- Open Access
A social learning analytics approach to cognitive apprenticeship
© Abu Khousa et al. 2015
- Received: 26 May 2015
- Accepted: 10 September 2015
- Published: 24 September 2015
The need for graduates who are immediately prepared for employment has been widely advocated over the last decade to narrow the notorious gap between industry and higher education. Current instructional methods in formal higher education claim to deliver career-ready graduates, yet industry managers argue their imminent workforce needs are not completely met. From the candidates view, formal academic path is well defined through standard curricula, but their career path and supporting professional competencies are not confidently asserted. In this paper, we adopt a data analytics approach combined with contemporary social computing techniques to measure, instil, and track the development of professional competences of learners in higher education. We propose to augment higher-education systems with a virtual learning environment made-up of three major successive layers: (1) career readiness, to assert general professional dispositions, (2) career prediction to identify and nurture confidence in a targeted domain of employment, and (3) a career development process to raise the skills that are relevant to the predicted profession. We analyze self-declared career readiness data as well as standard individual learner profiles which include career interests and domain-related qualifications. Using these combinations of data sources, we categorize learners into Communities of Practice (CoPs), within which learners thrive collaboratively to build further their career readiness and assert their professional confidence. Towards these perspectives, we use a judicious clustering algorithm that utilizes a fuzzy-logic objective function which addresses issues pertaining to overlapping domains of career interests. Our proposed Fuzzy Pairwise-constraints K-Means (FCKM) algorithm is validated empirically using a two-dimensional synthetic dataset. The experimental results show improved performance of our clustering approach compared to baseline methods.
- Learning analytics
- Career readiness
- Community of practice
- Big data
- Social networks
- Computational science
- Fuzzy logic
Worldwide, 31 percent of employers are having difficulties filling available positions, not because there aren’t enough workers, but because of “a talent mismatch between workers’ qualifications and their specific skill sets, against combinations of skills employers want” (Group 2010; 2013). New educational approaches are needed to prepare graduates enter the workforce through improving their capacity to succeed in a knowledge economy (P21 2010). However, higher education systems do not sufficiently utilize career-oriented data about current learners to improve the quality and the value of graduates in meeting market needs (Seely Brown 2008). Failure to exploit readily evident data and feedback on learning practices that match market needs, increases further the gap between education and industry and reduces intervention opportunities to prepare graduates for a successful career path with relevant professional performances. The pressure induced by education reforms and market needs require the integration of a new and smart learning environment in higher education to bridge diverse viewpoints and develop a common assertion of what it means to be career-ready. Developing this career-readiness capacity requires a sustained and progressive growth of professional habits and skills. Professional habits or dispositions could mature over time through a parallel path of professional development alongside the university’s formal academic path. This path could further be extended to complement these habits with relevant skills. However, current methods of teaching and learning in higher education programs are not sufficient to facilitate the development of these career-readiness dimensions. To fill this gap, we propose a virtual structure named Community of Practice (CoP) as an alternative informal way to achieve this aim (Gannon-Leary and Fontainha 2007). CoP concept has actually gained momentum in different educational systems since the 1990s (Lave and Wenger 1991; Wenger 1999; Wenger et al. 2002). Many studies addressed the need to move towards CoP-based models of education to better serve the needs of 21st century students (Jakovljevic et al. 2013; Lea et al. 2005). This is mainly because sharing knowledge, especially tacit knowledge that is notoriously difficult to teach in traditional classroom configurations, has been accepted as a mean for innovation and competitive advantage.
In traditional higher education programs, students may spend years learning about a subject (learning about); only after amassing sufficient explicit knowledge, they are expected to start acquiring the (tacit) knowledge or exercise of how to be active practitioners/professionals in a targeted field (learning to be). But viewing learning as the process of joining a CoP fosters a new form of apprenticeship as students observe and emulate mentors, while engaging in a “learning to be” cycle to master the skill of a field. This involves acquiring the practices and the norms of established practitioners in the field through early and continuous cognitive and practical apprenticeship experiences. Under the guidance of established practitioners, students work together in a common (virtual) social space and participate in each other’s learning process, while benefiting from mentor’s feedback (Gannon-Leary and Fontainha 2007; Seely Brown 2008).
