Learning Analytics is a field combining techniques from several other fields. Among them are Academic Analytics, Educational Data Mining, Social Network Analysis and Business Intelligence [78]. One good definition of Learning Analytics is used by Siemens and Baker [182] and comes from the international Learning Analytics and Knowledge Conference in 2011: ”Learning analytics is the measurement, collecting, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.” As Ferguson [85] points out, this definition is usually extended by assumptions like “learning analytics have to be able to use machine-readable and preexisting data and further possesses the techniques to handle numerous datasets”, because the analysis of ’big data’ cannot be handled manually. But several other definitions can be found in the literature that only differ slightly from the one above. Agudo-Peregrina et al. [23] stress the fact that the data for analyzing purposes should be extracted from a virtual learning environment. However, a tool should be usable in more than one virtual learning environment. Other definitions stress the fact that the evaluation of the data should support the decision making and provide an insight into the learning and teaching methods [189]. Therefore, all of the stakeholders should benefit from learning analytics: Students should be able to evaluate their own strengths and weaknesses more accurately, and teachers should gain a better insight into the state of their class [124].
Dyckhoff et al. [79] summarized the goal of learning analytics as tracking the students’ activities and interactions with the system. Tools have to be used to collect the wanted data from several systems and offer educators and students information about these. The important learning aspects should be highlighted and the overview should enable a comparison between different groups of students. Educators can then discover patterns in their students’ behaviors and should identify possible capability gaps. They should be enabled to validate their used teaching material on its usefulness for students and adjust it accordingly. Students should be able to compare themselves with classmates. This should motivate them and may adjust their learning behaviors to become better learners [79].
Ferguson [85] splits the field into three parts:
• Educational Data Mining to observe the technical challenges, • Learning Analytics to observe the educational challenges,
• Academic Analytics to observe the political and economical challenges.
But the borders of these parts are smooth. Educational Data Mining focuses on the perceptions that can be automated by a system [182], whereas Learning analytics tries to leverage the human judgement and empower the instructors and learners. In contrast, Academic Analytics collects the data for educational administrators to benchmark and improve the effectiveness of educational institutions [86].
Chapter 2. Related Work
For implementing a learning analytics system, various frameworks have been developed to consider the environment and the necessary parameters. Greller and Drachsler [99] mention that learning analytics has many critical areas like the ownership of data and the ethical usage. Greller and Drachsler [99] and Drachsler and Greller [75] identified six critical dimensions which had to be considered when performing a learning analytics project. These dimensions were Stakeholders, Objectives, Data, Instruments, External Constraints and Internal Limitations. After the environmental parameters were clari- fied, Clow [57] presented a learning analytics cycle, which describes the successful process of learning analytics. The four steps of the cycle are learners which generate data. This is then analyzed with metrics and could lead to interventions by the lecturer. The learner then creates new data, and the circle is repeated.
Within learning analytics, one of the rising topics focuses on the mobile aspect. Mo- bile technologies are increasing rapidly and therefore carry the field of mobile learning analytics with them. Mobile learning analytics focuses on learning with and through mobile technology [191]. It focuses on data created by mobile learners on their smart de- vices [26]. Therefore the mobility of the students and its impact on the learning process is regarded separately [90]. This also considers the social aspects of today’s mobility, which is influenced by Facebook and Twitter and has a impact on the students’ learning behavior.
Researchers like Macfadyen and Dawson [137] showed a significant correlation between engagement tools and learning outcomes in online courses. A high participation in forum discussions can be correlated to the course’s final grade. With these kinds of knowledge, lecturers can engage their students to participate more actively in the classroom. But few of the tools used today correlate the teachers’ data to the student data [79]. There- fore, the knowledge of the lecturer’s teaching styles is not as large as it could be. But Dyckhoff et al. [79] also point out that the presence of a tool which is able to provide that information, does not necessarily have an impact on the students’ or the teachers’ performance.
Data Models
Learning analytics applications usually collect data from different sources and in different formats. To be able to analyze and compare them, thy have to be stored in a database model which is valid for several types of learning data. Therefore, a remarkable part of the learning analytics community is engaged in developing and evaluating suitable data models.
