Lifelong Learning and Collaboration of Smart Technical Systems in Open-Ended Environments -- Opportunistic Collaborative Interactive Learning

Bahle, Gernot; Calma, Adrian; Leimeister, Jan Marco; Lukowicz, Paul; Oeste-Reiß, Sarah; Reitmaier, Tobias; Schmidt, Albrecht; Sick, Bernhard; Stumme, Gerd; Zweig, Katharina Anna

doi:10.1109/icac.2016.36

Cited by 13 publications

(5 citation statements)

References 47 publications

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“…So while the term CIL is used in the IT sector [21], [22], in the context of university training it is the syntax of different tools and methods to make student learn in a highly engaging setting, to maximize the learning outcome. The university of applied Sciences FH Technikum Wien has embarked on a project of curricula modularization and standardization [24], [25], in order to offer students an up-todate studying experience.…”

Section: Collaborative Interactive Learning "The Field Of Collaborative Interactive Learning (Cil) Aims At Developing and Investigating Tmentioning

confidence: 99%

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Learning behavior of technical students – Awareness as key competence

Lackner¹,

Klamert-Schmid²,

Zangl³

et al. 2021

Technium

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Learning has become more important than ever before, as we are moving towards a digital, fast-changing society. Formal university training has changed, too, from pure “lectures” to more modern formats, including e-learning, interactive and collaborative settings. Students also find plenty of information online, and the new skill in demand is finding relevant knowledge instead of having memorized knowledge. Knowledge doubles fast and becomes outdated even faster. Being aware of one’s learning behaviors was found to be a core competence of university students eager to educate themselves. This work sets a self-assessment amongst bachelor and master students of a technical programme – International Business and Engineering – into perspective with literature findings. The authors conclude that knowing ones strengths in learning is a prerequisite of acquiring additional knowledge effectively and efficiency. Students have become more demanding towards the presentation of learning opportunities. However, it is them who have to take initiative to make most out of their learning journeys. As students progress through their studies, their awareness of collaborative and interactive learning seems to decrease, which reminds one of Socrates: “The more I know, the more I realize I know nothing.” One can speculate that his finding is due to more critical reflection by master students, or that by the time the students have entered the master’s programme, the novel learning approaches have been familiarized/assimilated and do not receive further attention, which is a hint that the art of collaborative interactive learning has been mastered.

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Section: Collaborative Interactive Learning "The Field Of Collaborative Interactive Learning (Cil) Aims At Developing and Investigating Tmentioning

confidence: 99%

“…In [21], opportunistic collaborative interactive learning (OCIL) is discussed. This might become an interesting future area of research.…”

Section: Conclusion Outlookmentioning

confidence: 99%

Learning behavior of technical students – Awareness as key competence

Lackner¹,

Klamert-Schmid²,

Zangl³

et al. 2021

Technium

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“…Various concepts have been proposed to overcome the limitations above. These include collaborative interactive learning [36,37] and proactive learning [38,39]. We summarize their main differences to traditional AL through the three following aspects:…”

Section: Introductionmentioning

confidence: 99%

A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification

et al. 2021

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Pool-based active learning (AL) aims to optimize the annotation process (i.e., labeling) as the acquisition of annotations is often time-consuming and therefore expensive. For this purpose, an AL strategy queries annotations intelligently from annotators to train a high-performance classification model at a low annotation cost. Traditional AL strategies operate in an idealized framework. They assume a single, omniscient annotator who never gets tired and charges uniformly regardless of query difficulty. However, in real-world applications, we often face human annotators, e.g., crowd or in-house workers, who make annotation mistakes and can be reluctant to respond if tired or faced with complex queries. Recently, a wide range of novel AL strategies has been proposed to address these issues. They differ in at least one of the following three central aspects from traditional AL: (1) They explicitly consider (multiple) human annotators whose performances can be affected by various factors, such as missing expertise. (2) They generalize the interaction with human annotators by considering different query and annotation types, such as asking an annotator for feedback on an inferred classification rule. (3) They take more complex cost schemes regarding annotations and misclassifications into account. This survey provides an overview of these AL strategies and refers to them as real-world AL. Therefore, we introduce a general real-world AL strategy as part of a learning cycle and use its elements, e.g., the query and annotator selection algorithm, to categorize about 60 real-world AL strategies. Finally, we outline possible directions for future research in the field of AL.

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“…Research on CIL started in 2015 by carving out the various research challenges and solution statements in the field of dedicated CIL (D-CIL) [9]. Then, preliminary work in the field of opportunistic CIL (O-CIL) was evaluated, various application fields were discussed, and results of a case study on collaborative travel time estimation were presented [2]. After some further work on the CIL concept [10,60,8], the authors prepared the work on CIL with joint work such as developing techniques for simulating human oracles [11], analysis of the practical use of human oracles in active learning [12], and performing a case study that evaluates how human oracles perform when labeling traffic signs [7].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields

Hanika¹,

Herde²,

Kühn³

et al. 2019

Preprint

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The field of collaborative interactive learning (CIL) aims at developing and investigating the technological foundations for a new generation of smart systems that support humans in their everyday life. While the concept of CIL has already been carved out in detail (including the fields of dedicated CIL and opportunistic CIL) and many research objectives have been stated, there is still the need to clarify some terms such as information, knowledge, and experience in the context of CIL and to differentiate CIL from recent and ongoing research in related fields such as active learning, collaborative learning, and others. Both aspects are addressed in this paper.

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Lifelong Learning and Collaboration of Smart Technical Systems in Open-Ended Environments -- Opportunistic Collaborative Interactive Learning

Cited by 13 publications

References 47 publications

Learning behavior of technical students – Awareness as key competence

Learning behavior of technical students – Awareness as key competence

A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification

Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields

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