This study presented in the paper aims at analyzing reading pattern and behavior analysis of the students in classroom and digital learning environment from the reading behavior log data activities analyzed on the dataset provided for the year 2019 and 2020 courses. The analysis is able to detect reading behavior changes in student reading pattern from the onsite classes in 2019 to the online classes in 2020 in the same education institution based on the overall operation counts, aggregate page transition analysis and time aggregation analysis for the operations and pages ready by the students across two courses A and B for the year before and the year after the pandemic of 2019 and 2020 respectively. The shift to the online learning system and its impact on the student learning is based on various features. The number of page navigation operation counts, page transitions, time range aggregations for page access done by the student for a specific time interval are useful factors for assessing his or her reading pattern. This paper emphasizes on the importance of relevant features in analyzing a student's reading pattern behavior with page transition and aggregation time intervals of students in the offline and online reading activities. Learning Analytics intervention and its integration in the students reading pattern analysis is very important for giving feedback in the way of learner's reading and learning journey. This paper would possibly contribute to the reading learning behavior pattern interpretation of the learner in the world of digital learning which holds the future of smart education. The results are preliminary in nature and with further depth analysis will lead to automated student reading behavior pattern prediction systems using the machine learning techniques. The paper has a reasonable insight into the reading strategies of students during the two different years of onsite 2019 classroom reading activities and 2020 digital reading activities respectively. The results of this study indicate that reading time of a user in digital reading activities is more as with the onsite reading activities whereas the navigation operation counts are more with onsite reading as compared to the digital reading. It will motivate the researchers for further study and analysis for predicting the various student related reading behaviors using Learning Analytics and Machine Learning.
Reading is an ongoing educational activity that humans embrace since their childhood. It is a
constant mechanism for all the stages of human cognitive development from childhood till old age. The
Education system has moved from traditional classroom-based systems to the new online based
learning. The flipped based learning combines the best of both the worlds. The rise and need for digital
education and learning seems to flourish in the coming years of human existence. With the advent of
educational technologies and smart education there has been a rising need for assessing the impact of
the technologies on student learning. Intelligent textbooks and reading devices have a plethora of data
for analyzing the student’s reading behavior and pattern. The impact of digital education on students
reading is a matter of concern for all student age groups. The benefits of digital learning are numerous
as compared to the in classroom reading but an empirical study is needed to carry out the seed indicators
in order for supporting the advantages [
The analysis of the reading behaviors of the learners have been studied in the past educational
theories and with the advent of educational technologies have laid a fertile ground to validate their
results for a useful scientific study. The results of the study would be helpful in understanding the
reading difficulties faced by the students with their present reading behavior patterns and would also
help in designing the personalized material by the teachers based on the results and feedback received.
Reading behavior analysis holds significant importance in predicting the students’ performance.
Various researches have been done in the reading behavior analysis of the student such by analyzing
learners reading logs and based on the clickstream data various reading profiles have been calculated
as demonstrated by Majumdar et al [
The Reading time of the students in the pandemic has more shifted towards digital learning. There
comes a need to access the impact of the digital book reading on the normal reading behavior of the
student and at the same time measure its change with the traditional reading systems of the classroom.
The change in variation can be seen more importantly from the variation in the reading time over a
range of years of the student reading activities. This section discussed a preliminary analysis of the
students reading pattern with the study of the reading pattern of the student. The page transition of
students with time is being analyzed and studied for the courses during the year 2019 and 2020 as
provided in the dataset [
The change in the reading behavior of the learners can be seen from the operation counts, page transition and page aggregation functions as shown in Figure1-4. The Figure 1 below gives the average operation count of all the users in all the contents across both the courses of 2019 and 2020. The X-axis label defines the operation count with values open, close, add marker, add memo and add bookmark. The Yaxis represents the content id and depends on the number of contents in the particular course and year. It can be clearly observed that the number of open and close navigation operations are higher in the Year of 2020 for online classes as compared to Year 2019 for onsite classes. The A- 2019 course open and close navigation operations are similar to A-2020 and B-2020 courses and differ in the add marker operation. The teacher and the contents uploaded for the particular course can be a reason for the similar open and close operations counts. Also, it can be seen that the add bookmark has the highest count in the B-2019 course as compared to the other courses. Therefore, it can be inferred that the reading pattern behavior difference of the users in for the A-2020 and A-2019 courses in terms of open operation and varies very significantly for the close operation as the students may have the same open navigation pattern across the two years. Also add bookmark operation is present in the A-2020 course whereas add marker operation is present in the A-2019 course. Similarly for the B-2020 course there can be seen a significant difference between the number of operations counts for the open and close operation which is highest in B-2020 course as compared to the add marker operation highest for B-2019 course followed by all other bookmarks.
