In this exploration, we thoroughly analysed all the articles selected in a Microsoft spreadsheet having column name Title, Publication, Year, Idea, Database. All the articles are arranged in a particular manner to analyse and figure out the relevant content. The distinctive merger of keywords such as Smart Learning Environment, Learning Analytics, Programming instruction etc., is used to conduct the research.
The most relevant analyses for the literature review (A Meta-Analysis of Empirical Research Results from 2009 to 2015) was searched and downloaded from Google Scholar by utilizing the keyword string of Learning Analytics and Smart Learning environment as the specific keywords in Google Scholar and the in the wake of going through its theoretical framework. Subsequently, it was noted down in the excel sheet by saving its Title, Year of publication, primary thought introduced in it, and the database from which it was downloaded, Google Scholar. Thus, it became simpler to evaluate a diverse range of articles in a similar manner. If needed to go through any critical articles, I would examine the excel document to learn about the article name and the relevant information for evaluating the article, which could be utilized in the thesis. Various filters could be applied in the excel files or records to sift through the article from the excel.
Figure 11. Summary of the systematic review execution process
4 Results
Various articles were analysed and reviewed to find an appropriate answer to the research questions.
4.1 Types of data collected in Smart Learning Environment for Programming Education
In this section, the kind of data collected for programming education suitable for learning analytics techniques is unravelled.
Different kinds of data help form extensive datasets that would further aid in modelling the data to answer the research questions in consideration. The types of data collected range from user actions, their activity ad engagement with the learning platform and the scope of information provided by the data. Information gathered from Smart Learning Content can be separated generally in the following ways:
Information gathered from Smart Learning Content can be separated generally in the following ways:
Compose data: Compose data is a richer level of data that provides a big picture of the available data. It tells information about the data that occurs during creation. It is all about actions and describes all the things that stakeholders do while using the software. Such type of data is collected for automation and decision making. The main advantages of using compose data are:
1. Higher fidelity: As stated earlier, this data provides a big picture of the data in use based on the actions of all stake users. This gives more reliability to this data in decision making. Also, since the owner owns the composed data, they can use it more confidence.
2. More flexibility: This is raw data captured, giving users the power to perform any type of analytics.
This data extracted consists of various activities like clipboard operations, mouse click, keystroke and working the graphical widget. It also stores information about click history, such as navigation history of students like online programming courses (login/logout), duration of the session, time spent on a specific programming discussion, number of tutorial videos watched, etc. Again, it additionally collects the date and timing of login.
Compile-Time data: It stores activities such as coding error, compilation attempts, issue started, endeavoured the case, answer finished and submitted, feedback, specific tests, course task scored and so forth. It likewise gathers data about which programming course was chosen the maximum times. It additionally collects information about action logs and so on. It can gauge the time stamps of the programming tasks.
Run-Time Data: This type of data can be used to generate run-time, logs, and exceptions depending on the student's activity. The run time data is collected from Smart Learning IDEs. In addition, this type of data contains primary data with Smart Learning Environment, e.g., time required with the Smart Learning, average execution assessment (test and number of resolved issues and problems).
Additional data: Additional data about students include name, area, training level, email id, and other significant accessible demographics. It also contains data about the enrolment status, and programming languages choose for study purposes and programming languages contemplated.
Social Data: Social data extracted for programming education can likewise include messages, remarks, and comments for a given code. Social data can also be used for recommending courses in the future and even career paths to consider based on previous knowledge. It also collects information about various forums and discussions, replies by students and tutors regarding syntax or code reviews and so forth. Tutors can also collect data regarding the duration of the session, which will eventually help measure the level of student’s engagement on a particular page.
Testing Data: Testing data is about test cases, area, training level, email id, and other significant accessible demographics. It also contains data about the enrolment status, and programming languages choose for study purposes and programming languages contemplated.
Augmented data: Augmented data can be instrumental in improving learning analytics, mining information, and comprehending the patterns of the students and video content.
Biological data: Health data is collected from Smart Learning Contents. Instructors can manage it, students by learning several steps, running distance, sleep time etc.
All types of data can be helpful for the engineers or developers to build up the Smart Learning Environment. Different valuable portfolios regarding the progress of the students and assessment of their presentations. It would be of great use for both students and their instructors.
Furthermore, we have extracted data and prepared a table for a better understanding of the exposition. The table includes data category, technique, platform type, etc.,
On the other hand, for the exploration of the social data, educational data mining, qualitative and quantitative feedback, collaboration metrics, students learning behaviour pattern, visual environment data, assessment metrics and several other techniques has been used to analyse the category of novel taxonomy, mine craft, E-Textbook System, game-based learning environment, technology-enhanced learning and so on.
Table 3. Comparison of IDEs used in Programming Education
Data Category Type of data
Eclipse Hackystat Netbeans Visual Studio Marmoset IntelliJ IDEA Blackbox Terms Web cat BlueJ
Compose data Text ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔
Data collection services have been increasingly combined into Eclipse, BlueJ [26] and Hackystat [27]. Eclipse can be utilised for collection of testing data on student testing exercises & execution. In addition, eclipse contains computerised testing tools such as Web-CAT [28].
They function with the Eclipse IDE: the DevEventTracker plugin with WebCat [29], the Marmoset plugin
[8]
and the open-source HackyStat [27]. Through Web-API Eclipse, collect data and analytical framework. HackyStat and Marmoset can plugin into Visual Studio [30] and Netbeans [31].IntelliJ handles smart code completion, which is an integral part of the application. IntelliJ also collects MP3 files and Java database files by the GUI toolbar
[32]
. Blackboxobserves and records the programming behaviour of students or new learners and collect programming activity data [33].
Blackbox collects data from worldwide users of the BlueJ IDE and established computing education research techniques [46]-[49]. The data is compiled into a MySQL database on a single machine. Blackbox allows researchers to develop unique ID numbers, which contain a link, text data and demographic data about participants [34].