BIG DATA IN EDUCATION
Big data lies at the heart of making data-driven decisions. Big data holds immense potential for educators, offering insights to personalise learning, identify struggling students, enhance teaching methods and improve overall educational outcomes. However, harnessing this power requires careful planning and a strong foundation. This blog post will explore the opportunities and challenges of Big Data in education, outlining the key steps institutions can take to leverage this valuable resource effectively.
Datasets that satisfy at least three of the five V’s (Volume, Velocity, Variety, Veracity, and Value) can be characterised as Big Data. As the digitisation process accelerates, educational institutions generate vast amounts of data (Volume) rapidly (Velocity) and consistently across various platforms. The data inputted comes in the form of both structured (e.g., student information in MIS system) and unstructured (e.g., student interactions on online homework platforms) information (Variety). As educational institutions race to be kept abreast of technological advancements, they are inundated with big data on a day- to day basis.
How does big data work?
As we all know, the COVID-19 pandemic forced over a billion of students out of schools and college classrooms and into virtual learning environments. The World Economic Forum declared the landscape of education as changed forever, as we witnessed educators armed with data and technology make significant improvements to school systems, students, and curriculum.
However, before leveraging the opportunities that big data presents, it is crucial for educational institution to understand and implement the foundations that would drive big data analysis efficiently.
Foundations for use of big data:
- Set a big data strategy: Plan how to collect, store, manage and share the data assets you have, both internally and externally. Lay out a high-level plan on how to integrate big data into your current and future strategies, roadmaps & decision-making processes.
- Identify data sources: Identify all the different places you can gather data from to support your strategies. Examples of data sources include real-time data feeds, social media interactions, publicly available information, and any other relevant sources of data that can provide valuable insights.
- Collect, store, manage and access big data: Create a data architecture that is scalable, accessible, and consumable in near real time by various departments either locally or globally.
- Analyse big data: Examine the data to focus on pertinent information related to the situation. Be ready to distinguish between noisy and irrelevant data points and establish methods to visualise the analysis effectively.
- Fact based or Smart Decision Making: Revise or integrate business processes to incorporate the analysed data, enabling real-time feedback and fact-based decision-making to inform operations.
When it comes to developing a robust big data strategy or framework, it's crucial to take your time and consider all the factors involved. Why not get some help from experts who've been in the field for years? We can offer valuable insights and guidance tailored to your institution's unique needs. Schedule a preliminary discussion to assess your institution's current position in its data journey and explore how we can assist you in advancing further.
Opportunities big data creates in education:
To fully harness the power and opportunity big data creates, one must not only collect the information but strive to excel in analysing and interpreting the information.
Advanced analytics and data mining techniques are instrumental in uncovering patterns, sentiments, correlation, and trends. For instance, in the context of an education institution these techniques can identify at-risk students, ensure students are making sufficient progress, and enable the implementation of a more effective system for evaluating and supporting both teachers and students.
Educational institutions can also use big data to and for:
Increase productivity: Data analysis can help teachers identify the most effective and engaging methods to deliver lessons and even reveal lesson topics that are the most impactful and beneficial to students the most.
Teachers’ professional development: School administrators can use productivity stats and student performance data to identify skill gaps, pinpointing areas where teachers may require additional support in their teaching methods to enhance student engagement.
Personalised Learning: Analysing students’ personal preferences, interests, and learning behaviours will allow educators to customise content in a personalised manner, leading to enhanced academic achievements and a richer learning experience for students.
Develop Curriculum: By conducting thorough analyses of curriculum, student performance, and engagement, educational institutions gain factual insights on how to refine their curriculum to support weaker areas for students. This fosters a data-driven, dynamic, and responsive curriculum that evolves based on evidence rather than assumptions.
Learning Analytics: Analysing data in near real-time facilitates swift responses to areas requiring improvement in student performance. Combining academic data with attendance records can reveal problem areas that extend beyond learning. Factors such as time spent on topics, quiz scores, and engagement with online homework aid educators in understanding individual students' strengths and weaknesses, enabling early detection of those at risk.
A notable article by The Washington Post highlighted Georgia State University's analysis of over a decade's worth of transcripts from 140,000 students. This analysis identified eight hundred risk factors associated with higher chances of student dropout. For example, it revealed that only 1 in 4 political science majors who earned a C in their first class graduated on time. This insight allowed GSU to implement measures that reduced the dropout rate by 32%.
Sentiment Analysis: Unstructured information found in emails, feedback surveys or other communication exchanges between educational institutions, parents, or students holds significant value. Analysing the underlying sentiment and emotion behind comments and answers in a student survey can offer insights into their wellbeing and potential concerns. For instance, consider a scenario where a student regularly attends gym classes (which can be tracked through swiping into these classes). By aligning this data with academic and attendance records, a comprehensive picture of the student’s behaviour emerges. Any deviation from the norm can alert the institution to intervene promptly.
Educational Research: Big data enables researchers to delve into educational trends, effective teaching methods, factors impacting standardised scores and more. When these findings are shared with policymakers, they contribute to a fact-based approach to shaping educational policies.
While big data in education offers opportunities, it's crucial to acknowledge the limitations associated with its implementation and utilisation.
Limitations of big data:
- Overreliance on quantitative: Relying excessively on quantitative data poses a risk of overlooking the knowledge and expertise that educators bring to the sector. Data should complement human reasoning, socio-economic contexts, critical thinking, and creativity to inform decision-making effectively.
- Data privacy: Educational institutions must establish proper frameworks to manage and safeguard data privacy. This involves extracting value from big data while effectively protecting sensitive student information.
- Data security: In addition to ensuring and maintaining data privacy, it's crucial to recognise the security parameters surrounding the data. Adhering to data security principles is essential to provide appropriate data access to relevant audiences. Educational institutions must also take all necessary precautions to safeguard against data breaches and cyber-attacks, which could compromise the integrity of student and institutional data.
- Unconscious bias: To prioritise data in decision-making, it's vital that the data remains free from bias to uphold fairness and equity in the process. Bias can distort trends and predictions, contradicting the essence of utilising big data.
- Lack of data literacy: To leverage the opportunities presented by big data, educators and administrators must possess a certain level of data literacy skills to interpret data and trends effectively. Educational institutions should implement training and professional development programs before embarking on a big data journey.
- Implementation and Support: Implementing a robust, scalable centralised source of diverse and high-volume educational data can be costly, so it's essential for educational institutions to understand the return on investment (ROI) from that investment. Deriving the ROI can sometimes be challenging since the benefits are not immediately measurable and quantifiable. For ongoing changes and analysis, it's crucial to consider support arrangements post-implementation.
Looking to avoid common pitfalls and manage the risks associated with starting a big data project? Let us handle the complexity for you. Simply provide us with a brief description of your situation, and one of our consultants will reach out to you promptly. We're here to help you navigate the world of big data with ease.
The increasing use of technology has led to an abundance of data available to educators to manage. Especially for complex organisations like educational institutions making high impact decisions, big data acts as a valuable tool for making informed decisions, streamlining processes, and operating efficiently based on factual evidence rather than assumptions.
However, successfully implementing and benefiting from this data-driven culture requires careful planning and execution. Upskilling and embedding data literacy within the organisation are crucial steps to ensure the success of the big data journey. Once the foundation is firmly established, educational institutions can take advantage of the multitude of opportunities. From enhancing the learning experience to early detection of safeguarding incidents, big data analysis offers a holistic approach to improving educational outcomes.