From the Figure 2 below the average reading time using the page-wise aggregation module. The average operation count of all the users in each page are plotted with page number on the X-axis, average score of the operation count on the Y-axis and reading minutes in the page for all the users accessing a particular content which is around the same lecture time across all the lecture weeks of 1-8 in 2019 and 2020 courses. As can be seen from the plots the average reading time in the 2019 courses are higher as compared to the 2020 courses. Also, the number of close operations is higher in A-2020 as in A-2019 courses going along with the reading minutes in the page as students might be reading quickly and closing the page to move on to the next page. The number of open operation count is higher in A-2020 course. Also add marker and add bookmark operation has been used by the more by the students of the 2019 courses as compared to the 2020 courses. Thus, it can be inferred that the onsite classroom reading activities in terms of the operations counts for a particular week content is read more often by the 2019 courses as compared to the 2020 courses. From the Figure 2 graph it has been observed that there are a smaller number of navigation operation activity in 2020 courses which can be a probable measure of good flow and reading concentration in the online reading as compared to the onsite reading activity.
X-axis = page Y-axis = Operation count
As can be seen from the Figure 3 and with the help of the time range aggregation module of the Open LA library by calculating the average operation count of all the users in a particular lecture which is taken across the same week for all the two courses of the two years, the next and previous operation counts are higher in the initial minutes of 0-50 for A-2020 as compared to 50 to 100 for A-2019 and higher in later minutes of 50 to 90 for B-2020 courses as compared to 70 to 90 for B-2019 courses. Therefore, it can be inferred that the students are initially active during the initial time periods in digital reading whereas in off line reading the students become active in the middle of the reading intervals. Also, as can be seen from the Figure 4 graphs, the pages the user read in each time for a particular lecture is higher in the 50–150-minute time interval for A-2020 and A-2019 courses, 70 -90 B-2020 and 50 to 90 for B-2019 courses. It can be inferred that the users read more pages in the mid time interval after gaining momentum in the initial elapsed minutes.
The variation in the student reading patterns has been observed through the data aggregation module methods of the Open LA library which is described in detailed in the further subsections. Although more data analysis and mathematical derivations are needed to infer the claims, it can be partially inferred that the reading pattern between the classroom and digital learning activities varies significantly in terms of the number of pages viewed, average reading time, navigation pattern of operation counts, page wise aggregation and time range aggregation of the reading activities. A further work using machine learning, feature engineering and prediction techniques will be able to provide us with a precise impact of the above on the students’ performance based on reading behavior.
This section presents the methods used for the dataset analysis. The basic methods of the Open LA library have been used in carrying out all the processing related to the operation counts, page wise transition aggregation and time-range aggregation of the data modules of the library. The Table 1 and Table 2 below describe the dataset files and the features used which is same for all the dataset files of Course A and Course B for 2019 and 2020 years. The comparison thus holds valid as the same variables are being compared with around the same week contents for all the obtained results.
Table 1 Dataset Files Types
(.csv files) (Online/Onsite
EventStream 2020 / 2019
With the increase in number of navigations for the 2019 courses it can be inferred that the users have high navigation pattern in the on-site classes as compared to the online classes. The smaller number of navigations in 2020 courses can probably amount to the increase concentration and flow with less distractions in online reading activity.
O, C,AM
The reading minutes of the same user in the middle of the course around week 4 can talk about the motivation of the student for continuing reading. For 2020 courses the reading minutes across a particular content in the same lecture week are higher as compared to th2 2019 courses. With A2020_C6, A-2020_C7, B-2020_C6, B-2020_C7 having higher average reading minutes as 54.2, 0.78, 4.05 and 3.13 respectively whereas with A-2019_C3, B-2019_C3, B-2019_C4 courses the reading minutes are 3.433, 0.366 and 25.566 respectively. Therefore, it can be inferred that the reading minutes of the user are higher in 2020 as compared with 2019 courses.
