Dr Amara Atif is a Scholarly Teaching Fellow in the School of Information, Systems and Modelling within the Faculty of Engineering and IT, University of Technology Sydney, Australia. She was awarded with one of the most prestigious and highly competitive Macquarie University APA scholarship and completed her PhD in Learning Analytics. She is a conscientious, ambitious and highly motivated teacher. As a PhD graduate in Information Systems, she is always keen to explore the new horizons of different knowledge areas related to information systems. As an experienced academic and a competent researcher, she pride herself on her strong organisational skills and considerable skills to interact with the international students having diverse background, culture, skills and languages. She always enjoy creativity and stepping outside her comfort zone and experimenting things with her teaching methodology.
- Associate Fellow of Higher Education Academy UK (AFHEA)
- Member of Association for Information Systems (AIS)
Learning and Teaching:
- Flexible Learning at Macquarie - FLaME, Macquarie University, Sydney, Australia (2013)
- Foundations in Learning and Teaching - FILT, Macquarie University, Sydney, Australia (2012)
- Management of Change - CM, Executive Diploma from MSM - Maastricht School of Management, The Netherlands (2008)
- Software Quality Management - CSQP, Certified Software Quality Professional, Diploma from National University of Science & Technology (NUST) in collaboration with Pakistan Institute of Quality Control (PIQC), Islamabad, Pakistan (2002)
Awards and Grants:
- I was awarded with Vice-Chancellor’s Citation for Outstanding Contributions to Student Learning Award for developing and leading an innovative learning analytics solution that has influenced and enhanced learning and teaching and the student experience in November 2017 (PhD).
- I was awarded with Faculty of Arts Learning & Teaching Award for being involved in the design, development, and implementation of a learning analytics tool (Moodle Engagement Analytics Plugin-MEAP) that supports learning and teaching at Macquarie University in June 2017 (PhD).
- I was awarded with one of the most prestigious and highly competitive Macquarie University PGRF grant under the Post Graduate Research Fund scheme in September 2015 (PhD).
- I was awarded with one of the most prestigious and highly competitive Macquarie University APA scholarship in January 2013 (PhD).
- I won the People’s Choice Award for the best poster titled “Towards an Ontology-Based Approach to Knowledge Management of Graduate Attributes in Higher Education” in FoS-Faculty of Science, Research Conference held in September 2011 at Macquarie University (MPhil).
- I was awarded with one of the most prestigious and highly competitive Macquarie University PGRF grant under the Post Graduate Research Fund scheme in October 2011 (MPhil).
- I was awarded a fully funded fellowship from NUFFIC, The Netherlands, under the head of Netherlands Fellowship Programme in 2008 to travel to The Netherlands and attend an Executive Course held for mid-level career professionals titled "Management of Change".
Can supervise: YES
- Learning Analytics and Educational Data Mining
- Educational Technology
- Student Engagement
- Information and Knowledge Management
- Management of Change
- Technology Acceptance/Adoption of Information Systems
I reviewed papers for conferences such as:
- Australasian Conference of Undergraduate Research (ACUR 2013)
- European Conference on Information Systems (ECIS 2013)
- Computer Supported Cooperative Work in Design (CSCWD 2014)
- Information Systems
- Business Intelligenc/Data Analytics
- Database Design and Management
Froissard, JC, Liu, D, Richards, D & Atif, A 2017, 'A learning analytics pilot in Moodle and its impact on developing organisational capacity in a university', ASCILITE 2017 - Conference Proceedings - 34th International Conference of Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education, ASCILITE, ASCILITE, Toowoomba, QLD, pp. 73-77.
© ASCILITE 2017 - Conference Proceedings - 34th International Conference of Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education.All right reserved. Moodle is used as a learning management system around the world. However, integrated learning analytics solutions for Moodle that provide actionable information and allow teachers to efficiently use it to connect with their students are lacking. The enhanced Moodle Engagement Analytics Plugin (MEAP), presented at ASCILITE2015, enabled teachers to identify and contact students at-risk of not completing their units. Here, we discuss a pilot using MEAP in 36 units at Macquarie University, a metropolitan Australian university. We use existing models for developing organisational capacity in learning analytics and to embed learning analytics into the practice of teaching and learning to discuss a range of issues arising from the pilot. We outline the interaction and interdependency of five stages during the pilot: technology infrastructure, analytics tools and applications; policies, processes, practices and workflows; values and skills; culture and behaviour; and leadership. We conclude that one of the most significant stages is to develop a culture and behaviour around learning analytics.