In our proposed approach, Social Networks (SNs) are employed to build online CoPs within higher education context (Gunawardena et al. 2009; Zhang et al. 2010) to influence learners following needed career prospects in the market. Besides their influential power, SNs have a substantial value in strengthening student-to-student interactions, enhancing student social engagements, and building campus communities toward improving student learning (Davis III et al. 2012). Facebook, one of the most powerful SN, is perceived to enhance the connectedness and sense of social learning in higher education settings (Baran 2010; Qureshi et al. 2015; Selwyn 2009); and to advance the practice from information-sharing to synergistic knowledge development and innovation (O’Brien and Glowatz 2013). Our approach builds a social structure that is centred around a business need and empowered with professional connectivity. Towards that prospect, we devised a fuzzy clustering approach which predicts and sustains learner’s career path along specific profeciencies. The clustering algorithm analyzes different categories of career readiness data to predict a hypothetical career practice and bring learners with similar career patterns together into the same cluster. This process leads to a social structure made up of CoPs, which are identified to specifically respond to imminent industrial needs. We consider personal specific preferences and predispositions of learners that do not disappear when they join CoPs to enrich learners’ experience within CoPs as they contribute to their own growth and sustainability.
Traditional higher education programs focus on instructing subjects with limited attention to actually prepare students for their future career and seizing current opportunities available in the job market. This creates the need to integrate career readiness into formal higher education to develop a new learning environment that bridges the gap between education and industry. The challenge of devising a smart learning environment that supplements formal education with career development pedagogies appears to be multifaceted. This complexity is due to the numerous factors induced when instilling professional habits and skills. Hence, the process requires to first synthesize professional habits into well-defined dimensions, and then to create a platform to nurture their development and evolution into professional practices. This is because industry-needs require both generic professional dispositions and specific domain knowledge, which are usually remote from the ones acquired in formal education. Hence, an educational environment that builds specific domain-related skills is expected to claim career-readiness upon graduation, in addition to general professional dispositions. One more challenge would be to devise the process to identify and bring individuals whose career prospects are deemed similar, into a common learning environment that is aligned with job market needs and opportunities, even before graduation. Formal predicticve analytics methods combined with contemporary social computing structures are discussed in this paper to address these issues.
A CoP model in higher education to support career-prediction.
A portal structure to capture individual professional traits to support career-readiness.
A Career Profile data structure to record both individual professional traits and career aspirations.
A Fuzzy clustering algorithm to match similar career profile patterns and construct CoPs that are driven by current industrial needs.
A Social Learning Analytics (SLA) framework to track career development within CoPs.
In a medical school, learners spend two years studying general medical knowledge (called Basic Sciences), and two years of Clinical Sciences were they get to spend time acquiring knowledge in different medical specialties. They learn about subspecialties as well, but only after completing the required rotations across medical specialities to build background and interest into a potential medical career practice. The selected specialty results in a Residency program within the scope of the specialty, like family medicine, internal medicine, paediatrics, dermatology, surgery, etc.
The remaining sections of this paper are organized as follows. Section ‘Background and related works’ provides some background and explores some related works. Section ‘Community of practice apprenticeship model’ presents the general framework of our proposed CoP apprenticeship model, while Section ‘Fuzzy semi-supervised clustering algorithm’ describes our proposed social learning analytics method for career prediction. Section ‘Performance evaluation’ reveals some results of our experimental analysis which demonstrate the advantages of our CoP clustering method over standard methods. Finally, Section ‘Conclusion and future work’ concludes the paper with a summary of our contributions and our future work.
At the first stage of our scenario, learners fill out the Career Profile where they provide information about their competencies, qualifications, interests and skills. For example, going back to our scenario, students could list their medical career interests. Learners also complete a Career Readiness survey in order to measure their Career Dispositions. These are the generic skills that engenders the professional and deontological behaviors. In a previous work, we addressed this stage of career-readiness through the provision of an online instrument for collecting self-assessment data to produce willing, confident and creative lifelong learners (Atif et al. 2014). The provided instrument presents a storehouse view of career dispositionsthrough an integrated portal which captures self-stated learning experiences and converts them into analytical results. The outcome of this stage roots out deficiencies in dispositions for the targeted practice and prescribe improvement recommendations.
