Chapter 12 Self-evaluation and reflection of the sessions

The assignments included feedback from my observers. However, as they are not my words I have not included them and only included my own self-evaluations and reflections on the sessions.

12.1 Learning outcomes

The teaching session I created and ran went very well (A1 & A2). Learner’s were given a good introduction to the day’s materials and they were engaged and motivated. However, my observer pointed out that I could incorporate learning outcomes (LOs) into the materials and presentation better, and promote networking. In this reflection I would like to focus on incorporating LOs for future courses (A5).

LOs are important in higher education as part of changes and standardisation caused by the Bologna Declaration (1999) (V4). This agreement was intended to promote quality assurance and allow for the greater comparability and compatibility of University courses across Europe (Wächter., 2004). The Bologna Declaration was followed up by other agreements, including the Berlin Communiqué (2003). This first encouraged the use of Learning outcomes in these European processes (Sweetman et al., 2014).

LOs assist learners by giving them a good overview of the skills, knowledge, and/or competences to gain from the education (K3) (Dias, 2020). With my workshops this can give the prospective learner a clear view of the course, prior to applying, so they know if it is relevant for them (V1 & V2). Currently we have a brief explanation of the course. Including a bulleted list of learning outcomes can help set proper expectations of prospective learners.

I have previously found designing learning objectives for computational biology to not be straightforward. This is not uncommon with many computer science educators finding it difficult to determine the level of Bloom’s taxonomy to use (Masapanta-Carrión, & Velázquez-Iturbide, 2018). There are various evidence-informed recommendations to improve the creation of learning outcomes. One particularly interesting one I will use is the combined SOLO and Bloom’s taxonomy (V3) (Meerbaum-Salant et al., 2010). This taxonomy was specifically designed for computer scientists such as myself (K2). This utilises three of the five SOLO taxonomies; unistructural, multistructural, and relational. Within each of these are three subcategories from Bloom’s taxonomy; Understanding, Applying, and Creation.

An additional issue I find with LOs is when a learner does not reach the expected learning outcomes. This can lead to demotivation as LOs can make it seem like the failure is the student’s fault (V1). However, good learning outcomes that attempt to protect from loss of self-confidence can be created (Falout et al., 2009). I need to carefully consider what taxonomy level the training is intended to reach. Depending on the topic, as I do not purely teach computer science, I can use Bloom’s or the combined SOLO and Bloom’s taxonomy.

It is tempting to try and pick a higher complexity level so you feel you are giving the best value of teaching. However, higher complexity does not necessarily mean better and it is best to create a realistic learning outcome (K4). On the other hand, it is still useful to consider the higher levels of taxonomy. This can be used to try to develop deeper cognitive processing in learners where appropriate (Adams, 2015).

I am happy with my current teaching. There is still room for improvement and I aim to add LOs to my registration forms and materials (A5). I feel more confident using the SOLO and Bloom’s combined taxonomy than Bloom’s for my area of teaching. I will add a section in the workshop feedback forms to assess the usefulness and quality of the LOs I add (K5). I hope their incorporation will improve the learning experience and help with material creation in the future.

12.1.1 Citations

  1. Adams, N. E. (2015). Bloom’s taxonomy of cognitive learning objectives. Journal of the Medical Library Association: JMLA, 103(3), 152.
  2. Dias, D. (2020). Learning outcomes in European higher education. In The International Encyclopedia of Higher Education Systems and Institutions (pp. 1996-2000). Dordrecht: Springer Netherlands.
  3. Falout, J., Elwood, J., & Hood, M. (2009). Demotivation: Affective states and learning outcomes. System, 37(3), 403-417.
  4. Masapanta-Carrión, S., & Velázquez-Iturbide, J. Á. (2018, February). A systematic review of the use of bloom's taxonomy in computer science education. In Proceedings of the 49th acm technical symposium on computer science education (pp. 441-446).
  5. Meerbaum-Salant, O., Armoni, M., & Ben-Ari, M. (2010, August). Learning computer science concepts with scratch. In Proceedings of the Sixth international workshop on Computing education research (pp. 69-76).
  6. Sweetman, R., Hovdhaugen, E., & Karlsen, H. (2014). Learning outcomes across disciplinary divides and contrasting national higher education traditions. Tertiary Education and Management, 20(3), 179-192.
  7. Wächter, B. (2004). The Bologna Process: developments and prospects. European journal of education, 39(3), 265-273.

