# Analyzing Student Behavioral Patterns in MOOCs Using Hidden Markov Models in Distance Education
## 5 Conclusions  
This paper examines and interprets clickstream log files of a MOOC dataset with a 2-layer HMM. The student behavior is represented as a sequence of hidden states, and grades are treated as visible states. Cross-validation is not applied because 2 layers exist, and there are not many independent sequences. Additionally, there is no definition of the statistical significance of the variables. Instead of this, the variables' natural impact on the states is taken into account. The MOOCs in our model consist of 2 layers in distance educational problems, which is a natural fit. In other models, like speech recognition, many sequences exist. Moreover, the data are not segmented because it is clear that very few students achieve Grades 4 to 6. The transitions and the corresponding grades are considered rather than considering subsets of students. We find a unique pattern generated by the Viterbi algorithm. No prediction is performed. This methodology is easily applied to every dataset that pertains to MOOCs. The uniqueness of our dataset ensures that if multiple datasets were used, the occurrence of randomized IDs and behaviors would lead to inconsistent conclusions. We find the most likely sequence by applying the Viterbi algorithm. The Baum-Welch Algorithm is also employed, which is an expectation-maximization algorithm, to estimate HMM parameters. Moreover, the Forward-Backward algorithm is employed, which computes probabilities of states and observations for parameter estimation. This methodology is applicable to both large and small datasets. In the future, we plan to investigate the effects of video lectures and the use of forums that follow the lectures. To do this, association rules will also employ the Apriori algorithm to obtain a correlation among students who participate in the lectures and subsequently engage in the forums. The impact of this communication on the learning outcomes will be explored, specifically the final grades, by analyzing the clickstreams in MOOCs with 2-layer HMMs. Additionally, we aim to examine the influence of various resources on learning outcomes and student behavior throughout the course. Clustering may be applied to the students according to their interests during the interaction with the resources of the MOOC and according to transition behaviors. This will be obtained with kNN by defining a function after training the data and by identifying the most frequently represented transitions. The membership of each student to behavior transitions defined as clusters will be performed with fuzzy k-means clustering. The at-risk students will be predicted according to their participation in transition behaviors. The revelation of the most predictive behavior patterns will be identified by 2-layer HMMs. The prediction pertains to student performance. To this aim, the aforementioned clustering will be applied.  
Conceptualization, N.S.A. and V.S.V.; methodology, N.S.A.; software, N.S.A.; validation, N.S.A. and V.S.V.; formal analysis, N.S.A. and V.S.V.; investigation, N.S.A.; resources, N.S.A.; data curation, N.S.A.; writing--original draft preparation, N.S.A., V.S.V. and E.P.; writing--review and editing, N.S.A., V.S.V., E.P. and G.F.; visualization, N.S.A.; supervision, V.S.V. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The authors declare no conflicts of interest.