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working on front ends for recommenders introduces artificial intelligence into the design process, an entirely ‘new design material [6].’ With that comes a multitude of new design challenges [6,7,8,9]. These challenges may include issues such as explaining the recommended results and straightforwardly presenting these explanations to the user or allowing the user to modify recommended results. 

Smits, A., & van Turnhout, K. (2023, August). Towards a practice-led research agenda for user interface design of recommender systems. In IFIP Conference on Human-Computer Interaction (pp. 170-190). Cham: Springer Nature Switzerland.

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C. Stanik, L. Montgomery, D. Martens, D. Fucci and W. Maalej, "A Simple NLP-Based Approach to Support Onboarding and Retention in Open Source Communities," 2018 IEEE International Conference on Software Maintenance and Evolution (ICSME), Madrid, Spain, 2018, pp. 172-182, doi: 10.1109/ICSME.2018.00027. keywords: {Feature extraction;Machine learning;Computer bugs;Open source software;Natural language processing;History;Forestry;open source software;onboarding;task selection;newcomers;machine learning;natural language processing},

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According to von Krogh et al. [5], “joiners” are members that have not yet made a contribution; while “newcomers” are members that have made at least one contribution, but have not yet found a more formal role within the community.

5. G. Von Krogh, S. Spaeth and K.R. Lakhani, "Community joining and specialization in open source software innovation: A case study", Res. Policy, vol. 32, pp. 1217-1241, 2003.

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“In a systematic literature review, Steinmacher et al. [2] found that one of the major barriers faced by newcomers to OSS projects is finding a way to start contributing to the project. Onboarding—the stage at which an outsider makes the first contributions to the project—usually starts by picking an open issue (e.g., a bug report or a feature request) to work on from the project issue tracker.” 

C. Stanik, L. Montgomery, D. Martens, D. Fucci and W. Maalej, "A Simple NLP-Based Approach to Support Onboarding and Retention in Open Source Communities," 2018 IEEE International Conference on Software Maintenance and Evolution (ICSME), Madrid, Spain, 2018, pp. 172-182, doi: 10.1109/ICSME.2018.00027. keywords: {Feature extraction;Machine learning;Computer bugs;Open source software;Natural language processing;History;Forestry;open source software;onboarding;task selection;newcomers;machine learning;natural language processing},

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Levels of engagement

While member engagement is important, the level of participation need not be equal among all members. Some will participate more actively than others because the domain is more directly relevant to them. Figure 3.2 shows the typical levels of participation often found in communities of practice:

Core group: a small group of people whose passion and engagement energize the community and who often take on some tasks to help nurture the community

Active participants: practitioners who are engaged and committed to the ongoing progress of the community (though they may not be of one mind as to what the community is about or where it should go)

Occasional participants: members who participate selectively when something is of special interest to them

Peripheral participants: people who belong to the community lightly, either because they are still newcomers or because they do not have as much personal commitment to the practice, but want to keep informed

Transactional participants: outsiders who interact with the community occasionally to receive or contribute something without being members themselves

Wenger, E. (1998). Communities of Practice: Learning, Meaning, and Identity. Cambridge: Cambridge University Press, pp.1–20.