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Smart learning analytics (Smart LA) – i.e. the process of collecting, analyzing and interpreting data on how students learn – has great potentials to support opportunistic learning and offer better – and more personalized – learning experiences. The purpose of this paper is to provide an overview of the latest developments and features of Smart LA by reviewing relevant cases.

The paper studies several representative cases of Smart LA implementation, and highlights the key features of Smart LA. In addition, it discusses how instructors can use Smart LA to better understand the efforts their students make, and to improve learning experiences.

Ongoing research in Smart LA involves testing across various learning domains, learning sensors and LA platforms. Through the collection, analysis and visualization of learner data and performance, instructors and learners gain more accurate understandings of individual learning behavior and ways to effectively address learner needs. As a result, students can make better decisions when refining their study plans (either by themselves or in collaboration with others), and instructors obtain a convenient monitor of student progress. In summary, Smart LA promotes self-regulated and/or co-regulated learning by discovering opportunities for remediation, and by prescribing materials and pedagogy for remedial instruction.

Characteristically, Smart LA helps instructors give students effective and efficient learning experiences, by integrating the advanced learning analytics technology, fine-grained domain knowledge and locale-based information. This paper discusses notable cases illustrating the potential of Smart LA.

The implementation of the national education and lifelong education should break through the traditional learning mode in the digital era. A seamless learning environment is the intelligent form of deep integration of digital learning environment and physical learning environment. The development and construction of seamless learning space and platform is a new trend of international mobile learning research and practice. So, the purpose of this paper is to build a seamless learning platform of the open education system by expanding and improving the connotation and extension of seamless learning, based on the theories of mobile learning, ubiquitous learning and open education, combining with the characteristics of the big data era.

A seamless learning platform model will be constructed through constructing five modules including resource integration module, requirement module, management module, teacher integration module, and carrier module, using interdisciplinary research methods (combined with economics), functional analysis, model methods and exploratory research methods. Finally, this paper attempts to realize the application of a seamless learning platform in open education through the strategic path of public-private partnership (PPP).

Seamless learning platform model given by this paper can effectively eliminate the problem of information asymmetry between learner-demander and educational manager through the effective management of carrier fusion module. Furthermore, it can effectively integrate the learning resources and teachers of the open education system and social individual education system, and provide high-quality shared learning resources and diverse stratification teachers to students and social workers through PPP project cooperation ways.

There are many papers on the study of open education and resource construction. However, few papers have studied how to effectively integrate and optimize various existing scattered online resources, including various system courses, teachers and platform systems. The paper provides an effective way to solve the above sub-problems and the realization path/model reference for the effective and accurate promotion of lifelong sustainable learning for all.

This study aims to focus on providing a computerized classification testing (CCT) system that can easily be embedded as a self-assessment feature into the existing legacy environment of a higher education institution, empowering students with self-assessments to monitor their learning progress and following strict data protection regulations. The purpose of this study is to investigate the use of two different versions (without dashboard vs with dashboard) of the CCT system during the course of a semester; to examine changes in the intended use and perceived usefulness of two different versions (without dashboard vs with dashboard) of the CCT system; and to compare the self-reported confidence levels of two different versions (without dashboard vs with dashboard) of the CCT system.

A total of N = 194 students from a higher education institution in the area of economic and business education participated in the study. The participants were provided access to the CCT system as an opportunity to self-assess their domain knowledge in five areas throughout the semester. An algorithm was implemented to classify learners into master and nonmaster. A total of nine metrics were implemented for classifying the performance of learners. Instruments for collecting co-variates included the study interest questionnaire (Cronbach’s a = 0. 90), the achievement motivation inventory (Cronbach’s a = 0. 94), measures focusing on perceived usefulness and demographic data.

The findings indicate that the students used the CCT system intensively throughout the semester. Students in a cohort with a dashboard available interacted more with the CCT system than students in a cohort without a dashboard. Further, findings showed that students with a dashboard available reported significantly higher confidence levels in the CCT system than participants without a dashboard.

The design of digitally supported learning environments requires valid formative (self-)assessment data to better support the current needs of the learner. While the findings of the current study are limited concerning one study cohort and a limited number of self-assessment areas, the CCT system is being further developed for seamless integration of self-assessment and related feedback to further reveal unforeseen opportunities for future student cohorts.

The purpose of this paper is to share the lessons learned in implementing specific design patterns within the “Dr Internet” massive open online course (MOOC).

