificial intelligence (AI) and machine learning technologies may also be useful in spotting propagandistic tendencies. Such tools might be trained to identify certain forms of disinformation and monitor their online propagation. By identifying possible trouble spots, AI and ML systems can warn teachers and students about the availability of false material that might affect their studies.

Evaluation technique

It is possible that teaching pupils how to think critically and use information effectively might help with the assessment process. Students would benefit from learning how to evaluate the reliability of material found online and how to spot false claims. Teachers may further emphasise the significance of these abilities by including examinations that require students to demonstrate their ability to evaluate sources and spot false information. Establishing open discourse about the presence and effect of bogus information between educators and students is another assessment strategy that may be helpful (Khalaf, 2022). If there was more of a safe space for kids to raise questions and report instances of false information, learning would be more effective. Educators would thus be better equipped to help pupils recognise and avoid false information that might have a negative impact on their studies. The problem of false information has several facets and must be tackled systemically. Teachers may aid their pupils in succeeding academically by using both technological solutions, such as fact-checking algorithms and AI technologies, and evaluative strategies, such as critical thinking training and open communication.

Timeline, planning & risk management

Planning Strategy

Effective research processes are essential for guaranteeing the credibility of data utilised in academic contexts. However, students' grades and performance can be severely impacted by exposure to false information, so it's important to develop a strategy to combat this problem. The following are some important considerations when formulating a plan to address the impact of false information on students' academic performance and grades:

• Define the problem: The first step is to identify the issue at hand, which is the dissemination of false information that has an adverse effect on students' academic standing. The first step is to determine where and how students are exposed to false information, as well as the effect this has on their academic performance.
• Establish a research team: It is recommended to form a research group to look into the issue and come up with potential remedies. Members of the team should have extensive experience in teaching, doing research, and managing large amounts of data.
• Conduct research: The study group needs to investigate where pupils are getting their false news and how it is affecting their academic performance. Examining previous studies, conducting surveys, and talking to educators and students are all essential parts of any investigation.
• Develop solutions: The study group has to come up with ways to fix the issue based on their findings. Training for educators and students, as well as the creation of new regulations and practices, may be part of the answer (Mirjafari et al., 2019).
• Implement solutions: The study team's recommendations should be put into action, which may include revising current rules and procedures, creating new learning materials, and providing additional assistance.
• Monitor and evaluate: The third phase is keeping tabs on and assessing how well the adopted measures are working. Assessing the effectiveness of the proposed remedies in preventing the spread of false information requires monitoring changes in students' grades and performance.

Timeline

Table 1: Timeline

Risk Management

There are potential dangers associated with the approach being designed to counteract the impact of false information on students' grades and performance, thus a risk management plan should also be created. Some major dangers and how they might be avoided are listed below:

• Resistance to change: Existing regulations and processes may be resisted by teachers, students, and other stakeholders. Strategy for reducing risk: include affected parties in planning, provide assistance, and spread the word about how great the changes will be (Alsubari et al., 2022).
• Lack of funding: The study team's proposed remedies may need supplementary money before they can be put into action. Identify potential financial sources, create a budget, and, if required, look for outside money to implement this mitigation approach.
• Limited impact: The study group's proposed remedies to the problem of disseminating false information may not be very effective. Plan for reducing risks involves keeping tabs on how well countermeasures are working and adjusting course as needed (Leeder, 2019).
• Lack of expertise: Information management and instructional technology are two areas where the research team may be lacking. Strategy for avoiding this problem: equipping the research team with additional knowledge and skills. One solution to the issue of false information influencing students' grades and performance is to create a risk management strategy.
• Personal Reflection:As a student, I've learned the hard way that relying on false information may seriously affect both my marks and the quality of my work. When I'm in a rush or don't feel confident in my understanding of a subject, I can't help but be tempted to fudge the truth. However, I recognise that these practices can be counterproductive to my success in school.

Using false data may have disastrous effects on your grades and final products. When I use fabricated or faulty information in my schoolwork, I am not only dishonest with my lecturers but also with myself. As a result, my professor may reduce my mark or possibly fail me for submitting fake information. Moreover, a project that relies on false information may fail to deliver the intended results or meet the required standards. Another consideration is how the presence of false information affects my own academic progress. The most important thing for me to do as a student is to educate myself so that I may succeed in the future. The more I depend on inaccurate sources, the less I learn, which might stunt my progress. There is a risk that I may fail to grasp fundamental ideas or theories that are crucial to my education and development. My academic standing may be affected if false information is uncovered. Plagiarism and other forms of academic dishonesty are taken extremely seriously by professors and universities. Getting caught lying on a college application can ruin not only my reputation, but also my chances of getting into graduate school or getting a job. Furthermore, it is crucial that students refrain from using any fabricated data. It could be tempting to take this shortcut, but I know full well the damage it might do to my education and career prospects in the long run. It's important that I take my time to learn as much as I can about a subject, consulting only reputable sources. This way, I know I'm gaining information and experience that will serve me well in the future while also ensuring the quality of my academic projects and assignments.

References

Injadat, M., Moubayed, A., Nassif, A.B. and Shami, A., 2020. Systematic ensemble model selection approach for educational data mining.Knowledge-Based Systems,200, p.105992.

Mirjafari, S., Masaba, K., Grover, T., Wang, W., Audia, P., Campbell, A.T., Chawla, N.V., Swain, V.D., Choudhury, M.D., Dey, A.K. and D'Mello, S.K., 2019. Differentiating higher and lower job performers in the workplace using mobile sensing.Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies,3(2), pp.1-24.

