Month: February 2021

Time spent preparing for a lesson and my performance in a lesson.

The data that I have collected is subjective, and many other factors need to be considered when evaluating this data and taking further actions. For instance, I gave a grade for my personal performance in each lesson. However, multiple factors could have influenced my decision. Why did I evaluate one class as a six and another one as a seven? Is there a clear distinction? I have not thought thoroughly about the meaning of a specific grade before collecting data. This needs to be improved in the future, and clear criteria for each specific grade mean need to be established.

Key finding: Does time spent preparing for a lesson influence my performance in a lesson? No

A small face represents each lesson that I had this week. Every speech bubble represents how much time did I spend preparing for a lesson. There is no clear relationship between these two factors. It seems that lesson success involves more things than just spending time getting ready for it.

Visualization design choices: faces were chosen to illustrate how I evaluated my performance. Smile illustrated high performance, while sad faces low. The colour of the lines of speech bubbles showed how much time do I spend preparing. The green colour represented that I spent a high amount of time preparing when red showed the opposite. Also, I was trying to represent my data, not as a dashboard, because it can often harm education (Brown, 2020).

This type of data might be useful for every teacher to evaluate how much time they spend preparing for lessons and whether it really influences their performance. It can reveal ‘truths’ about education (Beer 2019, cited by Williamson et al. 2020). Maybe spending their time developing other things would be more beneficial? Such as deploying new technologies and becoming ‘data literate’ (Williamson et al. 2020)?

However, it is important that such information would be collected by instructors themselves because otherwise, it can seem like surveillance (Brown, 2020). Often learning analytics intervene in education in counterproductive ways. Tools need to be aligned with practitioners’ values and views.

References

Brown, M. (2020). Seeing students at scale: How faculty in large lecture courses act upon learning analytics dashboard data. Teaching in Higher Education, 25(4), 384-400.

Williamson, B., Bayne, S. & Shay, S., (2020). The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in higher education, 25(4), pp.351–365.

Visualization 3. How do I spend my time?

This visualization tries to assess the amount of time I spend learning. It helped me to learn about my own learning.

More than 50% of the time I spend teaching

It is a key way how I receive my income. However, data is not neutral, and my personal imprint needs to be considered (Knox et al., 2019). I collected data from Wednesday to Sunday. If I had gathered it from Monday to Sunday, the percentage would have been probably even higher.

Almost 25% of the time I spend learning

I expected a lower percentage of my time. It surprised me. Learning is highly important to innovate and make sophisticated decisions (Wiggins, 2018, cited by Eynon, 2015).

Visualization design choices: bars were chosen to show a clear difference in time spent on different activities. Colours were chosen for bars based on how much do I enjoy while doing a particular activity. Enjoyment plays a crucial role in the continuation of activity (Stevens, 2000, p. 601).

Key finding: fourth of my time, I spend learning. This is a sufficient percentage, and my concentration might need to be shifted towards physical activities or personal needs.

Learning with data

Collecting data about personal activities can help find necessary data related to learning (Eynon, 2015, p. 407) and encourage conversation between students and facilitators, which is crucial in learning (Stocchetti, 2020). It can provide the necessary feedback (Ifenthaler & Erlandson, 2016, p.1). My third visualization shows the necessary feedback to me about where I spend my time.

Not all findings from data can be linked to learning. What parts of experiences, processes, and outcomes can be related to learning need to be carefully assessed (Eynon, 2015, p. 408). For instance, my own physical activities analysis showed that it has little or no influence on learning.

Using numbers in a very complex field such as learning can be challenging. It can result in many different consequences for individuals. For example, it can diminish creativity (Beach & Dovemark, 2009), or encourage learners to concentrate on data rather than learning itself (Wise et al. 2013, cited by Eynon, 2015). Therefore, various factors need to be carefully considered (Eynon, 2015). For instance, how motivation or enjoyment can get a specific value in machine learning models? Black-boxes (Tsai et al., 2020) can not be left. Various legal and ethical issues need to be addressed too (Eynon, 2015). It was not an issue for me because I was analyzing my own data.