MOOCs are boasting considerable participant numbers, but also suffer from declining participant activity and low completion rates. Learning analytics results from earlier xMOOCs indicate that this might be alleviated by certain instructional design patterns – critical aspects include shorter course duration, narrative structures with suspense peaks, and a course schedule that is diversified and stimulating. To evaluate their impact on retention, the authors have tried to implement these patterns in the design of the “Dr Internet” MOOC.

Statistical results from the first run of the case study MOOC do not indicate any strong influences of these design patterns on the retention rate.

With inconclusive statistical results from this case study, more research with higher participant numbers is needed to gain insight on the effectiveness of these design patterns in MOOCs. When interpreting retention outcomes, other influencing factors (course content, pacing, timing, etc.) need to be taken into account.

This publication reports about a case study MOOC and gives practical hints for further research.

The quick growth of web-based and mobile e-learning applications such as massive open online courses have created a large volume of online learning resources. Confronting such a large amount of learning data, it is important to develop effective clustering approaches for user group modeling and intelligent tutoring. The paper aims to discuss these issues.

In this paper, a minimum spanning tree based approach is proposed for clustering of online learning resources. The novel clustering approach has two main stages, namely, elimination stage and construction stage. During the elimination stage, the Euclidean distance is adopted as a metrics formula to measure density of learning resources. Resources with quite low densities are identified as outliers and therefore removed. During the construction stage, a minimum spanning tree is built by initializing the centroids according to the degree of freedom of the resources. Online learning resources are subsequently partitioned into clusters by exploiting the structure of minimum spanning tree.

Conventional clustering algorithms have a number of shortcomings such that they cannot handle online learning resources effectively. On the one hand, extant partitional clustering methods use a randomly assigned centroid for each cluster, which usually cause the problem of ineffective clustering results. On the other hand, classical density-based clustering methods are very computationally expensive and time-consuming. Experimental results indicate that the algorithm proposed outperforms the traditional clustering algorithms for online learning resources.

The effectiveness of the proposed algorithms has been validated by using several data sets. Moreover, the proposed clustering algorithm has great potential in e-learning applications. It has been demonstrated how the novel technique can be integrated in various e-learning systems. For example, the clustering technique can classify learners into groups so that homogeneous grouping can improve the effectiveness of learning. Moreover, clustering of online learning resources is valuable to decision making in terms of tutorial strategies and instructional design for intelligent tutoring. Lastly, a number of directions for future research have been identified in the study.

Studies on academic entrepreneurship (AE) agree on the significant impact that Universities can have on entrepreneurial development. AE deploys through fundamental activities, like the start-up of new companies and the connection of the University with Enterprises. The purpose of this paper is to analyse the role of digital learning platforms (DLP) to connect Universities and Enterprises effectively. Although the literature has extensively investigated DLP, there is a lack of understanding of the role of DLP in supporting digital AE. This paper focuses, in particular, on the functional requirements that have to distinguish the development of DLPs supporting education-based activities of knowledge transfer between academia and enterprise.

The research is carried out, adopting a case study methodology. A single and holistic case regarding a DLP developed for the strategic and exclusive deployment of AE activities is proposed to describe and discuss the functional requirements of such Platform.

The DLP is a virtual learning space in which Enterprises and Universities can interact. The definition of design requirements is crucial for the efficacy of DLPs and needs to be carefully supported. Various criteria are proposed, respect to the various stakeholders engaged in DAE learning platform (Universities, Enterprises, students, employees), and according to the short- and long-term objectives of Universities and Entrepreneurship connection.

The paper explores an original case of DLP established in AE, to connect Universities and Enterprises. The research also sheds light on the under focussed typology of AE activities regarding education-based knowledge exchange. They are currently unaddressed by the literature on AE.

Recent years have witnessed the rapid development of massive open online courses (MOOCs). With more and more courses being produced by instructors and being participated by learners all over the world, unprecedented massive educational resources are aggregated. The educational resources include videos, subtitles, lecture notes, quizzes, etc., on the teaching side, and forum contents, Wiki, log of learning behavior, log of homework, etc., on the learning side. However, the data are both unstructured and diverse. To facilitate knowledge management and mining on MOOCs, extracting keywords from the resources is important. This paper aims to adapt the state-of-the-art techniques to MOOC settings and evaluate the effectiveness on real data. In terms of practice, this paper also tries to answer the questions for the first time that to what extend can the MOOC resources support keyword extraction models, and how many human efforts are required to make the models work well.