12.2 Effective onboarding

I have received very positive feedback from my observer. Katy is a member of the NEOF training subgroup so was able to observe the entire day’s session (9.30am-4pm). The main area for further development was to explain the use of the online html book and how to start after the presentation. I believe this ties into improving my onboarding to assist struggling learners (V1). There are a variety of methods I am aiming to use to do this. This includes adding a section at the end of the first presentation to set them up for the practical, and improving the stated goals and outcomes of the course.

Onboarding can be an effective method to prepare learners for the course. In online courses this can introduce the learners to the technologies to be used, the materials to be covered, and how assignments will be carried out. Effective onboarding can help student retention and the materials can be useful for their future reference (V2) (Milner & Price, 2021). Etiquette can be set to help prevent misunderstanding and conflicts in a multicultural group, improving inclusivity (V1) (Franz et al., 2021).

The online learning of bioinformatics can be halted by the various tools and software that need to be learnt such as Zoom, Slack, webVNC, RStudio, Juptyer notebook, etc. Primarily bioinformatic software packages that academics use are open-source. Unfortunately the majority of open-source software packages have a significant barrier to entry with steep learning curves, in part due to poor documentation (K1) (Ngo et al., 2021). To counteract demotivation at the start of the course I use user-friendly tools in the materials, where possible (A1, K4, & V3) (Franz et al., 2021). Additionally, I train the learners on how to use the tools (A2). This is seen as a core competency for all bioinformaticians (V4) (Walch et al., 2014).

Unfortunately I have not focussed on onboarding the materials. To improve on this I will give a demonstration of the online HTML book at the end of my first presentation (A5). I will show them different sections of it such as the theory, practice, interactive multiple choice questions, and exercises. In past courses I have found a small number of students have been confused as to how to start the practical. I have found this out by regularly asking students if they need assistance and by being able to check their progress on their virtual computer I have access to (A4 & K5).

More generally, to improve onboarding I will improve the introduction of the course. I will incorporate learning outcomes and goals into the presentation and materials to help elucidate the purpose of the course (Hayes, 2014). I will also use narrative to introduce the dataset being used in a way that invests the learners (K3) (McNett, 2016).

I am very happy with my feedback from my observer. However, I agree with their criticism of my onboarding. I have previously focussed on effective onboarding on the bioinformatic concepts and tools. I will now focus on improving the more general onboarding of the course. This will involve setting up the students to start practical sessions more smoothly, improving the learning goals, and giving better context to the biological datasets that are used through storytelling.

12.3 Citations

  1. Franz, L., Mählitz-Galler, E., & Herzog, M. (2021, October). Onboarding Challenges in Online and Blended Courses: Reviewing Virtual Cross-Country Collaboration of Student Teams in Higher Education. In ECEL 2021 20th European Conference on e-Learning (p. 164). Academic Conferences International limited.
  2. Hayes, S. (2014). A mixed methods study of shared epistemic agency in team projects in an online baccalaureate nursing course. State University of New York at Albany.
  3. McNett, G. (2016). Using stories to facilitate learning. College Teaching, 64(4), 184-193.
  4. Milner, P., & Price, M. (2021, July). All On Board: A Facilitated Onboarding Program Supporting Retention of Online Undergraduate Learners. In ANNUAL (p. 145).
  5. Ngo, C. J., Chang, J., & Chung, S. (2021). Decreasing the Barrier to Entry for an Open-Source Full-Stack Web Development. In Proceedings of the Conference on Information Systems Applied Research ISSN (Vol. 2167, p. 1508).
  6. Welch, L., Lewitter, F., Schwartz, R., Brooksbank, C., Radivojac, P., Gaeta, B., & Schneider, M. V. (2014). Bioinformatics curriculum guidelines: toward a definition of core competencies. PLOS computational biology, 10(3), e1003496