Tag Archives: higher education

Type: Evidence | Proposition: A: Learning | Polarity: | Sector: | Country:

Learning analytics makes extensive use of trace data from learners interacting with Learning Management Systems (LMS), and one of the most common uses is to derive an estimate of the time each learner spent on task, that is, engaging in particular learning activities. The authors of this paper note that while this approach is widely used, the details are often not given, and the consequences of choices made in generating these measures are not explored.

They present two experiments exploring different measures of time on task, one using data from a fully-online course at a Canadian university, and another using data from a blended course at an Australian university.

They found that time-on-task measures typically outperformed count measures for predicting student performance, but more importantly, identified that the precise choice of time-on-task measure "can have a significant effect on the overall fit of the model, its significance, and eventually on the interpretation of research findings".

Given their findings, they argue that there are implications for research in learning analytics: "Above all is the need for more caution when using time-on-task measures for building learning analytics models. Given that details of time-on-task estimation can potentially impact reported research findings, appropriately addressing time-on-task estimation becomes a critical part of standard research practice in the learning analytics community. This is particularly true in cases where time-on-task measures are not accompanied by additional measures such as counts of relevant activities."

They call for researchers to recognise the importance of time-on-task measures, for fuller detail to be given of the measures used, and for further investigation.

(This paper is an expanded version of an earlier paper at LAK15, which reported only on the data from the Canadian university: Kovanovic, Vitomir, Gasevic, Dragan, Dawson, Shane, Joksimovic, Srecko, Baker, Ryan S, & Hatala, Marek. (2015). Penetrating the black box of time-on-task estimation. Paper presented at LAK '15, Poughkeepsie, NY. DOI 10.1145/2723576.2723623.)

Citation: Kovanović, V., Gašević, D., Dawson, S., Joksimović, S., Baker, R. S., & Hatala, M. (2015) Does Time-on-task Estimation Matter? Implications on Validity of Learning Analytics Findings. | Url: https://epress.lib.uts.edu.au/journals/index.php/JLA/article/view/4501

Type: Evidence | Proposition: A: Learning | Polarity: | Sector: | Country:

This literature paper briefly anticipates and outlines a project about integrating labour market information in a learning analytics goal-setting application, with the aim to help student to develop skills that are currently requested by the labour market.

Authors highlight the absurd contemporary trend that characterise labour market, with high levels of youth unemployment together with difficulties of companies to find candidates with the right job skills.

In the developed IT solution, abour market data will be analyzed to extract information that may impact student planning. This will lead to a "goal-setting program in which students can specify goals (e.g., learning goals as subgoals of more distant career goals)" and have access to "relevant labour market information, view progress and success indicators, and receive recommendations from course advisors".

According to authors, "this research expands the impact of learning analytics beyond the educational setting by helping students to navigate the education-to-employment pathway using a goal-setting application" characterized by analytical methods joint with an examination of learning constructs and educational theories.

Citation: V. Kobayashi, S.T. Mol, G. Kismihók (2014). Labour Market Driven Learning Analytics. Journal of Learning Analytics, 1(3), 207-210 | Url: https://epress.lib.uts.edu.au/journals/index.php/JLA/article/view/4194

Type: Evidence | Proposition: A: Learning | Polarity: | Sector: | Country:

In recent years, one of the main issues for companies in hiring new employees consists in the bridge between skills provided by HE and the ones required by a more and more fast and agile market. This is why it is particular important for students to identify as soon as possible what can be their career fields, taking into consideration natural attitudes and personal wishes.

In this scientific paper, authors describe the development of a tool, or a Portal, to improve career readiness of students during Higher Education. IT tools like this are particularly interesting, because gives to companies the potential capability to have visibility on the areas of interests of the best graduates, and contacting them soon as they finish their studies, as well as compare these attitudes with workplace results and skill improvements.

This Portal consists of three major processes:

1) Career Readiness

2) Career Prediction

3) Career Development.

Career Readiness is formed  by different software modules which allows to measure the general professional dispositions required for a successful career in the 21st Century (21st Century Skills). These professional dispositions have been divided in 6 macro-areas, which in general describe  "the natural tendencies, mind state and preparations of each individual towards a professional practice": Openness to challenge, Critical Thinking, Resilience, Learning Relationships, Responsibility for Learning, and Creativity.

Career Prediction is calculated on some indicators, which are transformed in "Raw scores", that allows to cluster individuals and compare them with the features of different Community of Practice (people who share knowledge, experiences and passion on the same set of topics).

Career Development, which allows people to join different Communities of Practice and build their career domain awareness and skills.

The  tool integrates Learning Analytics techniques for career readiness "by focusing on meta-learning dimensions that accompany formal education while positioning learners and instructors at the center of the analytics process". Learning Analytics allows to provide solutions to help learners to reflect and act upon feedback about their learning performance.  Furthermore, Learning Analytics reveals hidden patterns of common traits among learners viewed as future candidates of the job market.

Citation: AbuKhousa, E.; Atif, Y., "Big learning data analytics support for engineering career readiness," in Interactive Collaborative Learning (ICL), 2014 International Conference on , vol., no., pp.663-668, 3-6 Dec. 2014 doi: 10.1109/ICL.2014.7017849 | Url: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7017849&punumber%3D7002490%26sortType%3Dasc_p_Sequence%26filter%3DAND(p_IS_Number%3A7017737)%26pageNumber%3D4

Type: Evidence | Proposition: C: Uptake | Polarity: | Sector: | Country:

There is a large body of research suggesting that the amount of time spent on learning can improve the quality of learning, as represented by academic performance. The widespread adoption of learning technologies such as learning management systems (LMSs) has resulted in large amounts of data about student learning being readily accessible to educational researchers. One common use of this data is to measure time that students have spent on different learning tasks. Given that LMS systems typically only capture times when students executed various actions, time-on-task measures can currently only be estimates.

This paper takes five learning analytics models of student performance, and examines the consequences of using 15 different time-on-task estimation strategies. It finds that choice of estimation strategy can have a significant effect on the overall fit of a model, its significance, and the interpretation of research findings.

The paper concludes

  1. The learning analytics community should recognize the importance of time-on-task estimation and the role it plays in the quality of analytical models and their interpretation
  2. Publications should explain in detail how time on task has been estimated, in order to support the development of open, replicable and reproducible research
  3. This area should be investigated further in order to provide a set of standards and common practices for the conduct of learning analytics research.

This was selected as the best paper at the Learning Analytics and Knowledge conference 2015.

Citation: Kovanovic, Vitomir, Gasevic, Dragan, Dawson, Shane, Joksimovic, Srecko, Baker, Ryan S, & Hatala, Marek. (2015). Penetrating the black box of time-on-task estimation. Paper presented at LAK '15, Poughkeepsie, NY. DOI 10.1145/2723576.2723623 | Url: http://dl.acm.org/citation.cfm?id=2723576

Type: Evidence | Proposition: D: Ethics | Polarity: | Sector: | Country:

Approaches taken to understand the opportunities and ethical challenges of learning analytics necessarily depend on a range of ideological assumptions and epistemologies. This paper proposes a socio-critical perspective on the use of learning analytics. Such an approach highlights the role of power, the impact of surveillance, the need for transparency and an acknowledgment that student identity is a transient, temporal and context-bound construct. Each of these affects the scope and definition of the ethical use of learning analytics. The authors propose six principles as a framework for a number of considerations to guide higher education institutions to address ethical issues in learning analytics and challenges in context-dependent and appropriate ways.

Citation: Slade, Sharon, & Prinsloo, Paul. (2013). Learning analytics: ethical issues and dilemmas. American Behavioral Scientist.