The SMARTLEARN research group envisions the Learning Engineering (eLearning) domain from a interdisciplinary perspective.
The ultimate goal is to meet the demanding and changing requirements of the next generation of online education systems and services.
Our methodology tackles the full picture of the Learning Engineering domain by integrating:
- pedagogical conceptualization of online education systems and services
- technological and engineering methodologies
- developments to be prototyped and integrated in real LMS
- dissemination of knowledge
- exploitation of the technological outcomes in academia and industry
SMARTLEARN RESEARCH LINES (7)
- ICT education through formative assessment, Learning Analytics and Gamification
The ICT degrees include very practical competencies, which can only be acquired by means of experience, performing exercises, designs, projects, … In addition to the challenge of motivating students to solve activities, lecturers face the problem to assess and provide suitable feedback to each submission. Receiving immediate and continuous feedback can facilitate the acquisition of the competencies, although this requires support in the form of automatic tools. The automation of the assessment process may be simple in some activities (e.g., practical activities on programming) but it may be complex in activities about design or modeling. Monitoring the use of these tools can reveal very valuable information for the tracking, management and continuous improvement of the course by the teaching team. However, in order to leverage all its potential, this information should be complemented with data from other sources (e.g., the student’s academic file) and historical information of previous editions.
The main goal of this research line is to design and build a set of e-Learning tools and services to provide support to the learning process in university degrees in the field of ICT (Information and Communication Technologies). The expected benefits will have a repercussion on the students (improvement of the educational experience, greater participation and performance, lower drop-out rate) and on the lecturers, managers and academic coordinators (resources for monitoring a course, making decisions and predictions).
Taking into account these elements, the contributions will focus on three axes:
– Tools for formative assessment, which can provide immediate feedback by means of automatic assessment. In particular, the research activity will focus on knowledge areas with high cognitive or modeling levels, such as the design or modeling of software and hardware.
– Learning analytics that monitor the activity and the progress of the student about the use of the mentioned tools and allow for analyzing the learning results, identifying the critical points and defining actions of improvement. These analytics will also incorporate other sources of academic and historical information to facilitate the course tracking and decision making processes to the teaching team.
– Gamification, as an incentive scheme in order to motivate students to perform new activities and increase their engagement without sacrificing the academic rigor.
A relevant aspect to be considered by e-learning tools developed in this research line is the modularity and independence from technologies or particular virtual campuses, with the aim to facilitate its application to different courses and contexts. To this end, the functionalities of these tools will be offered as a set of services, using appropriate standards. The tools will be evaluated in courses of mathematics, computing engineering and telecommunication and it is expected that their use becomes feasible as part of both self-taught education (life-long learning) and traditional formal education as well as massive courses of on-line learning (MOOCs).
- Multi-modal emotion-awareness e-learning tools
Emotions and affective factors, such as confusion, frustration, shame and pride, are acknowledged as major influences in education in LMSs (Learning management systems). However, despite major advancements in fields such as artificial intelligence, human-computer interaction, and sensorial technologies, e-learning environments are still struggling with incorporating emotional-aware tools. The limited-to-null adoption of emotional analysis tools and affective feedback prevents both learners and teachers from reaping the benefits of emotion-aware LMSs.
This research line aims at enhancing existing e-learning platforms by developing tools and services which support the detection and representation of learners’ emotions, as well as emotion-based learning adaptation and affective feedback. To this end, the research will apply novel emotion detection models to rich multimodal data collected using state of the art channels, advanced sensors and novel adaptive interfaces. Moreover, via multiple small-scale pilots in formal, informal and workplace learning environments, the research will intend to demonstrate a positive impact of emotion-aware e-learning on decreasing learners drop-out rates, increasing satisfaction and improving learning performance, thus making learning as a whole a better experience.
The ultimate goal of the research conducted here is to understand the underlying mechanisms of socio-affective processes as well as how best to build multi-modal emotional-awareness e-Learning tools that are adaptive not only to learners’ cognitive performances but also to their affective states and social interactions with peers and teachers. This goal is thus two-fold:
- Embed non-intrusive, module-based emotional awareness tools into LMSs that allow for socio-affective learning and assessment of individuals and groups in different environments: formal (university, primary/secondary school, and special education), informal (open education e-learning for adults), and the workplace;
- Validate and measure improvements in knowledge gain, drop-out rate, learning analytics capacity, and affective profiling as measured by changes in socio-cognitive performance, motivation, collaborative and social interactions, together with the cost-effectiveness of the platform, including the rate of adoption of these technologies for the modernisation of education and training, and validating also gender differences
- Conversational Agents and Learning Analytics for MOOCs
Higher Education Massive Open Online Courses (MOOCs) introduce a way of transcending formal higher education by realizing technology-enhanced formats of learning and instruction and by granting access to an audience way beyond students enrolled in any one Higher Education Institution. However, although MOOCs have been reported as an efficient and important educational tool, there is a number of issues and problems related to the educational aspect. More specifically, there is an important number of drop outs during a course, little participation, and lack of students’ motivation and engagement overall. This may be due to one-size-fits-all instructional approaches and very limited commitment to student-student and teacher-student collaboration.
This research line aims to enhance the MOOCs experience by integrating:
- Collaborative settings based on Conversational Agents (CA) both in synchronous and asynchronous collaboration conditions
- Screening methods based on Learning Analytics (LA) to support both students and teachers during a MOOC course
CA guide and support student dialogue using natural language both in individual and collaborative settings. Moreover, LA techniques can support teachers’ orchestration and students’ learning during MOOCs by evaluating students’ interaction and participation. Integrating CA and LA into MOOCs can both trigger peer interaction in discussion groups and considerably increase the engagement and the commitment of online students (and, consequently, reduce MOOCs dropout rate). Pilots will be mainly run at the virtual campus of the Universitat Oberta de Catalunya and Miriadax MOOC platform, among other potential real learning contexts.
