Predictive modelling is used in educational data mining (EDM) and learning analytics (LA). This predictive modelling has become a core practice in companies like Tooliqa and for researchers, greatly for predicting student's success rates based on academic achievements.
Predictive analytics is a collection of methodology to make speculations about uncertain eventful events. In the educational domain, predictive analytics is mainly used to measure learning, teaching, or any other proxy measure for administrations.
Predictive analytics has a deep-rooted history in the area of research in education and several commercial products now engulf predictive analytics in learning content management systems.
On the other hand, specialized companies provide predictive analytics for higher education in terms of consulting and products. Let us discuss more about predictive modelling and how these techniques are being applied in teaching as well as learning.
Distinguish predictive modelling from explanatory modelling
In explanatory modelling, the ultimate goal is to use all the available facts to explain an existing outcome. For instance, observations of age, gender, and socio-economic status of a learner population and how they come up with the student's achievement results in a regression model.
The aim of these explanations is generally the results obtained through experimental studies that depend on the interpretation of the causation.
In predictive modelling, the intent is to create a model that is used to predict the values of new data based on the observations. Predictive modelling is based on the assumption of value or classes of new data based on the set of known variables.
The difference between explanatory and predictive modelling is that predictive modelling deals with future events whereas explanatory modelling does not make any claims about the future. There is a more pragmatic difference between explanatory and predictive modelling when applied to educational data.
Explanatory modelling is a post hoc and reflective activity aimed at developing an interpretation of an occurrence. Predictive modelling is an in-situ activity aimed to make systems responsive to the existing fundamental data.
It is feasible to apply the modelling to higher education technologies. For instance, both methods intended to design the intervention systems, the former does this by building the software based on the review of explanatory models developed by experts while the latter does this using the data collected from the authentic log files.
Predictive modelling is a widely used statistical technique to predict future behavior.
The colossal methodological difference between the two modellings is how they address the generalizability issues. In explanatory modelling, all the data are collected from a sample which is used to describe the general population.
The generalizability issue deals with the sampling techniques. Ensuring whether the samples represent the general population by minimizing the selection bias, through random or stratified sampling and ensuring the appropriate sample based on the analysis of the accepted population size and the levels of error.
In a predictive model, a hold-out dataset is mainly used to assess the suitability of the prediction model and protect against the overfitting of data in the training. There are different strategies for creating a hold-out dataset which includes k-fold cross-validation, leave-one-out cross-validation, and much more.
Predictive modelling workflow
1. Problem identification
In the domain of education, predictive modelling deals with a larger action-oriented educational policy and technology context, the institutions use this model for real-time student needs.
The predictive modelling works well and accurately predicts the outcome of any student who has no new intervention. For instance, one might use this predictive model to determine when an individual student completes their academic degree with no intervention strategy.
Several factors make predictive modelling more difficult like sparse and noisy data pose challenges to creating an accurate predictive model.
2. Data collection
In predictive modelling, historical data is commonly used to create the relationship model between the features. The researchers must primarily identify the outcome of the variable and also suspected correlates of this variable.
Those correlates must be available on or before the time of intervention in the situational nature of the modelling activity. In time-based modelling activity, multiple models should be created each corresponding to a different period for a set of observed variables.
While state-based data which includes demographics, relationships, and performance are vital for the educational predictive models. Event-driven data collection has a recent rise is large and complex and also needs great effort to convert the features to machine learning.
3. Classification and Regression
In statistical modelling, there are four types of data which include categorical, ordinal, interval, and ratio. Each type of data differs from the kind of relationships, and thus mathematical operations are derived from the individual elements. The ordinal variables are often categorical whereas interval and ratio are numeric. Categorical values can be binary or multivalued.
Two distinct algorithms used for this application are classification algorithms, generally used to predict categorical values; on the other hand, regression algorithms are used to predict numerical values.
4. Feature Selection
To build a predictive model, predicting the correlating features with values, must be created. More information should be collected rather than less because it is difficult to add data later. But removing in case of additional data is quite easier.
There would be a single feature that would be an ultimate choice that correlated with the chosen outcome prediction. The learning algorithms will make use of the available attributes whether informative or not to predict whereas some algorithms use variable selection to eliminate the uninformative attributes.
Based on the algorithms used for the predictive model, to examine the correlation features which either remove correlated attributes or apply a transformation to the features which in turn are used to eliminate the correlation.
Methods of building predictive models
A predictive model is built once collecting the dataset and performing attribute selection from the historical data. The predictive model intends to make a prediction of some unknown attribute using the known information. Some of the common algorithms for building the predictive model are as follows:
1. Linear Regression:
From the linear combination of attributes, it predicts the continuous numeric output.
2. Logistic Regression:
Follows categorical predictions, odds of two or more outcomes.
3. Nearest Neighbours Classifiers:
To determine the appropriate predicted labels for new data use only the closest labelled data in the training dataset.
4. Decision Trees:
Repeats the partitions of the data based on the series of single attribute tests. Each test maximizes the purity of classifications in each of the partitions.
Evaluating the model
To assess the quality of a predictive model, a test data set with known labels is predicted by comparing the test set with known true labels of the test set.
Also read: Data Science In Education: Socio-Emotional Learning (tooli.qa)
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