There are multiple advantages from data that can be used for better learning, such as enhanced motivation, additional support, informed learning choices, and enhanced meta-cognition (Eynon, 2015). The past three weeks’ visualizations helped me to realize that physical activities do not have a significant effect on my learning and that I spend a sufficient amount of time learning.

However, there are multiple disadvantages too: closing down creativity or alternative ways of learning, changing self-concept, shaping educational opportunities. Various stakeholders need to be aware of it (Tsai et al., 2020). I have experienced changing self-concept regarding the relationship between physical activities and learning. I do trust data, however, a critical evaluation of data and its visualizations are essential! Data literacy is an important skill (Knox et al., 2019).

References

Beach, D. & Dovemark, M. (2009). Making ‘right’ choices? An ethnographic account of creativity, performativity and personalised learning policy, concepts and practices. Oxford Review of Education, 35(6), 689-704.

Eynon R. (2015). The quantified self for learning: critical questions for education, Learning, Media and Technology, 40:4, 407-411, DOI: 10.1080/17439884.2015.1100797

Ifenthaler, D. & Erlandson, B.E. 2016. Learning with Data: Visualization to Support Teaching, Learning, and Assessment, Technology, Knowledge and Learning, vol. 21, no. 1, pp. 1-3.

Knox, J., Williamson, B. & Bayne, S. (2019). Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies, Learning, Media and Technology, 45(1), pp. 1-15.

Perry D Wiggins. (2018). Metric of the Month: Learning Days Per Employee. CFO.com, CFO.com, 2018-09-10.

Stevens, M et al., (2000). The Groningen Enjoyment Questionnaire: A measure of enjoyment in leisure-time physical activity. Perceptual and motor skills, 90(2), pp.601–604.

Stocchetti, M. (Ed.). (2020). The Digital Age and Its Discontents: Critical Reflections in Education. Helsinki University Press. doi:10.2307/j.ctv16c9hdw

Tsai, Y., Perrotta, C., & Gašević, D.. (2020). Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics. Assessment and Evaluation in Higher Education, 45(4), 554-567.

In this week’s visualisation, I tried to go deeper into the relationship between physical activity outside and learning. I tried to assess the influence of physical activities’ intensity on the relationship.

Does the intensity of physical activities outside influence my willingness to study? Not clear

It is hard to tell due to the fact that some days I felt more motivated to study after a hard workout, while during other days it even had a negative effect and my motivation went down due to physical fatigue (Yu et al., 2006, p.331).

Does the intensity of physical activities outside influence my engagement in course activities? No

It clearly can be seen that the physical activity intensity does vary between Wed/Thur/Fri. However, engagement varies. Various factors need to be considered. Data is not neutral, and the imprint of producer needs to be assessed (Knox et al., 2019). For instance, I leave my course activities at the end of the week. Most of my high-intensity physical activities are at night.

Visualisation design choices: lines where chosen to clearly show the intensity. The green colour was chosen to show a positive relationship between physical activities and learning. Green is often associated with positivity (Akers et al., 2012).

Physical intensity has little/or no influence on my learning.

References

Akers, A., Barton, J., Cossey, R., Gainsford, P., Griffin, M., & Micklewright, D. (2012). Visual Color Perception in Green Exercise: Positive Effects on Mood and Perceived Exertion. Environmental Science & Technology, 46(16), 8661-8666.

Knox, J., Williamson, B. & Bayne, S. (2019). Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies, Learning, Media and Technology, 45(1), pp. 1-15.

Yu, C. C. W, Chan, Scarlet, Cheng, Frances, Sung, R. Y. T, & Hau, Kit-Tai. (2006). Are physical activity and academic performance compatible? Academic achievement, conduct, physical activity and self-esteem of Hong Kong Chinese primary school children. Educational Studies, 32(4), 331-341.