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Integration of learning analytics in blended learning course at a University of Technology
| Content Provider | Semantic Scholar |
|---|---|
| Author | Naidoo, Kristie Naidoo, Richard |
| Copyright Year | 2016 |
| Abstract | The main purpose of this study is to use Learning Analytics to improve the instructional design in an undergraduate Mathematics Education I course. Students enrolled in the course come from varying backgrounds. The Learning Analytics will improve the flexibility of the course and provide a platform to understand misconceptions experience by the students. The integration of computational aspects is necessary to illuminate teaching learning and assessment in a Blended Learning setting. A Blended Learning model is used to teach first year undergraduate mathematics education I course at the School of Education, Durban University of Technology. Students were taught a course in mathematics using a Learning Management System. Data is constructed using items from the discussion forum on Black Board and a post assessment task given to 170 first year Mathematics Education I students. Four levels of Learning Analytics: descriptive, diagnostic, predictive and prescriptive are used to discuss the data set. Activity theory is used as theoretical framework. Mixed methods were used to analyse the quantitative and qualitative data. Student errors from the post-test are categorised as cognitive errors with structural errors, arbitrary errors and executive errors to establish links with pre-knowledge frames and concept representation. The structural errors indicate that the representation of concepts is necessary in the content design of the course. Results show that there are more structural errors than executive and arbitrary errors. Introduction Using Blended Learning in an undergraduate mathematics course provides opportunities to improve the instructional design to help students minimise errors. This study focuses on errors in basic trigonometry using a Blended Learning format. The essential elements of the Integration of Learning Analytics in Blended Learning Course at a University of Technology Kristie Naidoo, Richard Naidoo Reaching from the roots – 9th EDEN Research Workshop Proceedings, 2016, Oldenburg 271 ISBN 978-615-5511-12-7 learning involve a didactic contract referred to by (Gür, 2009) in a study at Turkey on Trigonometric Learning. The study applies learning analytics to examine the students understanding of the concepts. Learning Analytics Analytics is the process of discovering, analyzing, and interpreting meaningful patterns from large amounts of data (Jindal, 2015). Analytics is usually defined, in practice as any fact-based deliberation which leads to insights (diagnostics) and possible implications for planning future action in an organizational set up (Banerjee et al., 2013) Descriptive analytics provide a rich data source to measure, compare and improve individual performance. A Learning Management System (LMS) affords functionality to follow or trace student activities and capture data sets to help improve the learning experience. (Norris et al., 2009) is of the view that analytics that besides using quantitative analysis, the qualitative view will provide additional insight to aid the design of the course offering. Diagnostic analytics provide relevant data to on why students experience these types of errors and misconceptions. In a Mathematics education course such data will allow the designer of a course to alert students of certain obstacles in their learning path early enough to motivate to correct them and improve their effort. Prescriptive analytics refers to what should be done about such errors and misconceptions. Blended Learning strategies offer other opportunities to assist students in their effort to minimise errors. Predictive analytics are used forward planning. (Raj, 2014) suggests that analytics can be used for favourable planning using a combination of data about who, what, where, and when and analyzing why and how. Predictive analytics give a glimpse into the future. It can be used to make changes to course content based on data from the descriptive and diagnostic analytics Figure 1 is adapted from a business model (Banerjee, 2013) Figure 1. Types of Learning Analytics Integration of Learning Analytics in Blended Learning Course at a University of Technology Kristie Naidoo, Richard Naidoo 272 Reaching from the roots – 9th EDEN Research Workshop Proceedings, 2016, Oldenburg ISBN 978-615-5511-12-7 Activity Theory Framework The Blended Learning model is conceptualised using the Activity Theory (AT) (Vygotski, 1962). This is an artefact-mediated and object-oriented model. Research shows (Barab et al., 2004) and (Karasavvidis, 2009), that AT can be used as theoretical and an analytical framework for examining design and development of technology-supported humancomputer interaction, and online and blended learning communities. The six component of an Activity System, (Engestrom, 1993) are subject, object and related outcomes, mediating tools and artefacts, community or communities, division of labour and rules. In our project the subject is the student or class group from the