- Cloud, Cluster and Distributed computing for eLearning
This research line will leverage intensive computational capabilities of Cloud, Cluster and Distributed computing for eLearning in order to integrate adaptive and personalised approaches capable of identifying learners’ requirements (using Artificial Intelligence and data mining techniques), building users models based on navigation patterns in virtual campus, intelligently monitoring progress to purposeful and meaningful advice both learners and teachers, among others. In particular:
Cloud computing technologies are more and more popular in eLearning, most computing platforms and standalone eLearning applications are being deployed in Cloud platforms and offered as a service (SaaS) with many benefits. For instance, by porting eLearning applications to Cloud, it is possible to offer on-line learning as a Cloud service, which would alleviate the final user from the burden of installing and configuring at local computer or local networking infrastructure. Moreover, porting to Cloud allows for tackling mining of very large data sets, i.e. Big Data for eLearning.
User modeling in eLerning implies a constant processing and analysis of user interaction data during long-term learning activities, which produces huge amounts of valuable data stored typically in server log files. Due to the large or very large size of log files generated daily in Virtual Campuses, the massive processing is a foremost step in extracting useful information. Cluster computing is commonly used for this purpose using different distributed frameworks and technologies, such as Hadoop, Map Reduce, Pig and Spark.
Non-functional requirements in eLearning systems, such as maintenance cost, scalability and fault-tolerance are important aspects to consider. Distributed technologies, such as P2P are an important alternative to develop decentralized online learning systems in which students can be more than mere clients and can use their own computational resources for task accomplishment during online learning process.
This research line will implement and evaluate the eLearning approaches using the above computing paradigms in order to explore the real complexities and challenges, such as time performance of massive processing of daily log files implemented following the master-slave paradigm and the actual time efficiency of porting some Data Miming frameworks to the Cloud for mining Big Data for eLearning.
- Information models for enhancing security in eLearning
This research line aims at incorporating information security properties and services into on-line e-Learning. The main goal is to design innovative security solutions, based on methodical approaches, to provide e-Learning designers and managers with guidelines for incorporating security into on-line learning. These guidelines include all processes involved in e-Learning design and management such as security analysis, learning activities design, detection of anomalous actions, trustworthiness data processing, and so on.
This research is to be conducted by multidisciplinary perspective; the most significant are e-Learning and on-line collaborative learning, information security, learning management systems, and trustworthiness assessment and prediction models. In this scope, the problem of ensure collaborative on-line learning activities will be tackled by a hybrid model based on functional and technological solutions, such as, trustworthiness modeling and information security technologies.
- Integrating Business Intelligence and Learning Analytics Systems to create Global Analytical Information Systems for Universities
The goal of creating analytic information systems in order to make companies more competitive was stated in the sixties under the name of “decision support systems”. In the nineties these systems experienced a rebirth under the name of Business Intelligence (BI). From then on, they have been a key element in the enterprises success. However, when applied to universities, BI systems have been less successful since they do not cover the main activities of universities (mainly teaching and research).
In order to cover this lack, several approaches, focused to perform analytics in the educational context, have appeared recently, most of them under the umbrella of Learning Analytics. These new systems tend to be very focused and not sponsored by university leaders, as contraposition to BI systems. That leads to some problems when generalizing or adopting the created systems at institutional level. Therefore, there are two analytical approaches in universities: one focused to management and with institutional support and the other focused to the university activities without institutional support.
This research line considers and joins both approaches to create institutional and transversal Analytical Information Systems for Universities. The purpose is to state that idea, demonstrating, that developing analytical information systems in universities is a grand challenge for information systems research, showing the benefits of doing so by integrating both approaches and developing analytical information systems for universities that uses that global approach.
- Ethics and Artificial Intelligence in Online Learning
The new disciplinary approach of learning engineering as the merge of breakthrough educational methodologies and technologies based on internet, data science and artificial intelligence (AI) have completely changed the landscape of online education over the last years by creating accessible, reliable and affordable data-rich powerful learning environments. Particularly, AI-driven technologies have managed to automate pedagogical behaviours that we would deem as “intelligent” within an online education setting.
However, as reported in more mature sectors where AI-driven technologies have already been developed and deployed, automatic decision-making processes many times bear unexpected outcomes. For instance, systems based on machine learning (ML) technologies have been reported to discriminate certain social communities in the context of law courts, job applications or bank loans due to the use of biased datasets to feed the ML models. This has led to unfair and biased automated decisions that have been detrimental to our society, harmful to certain social groups, and contrary to the way we want to shape our future. In order to avoid unforeseen outcomes in their integration, the ethical dimension of deploying AI in different settings must be taken into account. This is particularly relevant when considering those sectors that are meant to provide universal services to our society, such as public healthcare and education, and where the arbitrary deployment of AI systems in these key sectors will neither guarantee the expected inclusivity and fairness, nor the support to professionals providing these services while focusing on value-added tasks, eventually constraining the access to such services.
In order to face those challenges, this research line explores the insights and experiences gained in the fields of AI ethics (AIE) and artificial morality (AM) to respectively consider the effects of integrating AI in education, as well as to explore the design of artificial pedagogical agents that can actively take ethical considerations into account when making teaching decisions. The novel and effective application of AIE and AM to AI-driven online learning environments aims at guaranteeing inclusive education whilst ensuring fair and equal opportunities to all students.