A Learning Analytics-informed Activity to Improve Student Performance in a First Year Physiology Course

2021 ◽  
Vol 29 (2) ◽  
Author(s):  
Mark Williams ◽  
Lesley Jan Lluka ◽  
Prasad Chunduri
Keyword(s):  
At Risk ◽  
Student Performance ◽  
Learning Analytics ◽  
Student Perception ◽  
Predictive Modelling ◽  
Performance Criteria ◽  
Targeted Interventions ◽  
Improve Student Performance ◽  
Short Answer Question ◽  
Outcome Performance

Learning Analytics (LA) can be employed to identify course-specific factors that hinder student course (outcome) performance, which can be subsequently rectified using targeted interventions. Supplementing interventions with predictive modelling also permits the identification of students who are at-risk of failing the course and encourages their participation. LA findings suggested that a targeted intervention for our course should focus on improving student short answer question (SAQ) performance, which we attempted to achieve by improving their understanding of features pertaining to various SAQ answer standards and how to achieve them using examples of varying scores. Every student was invited to the intervention via a course-wide announcement through the course learning management system. At-risk students identified using predictive models were given an additional invitation in the form of a personalised email. Results suggest that intervention improved student understanding of SAQ performance criteria. The intervention also enhanced student end-of-semester SAQ performance by 12% and 11% for at-risk and no-risk students respectively. Course failure rate was also lower by 26% and 9% among at-risk and no-risk intervention participants. Student perception of the intervention was also positive where an overwhelming majority of participants (96%) found the interventional activity to be useful for their learning and exam preparations.

Role of Machine Learning in Modern Education and Teaching

2021 ◽  
pp. 99-123
Author(s):  
Latika Kharb ◽  
Prateek Singh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Student Performance ◽  
Student Perception ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Improve Student Performance ◽  
Student Models ◽  
Performance Teachers ◽  
The Impact

Computers are being utilized in field in education for many years. In last few decades, research within the field of artificial intelligence (AI) is positively affecting educational application. Advanced machine learning and deep learning techniques could be used for extracting knowledgeable information from crude information. In this chapter, the authors have analysed the impact of artificial intelligence in the education domain. The authors will discuss how with the development of machine learning techniques in last few decades, machine learning models can anticipate student performance. By learning about every student, models can identify the shortcomings. Then the authors will propose different approaches to improve student performance. Teachers can also use this model to understand student perception levels in a better way so that they can modulate their lectures according to student perception levels.

scholarly journals Redesigning a First Year Physiology Course using Learning Analytics to Improve Student Performance

2021 ◽  
Vol 3 (1) ◽  
pp. 4
Author(s):  
Mark T. Williams ◽  
Lesley Jan Lluka ◽  
Prasad Chunduri
Keyword(s):  
Higher Education ◽  
At Risk ◽  
Student Learning ◽  
Student Performance ◽  
Statistical Models ◽  
Learning Analytics ◽  
First Year ◽  
Learning Context ◽  
Assessment Tasks ◽  
Course Outcomes

Learning analytics (LA), a fast emerging concept in higher education, is used to understand and optimize the student learning process and the envi-ronment in which it occurs. Knowledge obtained from the LA paradigm is often utilized to construct statistical models aimed at identifying students who are at risk of failing the unit/course, and to subsequently design inter-ventions that are targeted towards improving the course outcomes for these students. In previous studies, models were constructed using a wide variety of variables, but emerging evidence suggests that the models constructed us-ing course-specific variables are more accurate, and provide a better under-standing of the learning context. For our current study, student performance in the various course assessment tasks was used as a basis for the predictive models and future intervention design, as they are conventionally used to evaluate student learning outcomes and the degree to which the various course learning objectives are met. Further, students in our course are pri-marily first-year university students, who are still unfamiliar with the learning and assessment context of higher education, and this prevents them from adequately preparing for the tasks, and consequently reduces their course performance and outcome. We first constructed statistical models that would be used to identify students who are at risk of failing the course and to identify assessment tasks that students in our course find challeng-ing, as a guide for the design of future interventional activities. Every con-structed predictive model had an excellent capacity to discriminate between students who passed the course and those who failed. Analysis revealed that not only at-risk students, but the whole cohort, would benefit from in-terventions improving their conceptual understanding and ability to con-struct high-scoring answers to Short Answer Questions.