Liu, DYT, Richards, D, Dawson, P, Froissard, JC & Atif, A 2016, 'Knowledge acquisition for learning analytics: Comparing teacher-derived, algorithm-derived, and hybrid models in the moodle engagement analytics plugin', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 183-197.View/Download from: Publisher's site
© Springer International Publishing Switzerland 2016. One of the promises of big data in higher education (learning analytics) is being able to accurately identify and assist students who may not be engaging as expected. These expectations, distilled into parameters for learning analytics tools, can be determined by human teacher experts or by algorithms themselves. However, there has been little work done to compare the power of knowledge models acquired from teachers and from algorithms. In the context of an open source learning analytics tool, the Moodle Engagement Analytics Plugin, we examined the ability of teacher-derived models to accurately predict student engagement and performance, compared to models derived from algorithms, as well as hybrid models. Our preliminary findings, reported here, provided evidence for the fallibility and strength of teacher-and algorithm-derived models, respectively, and highlighted the benefits of a hybrid approach to model-and knowledge-generation for learning analytics. A human in the loop solution is therefore suggested as a possible optimal approach.
Atif, A, Richards, D & Bilgin, A 2015, 'Student preferences and attitudes to the use of early alerts', 2015 Americas Conference on Information Systems, AMCIS 2015.
Learning analytics is receiving increased attention because it offers to assist higher education institutions in improving and increasing student success by automating the identification of at-risk students, thereby enabling interventions. While learning analytics research has focused on detection and appropriate interventions, such as early alerts, there has been little investigation of student attitudes and preferences towards receiving early alerts. In this paper, we report the results of a study involving three first year units that sought to determine the opinions and preferences of students on their attitudes towards the interventions; how to best contact students; their academic issues; type(s) and quality of communication with the teaching staff; and types of university services required and received. We found that the majority of students did want to be alerted, preferred to receive alerts as soon as performance was unsatisfactory, and strongly preferred to be alerted via email, then face-to-face then phone.
Liu, D, Richards, D, Froissard, C & Atif, A 2015, 'Validating the effectiveness of the moodle engagement analytics plugin to predict student academic performance', 2015 Americas Conference on Information Systems, AMCIS 2015.
Given the focus on boosting retention rates and the potential benefits of pro-active and early identification of students who may require support, higher education institutions are looking at the data already captured in university systems to determine if they can be used to identify such students. This paper uses historical student data to validate an existing learning analytics tool, the Moodle Engagement Analytics Plugin (MEAP). We present data on the utility of the MEAP to identify students 'at risk' based on proxy measurements of online activity for three courses/units in three different disciplines. Our results suggest that there are real differences in the predictive power of the MEAP between different courses due to differences in the extent and structure of the learning activities captured in the learning management system.
Liu, DYT, Atif, A, Froissard, JC & Richards, D 2015, 'An enhanced learning analytics plugin for Moodle: Student engagement and personalised intervention', ASCILITE 2015 - Australasian Society for Computers in Learning and Tertiary Education, Conference Proceedings, ASCILITE, ASCILITE, Perth, Australia, pp. 180-189.
© ASCILITE 2015 - Australasian Society for Computers in Learning and Tertiary Education, Conference Proceedings.All right reserved. Moodle, an open source Learning Management System (LMS), collects a large amount of data on student interactions within it, including content, assessments, and communication. Some of these data can be used as proxy indicators of student engagement, as well as predictors for performance. However, these data are difficult to interrogate and even more difficult to action from within Moodle. We therefore describe a design-based research narrative to develop an enhanced version of an open source Moodle Engagement Analytics Plugin (MEAP). Working with the needs of unit convenors and student support staff, we sought to improve the available information, the way it is represented, and create affordances for action based on this. The enhanced MEAP (MEAP+) allows analyses of gradebook data, assessment submissions, login metrics, and forum interactions, as well as direct action through personalised emails to students based on these analyses.
Atif, A, Richards, D & Bilgin, A 2013, 'A student retention model: Empirical, theoretical and pragmatic considerations', Proceedings of the 24th Australasian Conference on Information Systems.
© Atif, Richards and Bilgin 2013. This research-in-progress paper draws on an extensive body of literature related to student retention. The purpose of this study is to develop a student retention model utilising student demographic data and a combination of data from student information systems, course management systems and other similar tools to accurately predict academic success of students at our own institution. Our research extends Tinto’s model by incorporating a number of components from Bean’s, Astin’s and Swail’s model. Our proposed eclectic model consists of seven components, identified as determinants of student retention. The strength in the model lies in its ability to help institutions work proactively to support student retention and achievement. The proposed research methodology to be used in this study is “a mixed-methods concurrent triangulation strategy”. The results are expected to indicate which of the factors are most important in developing an information system to predict and suggest interventions to improve retention.