We define the concept of career dispositions that emerge as the joint set of attitudes and generic skills that dispose individuals to engage profitably with learning from new professional environment in order to be able to adapt to career changes and to manage their career growth. We model these dispositions as a 6-dimensional construct that comprises: Openness to challenge (OC), Critical Thinking (CT), Resilience (R), Learning Relationships (LR), Responsibility for Learning (RL), and Creativity (C). These dimensions in general describe the natural tendencies, mind state and preparations of each individual towards a professional practice. As implied by disposition label, high score learners in openness to challenge are those who are curious and open to new ideas and experiences. Critical thinkers are those who are evidence based decision makers. learners who score high in resilience dimension are those who are determined, competitive and achievement oriented. While social oriented learners score high on learning relationships dimension, dependable and motivated learners are most likely to score high in responsibility for learning dimension. Creative learners are those who are original, imaginative and adventuresome. We developed the Self-Reflective Career Dispositions Scale (SRCDS) metric that is a self-report instrument to quantify these dimensions and qualify learners to embrace professional practices. Career disposition indicators are converted into numerical “raw scores”, which are then stored in a data warehouse for novel aggregation and analysis (Atif et al. 2014). This process provides the opportunity to create mentoring workflows to support a portfolio of assessments that gauge learners’ progress across curricular instructions and their social and professional interactions in the industry-needs matching CoP they assigned to by career prediction process.
The developmental realization of a career is better achieved by uniting around a common goal to learn from each other and from expert domain-specific mentors. The collected learning data from this initial career association process is further analyzed against current industry trends to refine career-patterns that in-turn synthesize further industry-needs matching CoPs. Learning Analytics (LA) techniques identify indicators that bridge education with industry needs to leverage workforce developments. Model of ontologies will be used to describe industry needs and market trends; in order to be able to match them with the learners’ domain of career interest (Maynard et al. 2005).
This paper scope falls within the Career Predication step through a model that allocates and connects learners who share common career interest to initiate a CoP experience. For example, medical practice students who share pediatrics interests could foster a comon CoP. Learners may actually be assigned to several CoPs according to their interests, which results into potential overlap between CoPs as learners interests may intiailly span multiple specialty prospects. At the hub of each CoP, there is a group of learners who displayed a high level of career dispositions (inferred from the portal analytics in the previous step). These seed learners support the elaboration of relationships with other medical practioners within selected disiplines labelling the CoP. Our model suggests to survey the current industry needs as part of CoP metadata. In the context of our scenario, the pediatric market demand analysis lists expert personnel deficiency in five sub-specialization for the next coming seven years (2016-2021) that are: Allergy/Immunlogy, Anesthesiology, Cardiology, Cardiothoracic Surgery, and Critical Care (Ministries 2015). Our dynamic CoP structure then evolves to transcend pediatrics medical practitioners into sub-disciplines, forming new CoPs as illustrated further in Fig. 1. Each CoP is assigned an expert mentor to operate the community synergistic relationships. This includes sharing experiences and learning resources to sustain the development of interest and skills of community members in a collaborative effort. Our model suggests a new role provided by the industry which in this case is the medical sector to incorporate representative pediatrician with a pedagogical profile to mentor the community. CoP admits automatically all learners who pass the disposition threshold and meet the advertised discipline by the CoP.
Career development and SLA
The members’ constant interactions within CoP create a dynamic knowledge container and a repertoire of shared practices and experiences. As the community thrives, learners develop their domain pracrices, and may recognize and then reach out other potential members (away from pediatrics) to migrate to other CoPs e.g. nutritionist, psychologist, etc. This gateway accomodates possible changes on Career Profile. However, the evolution of CoPs is outside the scope of this paper as we focus essentially on iniitial career predicitons whereas the career development stage is part of our future work.In this section, we provide a brief description of how this module operates.
The proposed module supports long-term career development utilizing an SLA engine and a CoP management component. SLA engine aims to investigate networking process, roles, properties of ties, relationships and how learners develop and maintain these relationships to support their career development. Specifically, we are interested in measuring user engagement and how they develop from a peripheral participation to centripetal participation in ongoing activities of the community. On other words, measure the interaction volume (e.g. login frequency, duration of login and number of connection) and the size of contribution to the practice resources (e.g. number of contributions, frequency of posts, and average length of posts). We expect learners to develop a changing understanding of practice over time by shifting from knowledge consumption only to knowledge creation through a social interaction process. Moreover, we propose to use an SLA engine to track the development of career dispositions in relation to the set of skills required by the industry for each designated career.