Based on which side generates the data, i.e instructors or learners, the data are classified to teaching resources and learning resources, respectively. The approach used on teaching resources is based on machine learning models with labels, while the approach used on learning resources is based on graph model without labels.

From the teaching resources, the methods used by the authors can accurately extract keywords with only 10 per cent labeled data. The authors find a characteristic of the data that the resources of various forms, e.g. subtitles and PPTs, should be separately considered because they have the different model ability. From the learning resources, the keywords extracted from MOOC forums are not as domain-specific as those extracted from teaching resources, but they can reflect the topics which are lively discussed in forums. Then instructors can get feedback from the indication. The authors implement two applications with the extracted keywords: generating concept map and generating learning path. The visual demos show they have the potential to improve learning efficiency when they are integrated into a real MOOC platform.

Conducting keyword extraction on MOOC resources is quite difficult because teaching resources are hard to be obtained due to copyrights. Also, getting labeled data is tough because usually expertise of the corresponding domain is required.

The experiment results support that MOOC resources are good enough for building models of keyword extraction, and an acceptable balance between human efforts and model accuracy can be achieved.

This paper presents a pioneer study on keyword extraction on MOOC resources and obtains some new findings.

The literature includes several studies that define different critical success factors (CSF) which have to be considered to support the implementation of technology-enhanced learning (TEL) approaches. An analysis of such studies revealed that (1) regional differences seem to determine the CSF for TEL approaches, (2) certain CSF are relevant for TEL approaches in general, and (3) professionals in higher education determine which influential factors they consider when implementing TEL approaches. Thus, the question arises: in general, which influential factors do professionals in Austrian and German institutions of higher education actually consider when implementing TEL approaches?

The study is a quantitative research approach based on survey data.

The results show that certain influential factors seem to be generally important, such as the factors of respecting learning success or motivation. However, the outcome of the study also indicated that different moderating variables like experiences and personal relevance affect the professionals’ choices.

The originality and value are in the approach to identify generally important influential factors for the implementation of TEL approaches in Austrian and German institutions of higher education.

This purpose of this paper is to use autoethnographic methods to enable an individual to reflect on their learning practices and habits in their professional role in computing and data science.

Action reflection cycles with autoethnographic methods are used in this enquiry to examine and transform the self-driven learning in the role of a professional in the context of computing and data science. Data are collected using personal thoughts and emotions in order to determine to what degree the changes that are brought about are improving their learning practice.

An incongruity between espoused theory and theory-in-use is identified. In this paper the authors explore how engaging in reflection within the structure of an action research framework can change the learning behaviour of an individual, motivating them to engage more positively and consistently with self-directed learning in their workplace environment.

Through analysis of reflective journals key aspects of personal and professional life that influences the individual’s approach to the learning tasks is identified. In addition, activity logs are maintained which collect information on the tasks that are undertaken. These activity logs are automatically generated using specific task-tracking software. Double-loop learning (Argyris and Schön, 1974) helps to identify the values that underpin the learning practice.

By resolving the tension created due to the incongruity of values, the individual has been able to find more motivation for learning and thus become more engaged in the learning process.

Participation in the knowledge economy requires individuals to engage in continuous learning. Organisations that support individuals in continuous learning are best adapted to take advantage of emergent and evolving knowledge and skill requirements.

This paper examined the behaviour of an individual engaging in self-directed learning and showed how through engagement with reflection and critical self-analysis, he increased his motivation and efficiency for self-directed learning. The authors show how this skill becomes increasingly important in modern knowledge economy workplaces.

This paper aims to examine how academics enact trust in e-learning through an inductive identification of perceived risks and enablers involved in e-learning adoption, in the context of higher education institutions (HEIs).

Grounded Theory was the methodology used to systematically analyse data collected in semi-structured interviews with 62 academics. Data analysis followed the constant comparative method and its three-staged coding approach: open, axial and selective coding.

The resulting trajectory of trust factors is presented in a Grounded Theory narrative where individual change and integration through shared collective understanding and institutionalisation are discussed as stages leading to the overcoming of e-learning adoption barriers.

The paper proposes that the interplay between institutionalism and individualism has implications in the success or failure of strategies for the adoption of e-learning in HEIs, as perceived by academics. In practical terms, this points to the need for close attention to contextually sensitive trust-building mechanisms that promote the balance between academics’ commitments, values and sense of self-worth and centrally planned policy, rules, resources and exhortations that enable action.