As discussed in the previous sections the reading behavior analysis done is discussed here. The change in the reading behavior of the learners can be seen from the operation counts, page transition and page aggregation functions as shown in Figure1-4. It can be clearly observed that the number of open and close navigation operations are higher in the Year of 2020 for online classes as compared to Year 2019 for onsite classes. Similarly, the average reading minute per page is higher in 2020. The average operation count of all the users and the pages the user read in each time in Lecture 1 are most accessed for the online reading. Also, as seen from the results in Table 3 the number of reading hours, minutes and seconds are higher for courses taken in 2020. The user navigation with the operations counts are more for the open, add bookmark, next and previous operations that clearly signifies increase in navigation activities by the learners in the online reading systems. To further analyze the importance of the study for educational reading-based application an initial machine learning classification has been done for the 2020 Year page transition dataset processed through the OpenLA library and derived from the operations to classify the users based on their contents navigated, their average reading time, the number of access operations and page numbers. The initial results and accuracy achieved by the classifiers are described in Table 6 along with their ROC curves in Figure 6 below. The research questions that were presented for the purpose of this study can be answered with this study analysis. Firstly, for answering the Research Question 1 related to “what are the reading patterns behavior of the users for a specific lecture during the onsite class reading and comparing it to the online class readings” can be seen from the reading patterns behavior of the users based on the total operation counts, total page transitions aggregation operation counts and total time-range aggregation operation counts and pages users read in each time interval of one minute for a specific lecture during the onsite class reading and comparing it to the online class readings. Secondly, for the Research Question 2 related to “What is the reading time patterns of the users based on the during the onsite class reading as compared to the online class readings” can be seen from the reading time patterns of the users based on the average reading score during the onsite class reading as compared to the online class readings of a specific user. Lastly, for answering the Research Question 3 related to “what are the significant features that contribute to the reading pattern behavior analysis of the student” the reading minutes, average reading time and average operation count are relatively important features for reading pattern behavior analysis. For validating the above claim further machine learning based feature importance analysis using Random Forest classifiers would be required as a suitable solution.
As the log dataset files for the event stream files correspond to 13, 937 instances and also for model building for various types of classification and prediction models there arises a need for the machine learning based analysis for the same. Therefore, this paper also presents an initial work on the implementation of machine learning models for classifying the users based with their unique userid on the features such as contentsid, page number, number of page visits, average reading seconds, reading seconds and type of navigation operations such as open, close, add marker, add memo and add bookmark. This is an initial analysis work for only one course of the year 2020 using the page wise aggregation module of the data conversion module of the Open LA library. The page wise aggregation stores the page navigation behavior by calculating the total staying seconds and operation count per page of the users in relation to the contents accessed by the user. For modeling various types of machine learning algorithms were used to train the model ranging from Neural Networks, Support Vector Machines (SVM), Decision Trees to Ada Boost that were implemented using MATLAB software. The initial analysis was done using the training data which resulted in a validation accuracy of 98.0% with Neural Network classifiers with one fully connected layer of size 100, 84.1% with Fine Gaussian SVM, 81.5% with Decision Trees and 71% with Ada Boost classifiers. The Neural Network Classifier gives the highest accuracy among all the classifiers. But with the 10 Cross-Fold Validation the AdaBoost algorithm achieved the highest accuracy of 64.4% among all the classifiers. For different educational datasets classifications and prediction-based applications, a high accuracy model is the need of the hour. Thus, the future work lies in the choosing of various other models and increasing the validation accuracy as well as testing our trained model on the unseen test data. These kind of behavior-based prediction models will help in providing valuable feedback to the students in knowing where they are spending most of their time in reading and at the same time would help teachers keep track of their students reading behavior pattern to prepare schedule according to the specific user needs. The Table 6 below lists the accuracy results followed by the ROC curves of the classifiers processed in MATLAB. Table 6 Users Classifications based on the Page Transition Behavior of Users (Four Unique Users) of A-2020 course with Machine Learning Classifiers in MATLAB
98.0%
change in reading patterns and behavior of the students in the classroom and digital reading activities. The number of operation counts, page transition, page access done by a student is an important factor for assessing his or her reading pattern. This paper emphasizes on the importance of relevant features in analyzing a student’s reading pattern behavior with page transition and aggregation time intervals of students in the offline and online reading activities. The results are preliminary in nature and with the completion of the work with lead to the final results using machine learning techniques. There is a need for deriving the reading pattern scores that would be calculating the reading pattern behavior of the students which would be fed into strong machine learning classifiers for building accurate reading behavior detection models. Future work will be motivated by building and deploying of such models for real time analysis.