Educational data are relatively great in numbers which led to exploring wide-ranging possibilities in education by many companies like Tooliqa. Data science is becoming unavoidable in the field of education.
Also in the educational domain, there are wider areas that need exploration more specifically on the result standards and future prediction and further linking these factors to a specific arbitration.
Using a data analytic framework, it is possible to obtain high-quality outputs faster with a simple processing methodology.
Google developed an AI called Alphago that witnessed a record-winning victory over the world record champion Lee Sedol in a perplexing game called Go. This victory is unbelievable among the AI experts.
On the other hand, educators are anxious that this growing technology will pose a threat to human skills in the upcoming days. This rapidly growing technology provides a lot more possibilities despite the threats.
Due to the wide-ranging data in education, data-driven resoluteness keeps on growing and is expected to grow to newer heights in the future.
Currently, data analysis in education evaluates and makes future predictions based on standardized tests like reading, writing, and arithmetic. But further research should be carried out for better outcomes.
All about data science
Data science analytics sets out the framework for seamless data-analytic thinking.
Data science allows extracting knowledge from a large data source by employing various methodology and theories which are drawn from varied fields like mathematics, statistics, and information technology.
It relies more on statistical methods for the extraction of knowledge from the data source.
Earlier statistical methods have a wider influence on mathematics. On the other hand, data science incorporates some of the computational factors along with conventional statistics to enhance knowledge better management and evaluate a wider range of data.
Future of education
Currently, studies suggest that non-academic skills play a significant role and impact more on students’ success rate, employability, and wellness. Also, social and emotional learning is rapidly growing in the field of academics.
Non-academic skills influence greatly according to the Program for International Students Assessment (PISA) where students lack in the labor market in terms of non-cognitive abilities instead of cognitive skills.
Further learning emotional skills in the young stage of life lead to better physical health and wellbeing. Hence these social-emotional skills now become part of the standard curriculum in many countries like China, Finland, Israel, Korea, Singapore, the US, the UK, and many more.
These social-emotional skills pose a serious issue like establishment, development, and offering skills to students and faculty.
Social-emotional learning needs further research especially in skills like self-control, perseverance, mastery orientation, academic effectiveness, and social proficiency. These non-academic skills will be used for the advancements of intervention strategies, supports meditating, and analysis.
Researchers currently prioritize these social-emotional learning in the educational domain for two reasons, namely,
(i) intent to develop evaluation mechanisms.
(ii) appropriate intervention approaches.
Data science techniques
Appropriate knowledge extraction within a standardized framework makes expected better results possible. Some aspects should be considered before applying the appropriate methodology.
(i) the results should be helpful to solve the user’s problem more effectively.
(ii) data science model should be more logical, and the level of difficulty should below.
(iii) standardization of the data science model will be more effective for the user to carry out the knowledge discovery process skipping the process of learning which greatly saves a lot more time, more genuine with easy management.
A most commonly used technique for knowledge discovery in data science is the cross-industry standard process for data mining (CRISP-DM) model.
Also, this model is further used in commercial knowledge discovery systems called SPSS modeler (Statistical package for the social sciences).
Using this base model CRISP-DM advancements for academics should frame out by processing the research-oriented steps which are as follows:
1. Deep knowledge about the problems
The process starts from identifying and understanding the problems to be solved. In this case, we can obtain the more common problems from the educational experts.
Once the better understanding of the problems split up the goals into multiple data science subtasks using the data science tools in the next step. Generating the problem domain is considered to be the initial step.
2. Understanding the data
The next step comes with collecting the sampling data from the data source. These are the data that are readily available and allow us to choose the data as per our requirements.
This step makes us understand the nature of any data by checking its integrity, redundancy, and missing values.
The next comes the crucial part, estimating the cost of each data source to get to know whether further investments can help you with more benefits.
Currently, data science in the educational domain relies on two most common data sources where one being the student information systems and the other one being the learning management systems.
3. Data preparation
Data preparation is nothing but manipulating and converting the raw data into meaningful information which acts as input data for the data science tools. Some DS tools accept input in form of symbolic and categorical data whereas some accept only numeric data.
This preparation involves identifying the sample data, establishing the relationships between the data, cleaning the missing and useless data. In addition, feature selection and data reduction algorithms are used to further filter the data.
The prepared data are further converted into a tabular format for better understanding.
4. Data mining
To the preprocessed data several data mining techniques are applied to obtain the data corresponding to the pattern matching the optimal values.
5. Knowledge evaluation
The obtained patterns are further evaluated to identify whether the results are effective and satisfy the research goals. If the results are not up to the mark the process again gets repeated with an alternative scenario to obtain better results.
Also, this evaluation process checks for the results novelty, attractiveness, and dominance.
The visualizing models are rapidly growing which reshaped the data analytics and made it easily accessible to even non-specialists.
This visualization can be further classified as time-based visualization, context-based visualization, emotional changes visualization, and information visualization.
6. Using knowledge discovery
The last step of this process is the knowledge discovery and making them real-time. This knowledge discovery can be used to enhance wide-ranging possibilities including teaching, learning, administrative adoption, and much more.
Also, this knowledge can be further visualized for easier access. Also, extensive knowledge can be used to check the robustness, and finally comes the deployment.
In the educational domain, the advancements of data science are highly growing due to its availability of data. Many data science models are now implemented in education today to ensure better results.
Researchers exploring social-emotional learning to a great extent will be unavoidable in the educational domain in the future.
Also read: Data Science In Education: Socio-Emotional Learning (tooli.qa)
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