5 one problem statement for education industry is

expertise in applied mathematics.
5. One problem statement for Education industry is social –emotional learning
The broad availability of educational data has led to an interest in analyzing useful knowledge to
inform policy and practice with regard to education. A data science research methodology is
becoming even more important in an educational context. More specifically, this field urgently
requires more studies, especially related to outcome measurement and prediction and linking
these to specific interventions. Consequently, the purpose of this paper is first to incorporate an
appropriate data-analytic thinking framework for pursuing such goals. The well-defined model
presented in this work can help ensure the quality of results, contribute to a better
understanding of the techniques behind the model, and lead to faster, more reliable, and more
manageable knowledge discovery. Second, a case study of social-emotional learning is
presented. We hope the issues we have highlighted in this paper help stimulate further research
and practice in the use of data science for education.
Emerging AI technologies not only pose threats but also create opportunities of producing a
wide variety of data types from human interactions with these platforms. The broad availability
of data has led to increasing interest in methods for exploring useful knowledge relevant to
education—the realm of data science
Although all those issues have varying significances regarding the measurement and
development of social and emotional learning, the following two research goals are priorities
for studies of social and emotional learning:
1. Developing assessment techniques,
2. Providing intervention approaches.
To better pursue those goals, it could be useful to formalize the knowledge discovery
processes within a standardized framework in DS. There are several objectives to keep in
mind when applying a systemic approach
1) Help ensure that the quality of results can contribute to solving the user’s problems;
(2) A well-defined DS model should have logical, well-thought-out sub steps that can be
presented to decision-makers who may have difficulty understanding the techniques
behind the model;

(3) Standardization of the DS model would reduce the amount of extensive background
knowledge required for DS, thereby leading directly to a knowledge discovery process
that is faster, more reliable, and more manageable..
In the context of DS, the Cross-Industry Standard Process for Data Mining (CRISP-DM) model
is the most widely used methodology for knowledge discovery.