In order for the community to grow and have meaning, the individual members must be motivated to engage with it actively to create and maintain information flow. In this essence, we propose a CoP management system that has three main functions: (1) Define CoP focus and major roles; (2) measure the effectiveness of CoP; and (3) dynamic updates when changes occur in learner’s profiles and/or industry needs. For measuring CoP effectiveness, we propose developing a comprehensive set of evaluation measures inspired by: (1) criteria to underpin the CoP of learners in the educational context (e.g. development of learners’ reflective experience, encouragement of multidisciplinary knowledge sharing, and support learning through cognitive and practical apprenticeship (Jakovljevic et al. 2013); and (2) fundamental elements of successful online CoP (e.g. knowledge generating interactions, efficiency of involvement, connections to the world, and belonging and relationships) (Wenger et al. 2002).
We have seeds and each class will have at least one seed. The seed labels are always correct.
We have pairwise constraints, must-links and cannot-links. These constraints could be wrong.
We allow fuzzy labeling, namely each instance can be in more than one cluster.
All labels are assigned to both seeds and constraints.
- 1.Initialize the centroids of each cluster as the average of the seeds belonging to that clusterTable 1
Notions and symbols
The input domain
Number of clusters
Initial centeroids of cluster
Indices running over clusters
Indices running over instances or output clusters’ labels
Input data instance x a ∈X
Output cluster lable y a ∈ [C]
D(x a ,μ j )
Distance between instance x a and center of cluster j
h ∗=a r g m i n h O new
Instance assignment that minimally increases the error terms
Assign instances to minimize the new objective function O n e w1 shown in Eq. (1)
Update the cluster centroids to minimize the objective function as shown in Eq. (2)
Repeat until convergence
For instances that are not part of constraints, perform a nearest cluster centroid calculation. For pairs of instances in a constraint, for each possible combination of cluster assignments, the function is calculated and the instances are assigned to the clusters that minimally increases the error term h ∗=a r g m i n h O new . I(A) is an indicator function defined as follows: I(A)=0 if A=T r u e and I(A)=1 if A=F a l s e, and l a b e l(x a ,x b ) is the label of the constraint. Thus when a link is violated, we check if its associated label is different from the label that x a is assigned to. If yes, the violation is not penalized.
The update rule applies that if a must-link constraint is violated, the cluster centroid is moved towards the other cluster containing the other instance. Similarly, the interpretation of the update rule for a cannot-link constraint violation is that cluster centroid containing both constrained instances should be moved to the nearest cluster centroid so that one of the instances eventually gets assigned to it, thereby satisfying the constraint. Our formal algorithm is formally depicted next.
In this section, we show the performance of our algorithm based on simulated artificial data, and compare our results along two K-means candidate methods: (1) Seeded K-Means (SKM); and (2) Pairwise-constraints K-Means algorithm (PKM). In our experiment, we run the three algorithms to obtain a complete seeding set from a sample dataset. We specifically aim to test our algorithm’s performance when the overlap degree increases as compared to baseline methods that do not support fuzzy assignments.
Randomly generate the center of the clusters. Then for each cluster, take a radius as input and randomly sample a given number of data points in the circle.
To determine if a data point belongs to multiple clusters, consider the distance of the data point to each cluster center. If the distance is no greater than the radius of the cluster, the point belongs to the cluster.
We then simulated a two-dimensional artificial data. The centers of clusters are generated randomly (μ=0 ; σ = 1) within the range, which is a circle with (0, 0) as the center and R = 15 as the radius of the circle in which cluster centers are generated. Then, for each cluster we consider its radius as input and then randomly sample a given number of data points within that circle (following a uniform distribution). To determine if a data point belongs to multiple clusters, we consider the distance of data points to each cluster center. If the distance is no greater than the radius of the cluster, then we consider that the point belongs to the cluster. The generated data set consists of three clusters (C = 3) with 200 samples in each cluster. The constraints used in our algorithm are generated as follows: for each constraint, we randomly pick two instances from the data (following a uniform distribution) and then we check their labels (which are made available for the evaluation purpose but not visible to the clustering algorithm). If they exhibit any common label, we generated a must-link constraint. Otherwise, we generate a cannot-link constraint.