Lukarov et al. [136] categorized the data models into four categories with six concrete examples. Their models were focused on analyzing motion data created by logging the students’ behavior within a system. These were: Event Centric, Activity Centric, Object Centric and User Centric. They identified Contextualized Attention Metadata (CAM) [160] and IMS Caliper from IMS Global as Event Centric models. They focused on the monitoring of user interactions with learning environments. The activity centered Activity Stream [160] is quite similar but spares to consider the individual sessions. It only regards the activity of a user with a learning object. Object-centric approaches like NSDL Paradata [160] only collect aggregated data about resources.
In contrast to the motion data models described by Lukarov et al. [136], Veeramachaneni and Dernoncourt [196] tried to develop a data schema to store a huge variety of different
Chapter 2. Related Work
data used in learning applications. They presented data standards which were especially suitable for MOOCs. They use a generic database schema to store many different types of learning content in a small number of database tables. The benefits are, that analyses can compare different types of learning content more easily and new types can be added without changing the database structure.
Fortenbacher et al. [89] builds upon this generic model but tried to further simplify it and specialized it to their learning management systems and learning analytics application. The previous application LEMO [83] was successfully transformed to the new generic data model, so that the connection to further learning management systems became possible.
In consideration of this simplified, generic data model, Sch¨on et al. [10] also developed a
small, generic model to collect the information of the learning activities at the University of Mannheim. The model was similar to the object-centric models of Lukarov et al. [136] and Fortenbacher et al. [89], but the focus was set on the analyses of the content of learning objects, especially student-generated quiz answers.
Learning Analytics Tools
During recent years, several tools have been developed to collect the necessary informa- tion and process it in a way that allows analyses of of teaching and learning processes. One very large and student-focused project was Signals at the University of Purdue. Arnold and Pistilli [27] analyzed the data of more than 11,000 students and applied statistical techniques to predict the students with a high probability to drop behind. The tool provided some intelligence by guiding its users to appropriate recourses and explaining their usage to them. For this purpose, Signals collected data from several sources and transformed them into compatible formats. Each student got a personal level of risk value which could also be used by the lecturers to schedule an intervention in the form of an e-mail or a personal meeting.
Another, more exploratory approach was eLAT (exploratory Learning Analytics Toolkit)
by Dyckhoff et al. [78]. It allows lecturers to explore and correlate learning object
usage, user properties, user behavior, and assessment results with the help of graphical indicators. One of its goals was to provide real-time processing of the necessary data within milliseconds to allow individual data analyses for lecturers. They should be able to self-reflect their technology-enhanced teaching and learning scenarios. eLAT had a connection to four Virtual Learning Environments, but it offered a fixed set of analyses and would have to be extended for adding further ones.
In contrast to the individual feedback, the Predictive Analytics Reporting Framework (PAR) was developed for large-scale analyses in higher education by the Western Inter- state commission [202]. PAR is a data-mining project which uses analyses of large scale federal data sets to support the understanding of student dynamics and in particular drop outs. To identify the main drivers that are related to the students’ progression, they aggregated data of more than 1,700,000 anonymized records. A further focus of the project was the search of parameters which influence the tendency of online students to stay enrolled. Their results showed that the analyses provided a useful benchmark to explore student drop outs and dynamics at different course levels. As a conclusion, they were able to show that the courses which were attended at the beginning of the college career had a higher influence on the students’ dis-enrollment probability.
Chapter 2. Related Work
In the mobile sector, Fulantelli et al. [90] presented MeLOD (Mobile Environment for Learning with Linked Open Data). It provided geo-located learning materials which should support the students’ informal learning experiences while visiting interesting places. They therefore got additional information on the current location and were able to vote and comment them. This information was then fed into the database together with the social learning activities of the students.
In conclusion we have now presented a view of related research to ARSs and interrelated fields from the early beginnings of e-learning activities to the modern times of smart- phones and iPads. Building upon this knowledge, the following chapters will introduce two new models and their implementations. The first model will describe a simple but polished ARS which has the benefits of the ARSs mentioned above, without most of their drawbacks. The second model will build upon that and will describe a far more complex application which is able to support a large variety of different and customizable teaching scenarios, not limited to plain knowledge quizzes.