Atif, A, Richards, D, Bilgin, A & Marrone, M 2013, 'Learning analytics in higher education: A summary of tools and approaches', 30th Annual conference on Australian Society for Computers in Learning in Tertiary Education, ASCILITE 2013, pp. 68-72.
© 2013 Amara Atif, Deborah Richards, Ayse Bilgin, Mauricio Marrone. Higher education institutions recently have been drawing on methods from learning analytics to make decisions about learners’ academic progress, predictions about future performance and to recognise potential issues. As the use of learning analytics in higher education is a relatively new area of practice and research, the intent of this paper is to provide an overview of learning analytics including a summary of some exemplar tools. Finally we conclude the paper with a discussion on challenges and ethical issues.
Atif, A & Richards, D 2012, 'A technology acceptance model for unit guide information systems', Proceedings - Pacific Asia Conference on Information Systems, PACIS 2012.
Curriculum mapping is an important task in implementing, embedding and monitoring the knowledge, skills and attributes that graduates must acquire in their program of study. Curriculum mapping ensures correspondence between learning outcomes, learning activities and assessments. To aid in performing this complex task, many higher education institutions are using unit/study guide tools or curriculum mapping tools. These tools may be known under different names in different institutions but we will refer to these tools as unit guide information systems. To evaluate the utilisation and acceptance of these tools, this research-in-progress paper draws on an extensive body of literature related to technology acceptance that includes social cognitive theory and model of PC utilization to explain the influence of perceived usefulness and perceived ease of use. Our research extends the technology acceptance model by incorporating the external variables of self-efficacy, anxiety and social influence. The results are expected to indicate which of the external factors are most important in predicting and explaining attitude and intention to use unit guide information systems.
Atif, A, Busch, P & Richards, D 2012, 'Towards an ontology-based approach to knowledge management of graduate attributes in higher education', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 229-243.View/Download from: Publisher's site
© Springer-Verlag Berlin Heidelberg 2012. Knowledge around graduate attributes (GAs) is an area in need of knowledge management strategies. GAs are the qualities and skills that a university agrees its students should develop during their time with the institution. The importance of GAs and ensuring they are embedded and assessed is widely accepted across higher education. This research paper uses Grounded Theory and Network Maps to gain insights into the issues of similarities and differences in the discourse around our sample universities. To cover these two perspectives, we had two researchers involved in data analysis, one with the goal of distilling key ideas and identifying similarities and the other with the goal of untangling and drawing out differences. There is no unified taxonomy of managing GAs. The motivation to create such ontology is to push the standardization process that will enable the connection among academic systems and improve educational workflows, communication, and collaboration between universities.
Atif, A, Richards, D & Bilgin, A 2012, 'Estimating non-response bias in a web-based survey of technology acceptance: A case study of unit guide information systems', ACIS 2012 : Proceedings of the 23rd Australasian Conference on Information Systems.
Surveys are mostly challenged by response rates. Among the various types of survey research, web-based (internet-based/electronic/online) surveys are commonly used for data collection for a geographically diverse population. In surveys with high/low response rates, non-response bias can be a major concern. While it is not always possible to measure the actual bias due to non-response there are different approaches and techniques that help to identify reasons of non-response bias. The aims of this paper are twofold. (1) To provide an appropriate, interesting and important non-response bias case study for future web-based surveys that will provide guidance to other Information Systems researchers. The case-study concerns an online-survey to evaluate a technology acceptance model for Unit Guide Information systems (UGIS). (2) To discuss how nonresponse bias in a web-based technology acceptance study of an information system (UGIS in this case) can be contained and managed. Atif, Richards and Bilgin © 2012.
Atif, A, Richards, D & Bilgin, A 2012, 'Predicting the acceptance of unit guide information systems', ACIS 2012 : Proceedings of the 23rd Australasian Conference on Information Systems.
Information Systems can play an important role in ensuring and improving the quality of education provided. However, lack of acceptance of these information systems and resistance of technology innovations by the end users limit the expected benefits of the system. This research attempts to identify the key determinants for the acceptance of the Unit Guide Information Systems (UGIS) in the Australian higher education sector. The technology acceptance model (TAM), social cognitive theory (SCT) and model of PC utilization (MPCU) are combined to provide a new framework for this analysis. Results of the study are consistent with the technology acceptance factors for explaining the behavioural intention of the academics. The study also shows the effects of application specific self-efficacy, application specific anxiety and social influence on the acceptance of UGIS. Implications of the results are discussed within the context of unit guides and curriculum mapping. Atif, Richards and Bilgin © 2012.