The precision is the ratio tp/(tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative.
The recall is the ratio tp/(tp + fn) where tp is the number of true positives and fn the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples.
Typically, precision and recall are given equal weight with α=1. Varying the coefficient α provides a means of biasing F- score towards precision or recall (e.g., α=0.5 biases it towards precision; α=2.0 biases it towards recall). The total F_S c o r e is calculated as the average of the largest F_S c o r e of each cluster.
Overlap degree vs accuracy of FPKM
No. of nodes in overlapped region
In response to the demands to bridge the growing gap between higher education and industry, we introduced a model to incorporate career readiness into formal education to form a new CoP-based learning model which utilizes learning analytics and social networks techniques. The proposed model consists of three major modules: career readiness, career prediction and career development. We first elaborated a learning analytics model to identify career indicators, as well as patterns that contribute to clustering learners into common virtual CoPs. The learners’ relationships, engagement and interaction instances within CoPs are tracked using a social learning analytics framework to evaluate the development of domain-related skills under the guidance of an experienced mentor or an active member with superior career dispositions.
We further devised a semi-supervised clustering method to bring learners with similar professional traits that match a typical career pattern together into the same cluster. Our method aims to initially form a CoP with a seed set of learners who can drive the CoP activities and sustain its effectiveness. We emphasized the natural overlap nature of industrial needs and career paths by allowing each leaners to be in more than one cluster. We experimentally show the improved performance of the proposed clustering approach when the overlap degree increases, in comparison with baseline line methods of seeded and pairwise-constraints K-means algorithm. Hence, our method has the potential to serve as a learning analytics tool to reveal hidden patterns of common traits among learners viewed as future candidates of the job market. These patterns could evolve into social communities of learners with shared career interests, that evolve socially rather than individually. A real data set that includes indicators captured by our career readiness module is expected to prove the concept proposed in this paper as part of our future work.
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- AbuKhousa, E, Y Atif, in Interactive Collaborative Learning (ICL). Big learning data analytics support for engineering career readiness (International Conference on, 2014), pp. 663–668. social learning analytics.Google Scholar
- Ali, L, M Hatala, D Gašević, J Jovanović, A qualitative evaluation of evolution of a learning analytics tool. Comput. Educ. 58(1), 470–489 2012.View ArticleGoogle Scholar
- Atif, Y, E Abu Khousa, SS Mathew, K Al Awar, N Al Sayari, in Advanced Learning Technologies (ICALT). A portal support to cognitive apprenticeship (IEEE 14th International Conference on, 2014), pp. 449–453.Google Scholar
- Arnold, KE, MD Pistilli, in Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. Course signals at purdue: Using learning analytics to increase student success (ACM, 2012), pp. 267–270.Google Scholar
- Baran, B, Facebook as a formal instructional environment. Br. J. Educ. Technol. 41(6), 146–149 2010.View ArticleGoogle Scholar
- Bauer, T, TN Bodner, B Erdogan, DM Truxillo, JS Tucker, Newcomer adjustment during organizational socialization: a meta-analytic review of antecedents, outcomes, and methods. J. Appl. Psychol. 92(3), 707 2007.View ArticleGoogle Scholar
- Blankenship, M, How social media can and should impact higher education. Educ. Digest. 76(7), 39–42 2011.Google Scholar