Alsubari, S.N., Deshmukh, S.N., Alqarni, A.A., Alsharif, N., Aldhyani, T.H., Alsaade, F.W. and Khalaf, O.I., 2022. Data analytics for the identification of fake reviews using supervised learning.Computers, Materials & Continua,70(2), pp.3189-3204.

Baneres, D., Rodríguez-Gonzalez, M.E. and Serra, M., 2019. An early feedback prediction system for learners at-risk within a first-year higher education course.IEEE Transactions on Learning Technologies,12(2), pp.249-263.

Guskey, T.R. and Brookhart, S.M., 2019.What we know about grading: What works, what doesn't, and what's next. ASCD.

Khan, I., Al Sadiri, A., Ahmad, A.R. and Jabeur, N., 2019, January. Tracking student performance in introductory programming by means of machine learning. In2019 4th mec international conference on big data and smart city (icbdsc)(pp. 1-6). IEEE.

Albreiki, B., Zaki, N. and Alashwal, H., 2021. A systematic literature review of student’performance prediction using machine learning techniques.Education Sciences,11(9), p.552.

Stiawan, D., Idris, M.Y.B., Bamhdi, A.M. and Budiarto, R., 2020. CICIDS-2017 dataset feature analysis with information gain for anomaly detection.IEEE Access,8, pp.132911-132921.

Ozbay, F.A. and Alatas, B., 2020. Fake news detection within online social media using supervised artificial intelligence algorithms.Physica A: statistical mechanics and its applications,540, p.123174.

Injadat, M., Moubayed, A., Nassif, A.B. and Shami, A., 2020. Multi-stage optimized machine learning framework for network intrusion detection.IEEE Transactions on Network and Service Management,18(2), pp.1803-1816.

Choudhary, A. and Arora, A., 2021. Linguistic feature based learning model for fake news detection and classification.Expert Systems with Applications,169, p.114171.

Gimpel, H., Heger, S., Olenberger, C. and Utz, L., 2021. The effectiveness of social norms in fighting fake news on social media.Journal of Management Information Systems,38(1), pp.196-221.

Samadi, M., Mousavian, M. and Momtazi, S., 2021. Deep contextualized text representation and learning for fake news detection.Information processing & management,58(6), p.102723.

Shu, K., Wang, S., Lee, D. and Liu, H., 2020. Mining disinformation and fake news: Concepts, methods, and recent advancements.Disinformation, misinformation, and fake news in social media: Emerging research challenges and opportunities, pp.1-19.

Liv, N. and Greenbaum, D., 2020. Deep fakes and memory malleability: False memories in the service of fake news.AJOB neuroscience,11(2), pp.96-104.

Rampersad, G. and Althiyabi, T., 2020. Fake news: Acceptance by demographics and culture on social media.Journal of Information Technology & Politics,17(1), pp.1-11.

Gaozhao, D., 2021. Flagging fake news on social media: An experimental study of media consumers' identification of fake news.Government Information Quarterly,38(3), p.101591.

Vafeiadis, M., Bortree, D.S., Buckley, C., Diddi, P. and Xiao, A., 2020. Refuting fake news on social media: nonprofits, crisis response strategies and issue involvement.Journal of Product & Brand Management,29(2), pp.209-222.

Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M. and Liu, Y., 2019. Combating fake news: A survey on identification and mitigation techniques.ACM Transactions on Intelligent Systems and Technology (TIST),10(3), pp.1-42.

Machete, P. and Turpin, M., 2020. The use of critical thinking to identify fake news: A systematic literature review. InResponsible Design, Implementation and Use of Information and Communication Technology: 19th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2020, Skukuza, South Africa, April 6–8, 2020, Proceedings, Part II 19(pp. 235-246). Springer International Publishing.

Zimmer, F., Scheibe, K., Stock, M. and Stock, W.G., 2019. Fake news in social media: Bad algorithms or biased users?.Journal of Information Science Theory and Practice,7(2), pp.40-53.

Wang, R., He, Y., Xu, J. and Zhang, H., 2020. Fake news or bad news? Toward an emotion-driven cognitive dissonance model of misinformation diffusion.Asian Journal of Cmmunication,30(5), pp.317-342.

Van Duyn, E. and Collier, J., 2019. Priming and fake news: The effects of elite discourse on evaluations of news media.Mass Communication and Society,22(1), pp.29-48.

Ruchansky, N., Seo, S. and Liu, Y., 2017, November. Csi: A hybrid deep model for fake news detection. InProceedings of the 2017 ACM on Conference on Information and Knowledge Management(pp. 797-806).

Vosoughi, S., Roy, D. and Aral, S., 2018. The spread of true and false news online.science,359(6380), pp.1146-1151.

Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M. and Liu, Y., 2019. Combating fake news: A survey on identification and mitigation techniques.ACM Transactions on Intelligent Systems and Technology (TIST),10(3), pp.1-42.

Bali, A.P.S., Fernandes, M., Choubey, S. and Goel, M., 2019. Comparative performance of machine learning algorithms for fake news detection. InAdvances in Computing and Data Sciences: Third International Conference, ICACDS 2019, Ghaziabad, India, April 12–13, 2019, Revised Selected Papers, Part II 3(pp. 420-430). Springer Singapore.

Bastick, Z., 2021. Would you notice if fake news changed your behavior? An experiment on the unconscious effects of disinformation.Computers in human behavior,116, p.106633.

Bhutani, B., Rastogi, N., Sehgal, P. and Purwar, A., 2019, August. Fake news detection using sentiment analysis. In2019 twelfth international conference on contemporary computing (IC3)(pp. 1-5). IEEE.

Leeder, C., 2019. How college students evaluate and share “fake news stories.Library & Information Science Research,41(3), p.100967.