Definition: Formative assessment is an educational approach focuses on shaping and guiding learning progress. It involves iterative gathering and using of students’ learning progress information during the learning process. It is an ongoing process providing feedback to help both teachers and students identifying areas that may need attention or improvement. Data mining, often called knowledge discovery in database (KDD), is known for its powerful role in uncovering hidden information from large volumes of data. Its advantages have landed its application in numerous fields including e-commerce, bioinformatics and lately, within the educational research which commonly known as Educational Data Mining (EDM). EDM is defined by The Educational Data Mining community website, www.educationaldatamining.org as an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational setting, and using those methods to better understand students, and the settings which they learn in”. EDM often stress with the improvement of student models which denote the student’s current knowledge, motivation, metacognition, and attitudes¹. Educational Data Mining (EDM) applies machine-learning, statistics, Data Mining (DM), psycho-pedagogy, information retrieval, cognitive psychology, and recommender systems methods and techniques to various educational data sets so as to resolve educational issues². EDM plays a very crucial role in ‘mining’ the most precise information about the behavior of students as well as gauging the efficacy of the learning process (Sana et al., 2019). The use of EDM methodology reveals useful knowledge about the educational settings, and facilitates the discovery of helpful trends and patterns from large and complex educational datasets (Han et al., 2011)³.

Example in practice: Here is a practical example of how EDM can be applied to formative assessment data. I recommend reading the article provided below:

Using Educational Data Mining to Identify and Analyze Student Learning Strategies in an Online Flipped Classroom https://www.mdpi.com/2227-7102/11/11/668

References:

1. Siti Khadijah Mohamad, Zaidatun Tasir, Educational Data Mining: A Review, Procedia - Social and Behavioral Sciences, Volume 97, 2013, Pages 320-324, ISSN 1877-0428, https://doi.org/10.1016/j.sbspro.2013.10.240. (https://www.sciencedirect.com/science/article/pii/S1877042813036859)
2. ASHISH DUTT, MAIZATUL AKMAR ISMAIL, AND TUTUT HERAWAN, A Systematic Review on Educational Data Mining, Digital Object Identifier 10.1109/ACCESS.2017.2654247, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7820050
3. Kudratdeep Aulakh, Rajendra Kumar Roul, Manisha Kaushal, E-learning enhancement through educational data mining with Covid-19 outbreak period in backdrop: A review, International Journal of Educational Development, Volume 101, 2023, 102814, ISSN 0738-0593, https://doi.org/10.1016/j.ijedudev.2023.102814. (https://www.sciencedirect.com/science/article/pii/S0738059323000901)