- Burke, M, C Marlow, T Lento, in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Social network activity and social well-being (ACM, 2010), pp. 1909–1912.Google Scholar
- Cabada, RZ, MLB Estrada, CAR García, Educa: A web 2.0 authoring tool for developing adaptive and intelligent tutoring systems using a kohonen network. Expert Syst. Appl. 38(8), 9522–9529 2011.View ArticleGoogle Scholar
- Chatti, MA, AL Dyckhoff, U Schroeder, H Thüs, A reference model for learning analytics. Int. J. Technol. Enhanced Learn. 4(5), 318–331 2012.View ArticleGoogle Scholar
- Colthorpe, K, K Zimbardi, L Ainscough, S Anderson, Know thy student! combining learning analytics and critical reflections to develop a targeted intervention for promoting self-regulated learning. J. Learn. Anal. 2(1), 134–155 2015.Google Scholar
- Dawson, S, JPL Tan, E McWilliam, Measuring creative potential: Using social network analysis to monitor a learners’ creative capacity. Australas. J. Educ. Technol. 27(6), 924–942 2011.Google Scholar
- Davidson, I, S Basu, A survey of clustering with instance level constraints. ACM Trans. Knowl. Discov. Data. 1, 1–41 2007.MATHView ArticleGoogle Scholar
- Davis III, R, CH Deil-Amen, C Rios-Aguilar, MS Gonzalez Canche, Social media in higher education: A literature review and research directions 2012.Google Scholar
- DeAndrea, DC, NB Ellison, R LaRose, C Steinfield, A Fiore, Serious social media: On the use of social media for improving students’ adjustment to college. Internet High. Educ. 15(1), 15–23 2012.View ArticleGoogle Scholar
- De Liddo, A, SB Shum, I Quinto, M Bachler, L Cannavacciuolo, in Proceedings of the 1st International Conference on Learning Analytics and Knowledge. Discourse-centric learning analytics (ACM, 2011), pp. 23–33.Google Scholar
- Ferguson, R, The state of learning analytics in 2012: A review and future challenges. Knowl. Media Inst. Tech. Rep. KMI-2012. 1, 2012 2012.Google Scholar
- Gannon-Leary, P, E Fontainha, Communities of practice and virtual learning communities: benefits, barriers and success factors. Barriers and Success Factors. eLearning Papers. 5 2007.Google Scholar
- Gray, K, L Annabell, G Kennedy, Medical students’ use of facebook to support learning: Insights from four case studies. Med Teach. 32(12), 971–976 2010.View ArticleGoogle Scholar
- Group, M, Talent Shortage Survey Results, Manpower 2010. Supply/Demand: 2010 Talent Shortage Survey Results 2010.Google Scholar
- Group, M, Supply/Demand: 2013 Talent Shortage Survey Results 2013.Google Scholar
- Greenhow, C, B Robelia, JE Hughes, Learning, teaching, and scholarship in a digital age web 2.0 and classroom research: what path should we take now?Educ. Res. 38(4), 246–259 2009.View ArticleGoogle Scholar
- Greller, W, H Drachsler, Translating learning into numbers: A generic framework for learning analytics 2012.Google Scholar
- Gunawardena, CN, MB Hermans, D Sanchez, C Richmond, M Bohley, R Tuttle, A theoretical framework for building online communities of practice with social networking tools. Educ. Media Int. 46(1), 3–16 2009.View ArticleGoogle Scholar
- Hämäläinen, W, Descriptive and predictive modelling techniques for educational technology. Licentiate thesis, Department of Computer Science, University of Joensuu 2006.Google Scholar
- Haythornthwaite, C, M De Laat, in 7th International Conference on Networked Learning. Social networks and learning networks: Using social network perspectives to understand social learning (Aalborg, Denmark, 2010).Google Scholar
- Helliwell, JF, RD Putnam, et al, The social context of well-being. Phil. Trans. R. Soc London Series B Biol. Sci., 1435–1446 2004.Google Scholar
- Hew, KF, Students’ and teachers’ use of facebook. Comput. Hum. Behav. 27(2), 662–676 2011.View ArticleGoogle Scholar
- Hung, H-T, SC-Y Yuen, Educational use of social networking technology in higher education. Teach. Higher Educ. 15(6), 703–714 2010.View ArticleGoogle Scholar