MTMC 101 undergraduate course. The object and the related outcomes are the actual online material the student examines in this course and what the material intends to achieve, how these activities transform the student or class group. The online tools, learning resources and conceptual theory used to facilitate the mediation between subject and object. The community is the individual student or the class group. Division of labour: All members of the community do all aspects of the work. The rules are all the implicit regulations, norms and standards that regulate the activity within the system. (Russel, 1997) describes an activity system as “any ongoing, object-directed, historicallyconditioned, dialectically-structured, tool-mediated human interaction”. A schematic representation of the Activity System used in this project is given below: Figure 2. Model of activity system (Adapted from Engeström, 1987; p.187) Integration of Learning Analytics in Blended Learning Course at a University of Technology Kristie Naidoo, Richard Naidoo Reaching from the roots – 9th EDEN Research Workshop Proceedings, 2016, Oldenburg 273 ISBN 978-615-5511-12-7 This triangular structure suggests that at any given point any two elements are mediated by another element in the system. For example to get to the object, the subject and community are mediated by the artefacts and rules. Cognitive Frame Theory A frame is an abstract formal structure that is stored in memory and somehow encodes and represents a sizeable amount of knowledge. This collection of knowledge representation structures or frames grows as more complex frames are built on the existing ones. We focus on the sequential processes which guide mathematical problem solving activity, the critique which is an information processing operator that is capable of detecting certain of frames, information in one’s mind must be typically organized into quite large chunks (Davis & Mc Knight, 1979; Minsky, 1975). Minsky (1975) states “when one encounters a new situation one selects from memory a substantial structure called a frame. This is a remembered framework to be adapted to fit reality by changing details as necessary”. Davis (1984; pp.276-7) lists six possible frame selection procedures: • Bootstrapping – deals with what one sees in the given. It leads to certain associations, frames that involve such things; • Not knowing too much – deals with the limited knowledge on a topic or concept; • Focus on some key cue – deals with the presence of a small number of cues that lead to the retrieval of some specific frame; • Using context – deals with how the context influences student’s choice; • Using systematic search – deals with the student learning things in a systematic way and develops systematic procedures for searching his/her memory; • Parameter-adjusting or spreading activation – deals with how certain frames or assimilation patterns acquire high expectation values. Categorisation of Errors Errors in trigonometry can be categorized as structural errors, executive errors and arbitrary errors as described by Donaldson (1963). Structural errors arise from a failure to appreciate the relationships involved in a problem or group of principles essential to the solution of the problem. Failure to tackle relationships in a problem arises from a false expectation of the problem. Structural errors may arise in connection with variable interaction. These errors occur in the deductive mode when the subject reasons deductively but fallaciously. One may expect that failure to perceive inconsistency or consistency would be a common source of structural error (Donaldson, 1963). An incorrect frame may be retrieved or the frame maybe not developed adequately. Structural errors are caused by incorrect frame retrieval, sketchy or incomplete frames, deep-level procedures and sub-procedures. The second type of error is the executive error. Executive errors occur when there is a failure to carry out manipulations, although the principles may have been understood. Some defect of concentration, attention or immediate memory lie at their origin. The most prevalent of Integration of Learning Analytics in Blended Learning Course at a University of Technology Kristie Naidoo, Richard Naidoo 274 Reaching from the roots – 9th EDEN Research Workshop Proceedings, 2016, Oldenburg ISBN 978-615-5511-12-7 this class of errors is loss of hold on reasoning (Donaldson, 1963). A correct frame maybe retrieved but a sub-frame responsible for calculations maybe underdeveloped. The third type of error is the arbitrary error. Arbitrary errors are those in which the subject behaves arbitrarily and fails to take account of the constraints laid down in what was given. These are errors which have as their outstanding common feature a lack of loyalty to the given. Sometimes the subject appears to be constrained by knowledge of what is “true” by some considerations drawn from “real-life” experience. Sometimes there is no constraint of |
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| Alternate Webpage(s) | https://openscholar.dut.ac.za/bitstream/10321/2416/1/Naidoo_EDEN%202016_Oldenburg_Conference%20Proceedings_Pg270-278_2016.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