- Hwang, A, EH Kessler, AM Francesco, Student networking behavior, culture, and grade performance: an empirical study and pedagogical recommendations. Acad. Manag. Learn. Educ. 3(2), 139–150 2004.View ArticleGoogle Scholar
- Jakovljevic, M, S Buckley, M Bushney, Forming communities of practice in higher education: a theoretical perspective 2013.Google Scholar
- Junco, R, G Heiberger, E Loken, The effect of twitter on college student engagement and grades. J. Comput. Assisted Learn. 27(2), 119–132 2011.View ArticleGoogle Scholar
- Kabilan, N, MK Ahmad, MJZ Abidin, Facebook: An online environment for learning of english in institutions of higher education?. Internet Higher Educ. 13(4), 179–187 2010.View ArticleGoogle Scholar
- Katz, S, L Earl, Creating new knowledge: Evaluating networked learning communities. Educ. Canada-Toronto. 47(1), 34 2007.Google Scholar
- Kitto, K, S Cross, Z Waters, M Lupton, in Proceedings of the Fifth International Conference on Learning Analytics And Knowledge. Learning analytics beyond the lms: the connected learning analytics toolkit (ACM, 2015), pp. 11–15.Google Scholar
- Koprinska, I, in EDM. Mining assessment and teaching evaluation data of regular, advanced stream students (Citeseer, 2011), pp. 359–360.Google Scholar
- Lave, J, E Wenger, Situated Learning: Legitimate Peripheral Participation (Cambridge University Press, 1991).Google Scholar
- Lea, M, D Barton, K Tusting, Communities of practice in higher education. Beyond communities of practice: Language, power and social context 2005.Google Scholar
- Leony, D, A Pardo, L de la Fuente Valentín, DS de Castro, CD Kloos, in Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. Glass: a learning analytics visualization tool (ACM, 2012), pp. 162–163.Google Scholar
- Liccardi, I, A Ounnas, R Pau, E Massey, P Kinnunen, S Lewthwaite, M-A Midy, C Sarkar, in ACM SIGCSE Bulletin, 39. The role of social networks in students’ learning experiences (ACM, 2007), pp. 224–237.Google Scholar
- LIP, I, IMS Learner Information Package. Information Model, Best Practice and Implementation Guide, XML Binding, Schemas. Version 2001.Google Scholar
- Martín, E, M Gértrudix, J Urquiza-Fuentes, PA Haya, Student activity and profile datasets from an online video-based collaborative learning experience. Br. J. Educ. Technol 2015.Google Scholar
- Maynard, D, M Yankova, A Kourakis, A Kokossis, in ESWC Workshop “End User Apects of the Semantic Web,”. Ontology-based information extraction for market monitoring and technology watch (Heraklion, Crete, 2005).Google Scholar
- Merceron, A, in EDM. Investigating usage of resources in lms with specific association rules, (2011), pp. 361–362.Google Scholar
- Ministries, MH, Pediatric Subspecialty Physician Needs. Online Report 2015.Google Scholar
- Morrison, EW, Newcomers’ relationships: The role of social network ties during socialization. Acad. Manag. J. 45(6), 1149–1160 2002.View ArticleGoogle Scholar
- O’Brien, O, M Glowatz, Utilising a social networking site as a learning tool in an academic environment: Advancing practice from information-sharing to collaboration and innovation (ici). AISHE-J: All Ireland J. Teaching & Learn. Higher Educ. 5(3) 2013.Google Scholar
- Ozgen, E, RA Baron, Social sources of information in opportunity recognition: Effects of mentors, industry networks, and professional forums. J. Bus. Ventur. 22(2), 174–192 2007.View ArticleGoogle Scholar
- Pardo, A, Social learning graphs: combining social network graphs and analytics to represent learning experiences. Int. J. Soc. Media Interactive Learn. Environ. 1(1), 43–58 2013.View ArticleGoogle Scholar
- Podolny, JM, JN Baron, Resources and relationships: Social networks and mobility in the workplace. Am. Sociol. Rev., 673–693 1997.Google Scholar
- P, 21, Up to the Challenge: The Role of Career and Technical Education and 21st Century Skills in College and Career Readiness 2010. http://www.p21.org/storage/documents/CTE_Oct2010.pdf.
- Qureshi, H, IA Raza, M Whitty, Facebook as e-learning tool for higher education institutes. Knowl Manag; E-Learning: Int J(KM&EL). 6(4), 440–448 2015.Google Scholar
- Rabbany, R, S Elatia, M Takaffoli, OR Zaïane, in Educational Data Mining. Collaborative learning of students in online discussion forums: A social network analysis perspective (Springer, 2014), pp. 441–466.Google Scholar
- Reich, J, R Murnane, J Willett, The state of wiki usage in us k–12 schools leveraging web 2.0 data warehouses to assess quality and equity in online learning environments. Educ. Res. 41(1), 7–15 2012.View ArticleGoogle Scholar
- Romero, C, S Ventura, Educational data mining: a survey from 1995 to 2005. Expert Syst. Appl. 33(1), 135–146 2007.View ArticleGoogle Scholar
- Seely Brown, J, Open education, the long tail, and learning 2.0. Educ. Rev. 43(1), 16–20 2008.Google Scholar
- Selwyn, N, Faceworking: exploring students’ education-related use of facebook. Learn. Media Technol. 34(2), 157–174 2009.View ArticleGoogle Scholar
- Seibert, SE, ML Kraimer, The five-factor model of personality and career success. J. Vocat. Behav. 58(1), 1–21 2001.View ArticleGoogle Scholar
- Segedy, JR, JS Kinnebrew, G Biswas, Using coherence analysis to characterize self-regulated learning behaviours in open-ended learning environments. J. Learn. Anal. 2(1), 13–48 2015.Google Scholar
- Scott, A, P Clarkson, A McDonough, Fostering professional learning communities beyond school boundaries. Australian J. Teacher Educ.36(6), 5 2011.View ArticleGoogle Scholar
- Siemens, G, What are learning analytics. Retrieved March. 10, 2011 2010.Google Scholar
- Siemens, G, P Long, Penetrating the fog: Analytics in learning and education. Educ. Rev. 46(5), 30–32 2011.Google Scholar
- Siemens, G, RS d Baker, in Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. Learning analytics and educational data mining: towards communication and collaboration (ACM, 2012), pp. 252–254.Google Scholar
- Shum, R, SB Ferguson, Social learning analytics. Educ. Technol. Soc. 15(3), 3–26 2012.Google Scholar
- Steinfield, C, NB Ellison, C Lampe, Social capital, self-esteem, and use of online social network sites: A longitudinal analysis. J. Appl. Dev. Psychol. 29(6), 434–445 2008.View ArticleGoogle Scholar
- Tian, SW, AY Yu, D Vogel, RC-W Kwok, The impact of online social networking on learning: a social integration perspective. Int. J. Netw. Virtual Organ. 8(3), 264–280 2011.View ArticleGoogle Scholar
- Wagstaff, K, C Cardie, Clustering with instance-level constraints. AAAI/IAAI. 1097 2000.Google Scholar
- Wang, X, C Wang, J Shen, in Web Information Systems and Mining. Semi–supervised k-means clustering by optimizing initial cluster centers (Springer, 2011), pp. 178–187.Google Scholar
- Wells, G, G Claxton, Learning for Life in the 21st Century: Sociocultural Perspectives on the Future of Education (John Wiley & Sons, 2008).Google Scholar
- Wenger, E, Communities of Practice: Learning, Meaning, and Identity (Cambridge University Press, 1999).Google Scholar
- Wenger, E, RA McDermott, W Snyder, Cultivating Communities of Practice: A Guide to Managing Knowledge (Harvard Business Press, 2002).Google Scholar
- Wertsch, P, JV del Río, A Alvarez, Sociocultural Studies of Mind (Cambridge University Press, 1995).Google Scholar
- Xu, B, M Recker, S Hsi, The data deluge: Opportunities for research in educational digital libraries. Internet Issues: Blogging, the Digital Divide and Digital Libraries (Nova Science Pub Inc., New York, 2010).Google Scholar
- Xing, W, S Goggins, in Proceedings of the Fifth International Conference on Learning Analytics And Knowledge. Learning analytics in outer space: a hidden naïve bayes model for automatic student off-task behavior detection (ACM, 2015), pp. 176–183.Google Scholar
- Yu, AY, SW Tian, D Vogel, RC-W Kwok, Can learning be virtually boosted? an investigation of online social networking impacts. Comput. Educ. 55(4), 1494–1503 2010.View ArticleGoogle Scholar
- Zhang, S, C Flammer, X Yang, Uses, challenges, and potential of social media in higher education’. Cutting-edge Social Media Approaches to Business Education: Teaching with LinkedIn, Facebook, Twitter, Second Life, and Blogs, 217 2010.Google Scholar
- Zimmermann, J, KH Brodersen, J-P Pellet, E August, JM Buhmann, in EDM. Predicting graduate-level performance from undergraduate achievements, (2011), pp. 357–358.Google Scholar