An Exploratory Study of the Online Learning Environment

2013 ◽  
Vol 29 (1) ◽  
pp. 117-147 ◽  
Author(s):  
Cynthia J. Khanlarian ◽  
Rahul Singh
Keyword(s):  
Learning Environment ◽  
Student Performance ◽  
It Use ◽  
Environment Impact ◽  
Web Based ◽  
Improve Student Performance ◽  
Research Questions ◽  
The Impact ◽  
Relevant Factors ◽  
Significant Factors

ABSTRACT Web-based homework (WBH) is an increasingly important phenomenon. There is little research about its character, the nature of its impact on student performance, and how that impact evolves over an academic term. The primary research questions addressed in this study are: What relevant factors in a WBH learning environment impact students' performance? And how does the impact of these factors change over the course of an academic term? This paper examines and identifies significant factors in a WBH learning environment and how they impact student performance. We studied over 300 students using WBH extensively for their coursework, throughout a semester in an undergraduate class at a large public university. In this paper, we present factors in the WBH learning environment that were found to have a significant impact on student performance during the course of a semester. In addition to individual and technological factors, this study presents findings that demonstrate that frustration with IT use is a component of the learning environment, and as a construct, has a larger impact than usefulness on student performance at the end of a course. Our results indicate that educators may benefit from training students and engaging them in utility of co-operative learning assignments to mitigate the level of frustration with the software in the WBH learning environment and improve student performance.

The Value of Handwritten Notes: A Failure to Find State-Dependent Effects When Using a Laptop to Take Notes and Complete a Quiz

2020 ◽  
pp. 009862832097989
Author(s):  
Roni M. Crumb ◽  
Ryan Hildebrandt ◽  
Tina M. Sutton
Keyword(s):  
Academic Performance ◽  
Student Performance ◽  
Incongruent Condition ◽  
Note Taking ◽  
Sole Purpose ◽  
State Dependent ◽  
Improve Student Performance ◽  
Teaching Implications

Background: Many students use laptops in the classroom to take notes; however, even when laptops are used for the sole purpose of taking notes they can negatively impact academic performance. Objective: The current study examined state-dependent effects, and the potential for a match in note taking and quiz taking methods to improve quiz performance. Method: Participants were placed into a congruent (take notes by hand and complete the quiz by hand or take notes using a laptop and complete an online quiz) or an incongruent condition (take notes by hand and take an online quiz or take notes using a laptop and complete the quiz by hand). Results: The results revealed that participants who took notes by hand performed better on the quiz overall, and better on conceptual questions, then students who took notes using a laptop. We failed to find evidence for state-dependent effects. Conclusions: The current study suggests that taking notes by hand may improve how students encode material, and result in higher quality external storage used by students when studying for quizzes. Teaching Implications: Reinforcing the notion that taking notes by hand may benefit quiz performance for lecture-style information and could improve student performance in class.

scholarly journals Improving drug safety predictions by reducing poor analytical practices

2020 ◽  
Vol 4 ◽  
pp. 239784732097863
Author(s):  
Stanley E Lazic ◽  
Dominic P Williams
Keyword(s):  
At Risk ◽  
Drug Discovery ◽  
Drug Safety ◽  
Prediction Models ◽  
Predictive Modelling ◽  
Equal Weight ◽  
Negative Consequences ◽  
Preclinical Data ◽  
Safety Pharmacology ◽  
Patients At Risk

Predicting the safety of a drug from preclinical data is a major challenge in drug discovery, and progressing an unsafe compound into the clinic puts patients at risk and wastes resources. In drug safety pharmacology and related fields, methods and analytical decisions known to provide poor predictions are common and include creating arbitrary thresholds, binning continuous values, giving all assays equal weight, and multiple reuse of information. In addition, the metrics used to evaluate models often omit important criteria and models’ performance on new data are often not assessed rigorously. Prediction models with these problems are unlikely to perform well, and published models suffer from many of these issues. We describe these problems in detail, demonstrate their negative consequences, and propose simple solutions that are standard in other disciplines where predictive modelling is used.

Refining Learned Repertoire for Percussion Instruments in an Elementary Setting

2006 ◽  
Vol 54 (3) ◽  
pp. 231-243 ◽  
Author(s):  
Donald M. Taylor
Keyword(s):  
Music Education ◽  
Student Performance ◽  
Teaching Effectiveness ◽  
Dependent Measure ◽  
Upper Elementary ◽  
Elementary Music ◽  
Orff Schulwerk ◽  
Rehearsal Strategies ◽  
Percussion Instruments ◽  
Improve Student Performance

The purpose of this study was to examine teaching effectiveness in an elementary music setting using student achievement as a dependent measure. Because Orff Schulwerk instruction is one of the most prevalent pedagogies in elementary music education, this study examined the rehearsal strategies of recognized Orff Schulwerk teachers as they worked to refine learned repertoire for percussion instruments. Eight instructors and their upper elementary students were videotaped in four regular rehearsals each. Systematic analyses of rehearsal frames in which teachers were working to improve student performance revealed fast teacher pacing and a predominance of instructional directives that were procedural (e.g., where to begin playing) rather than musical (e.g., how to perform more accurately or expressively). The majority of students' performance problems were related to precision, often caused by rushing the underlying pulse. Instructional targets were most often related to technique. Students successfully accomplished proximal goals in 63 % of the performance trials in which the targets were verbalized by the teacher prior to performance and in 74 % of the performance trials when the targets were verbalized by the teachers while students were playing. Students were most successful when teachers used clear, explicit directives and positive modeling.

scholarly journals The Impact of Social Media & Messaging Application Utilization to the Learning Effectiveness (Higher Education Learning Effectiveness Through Social Media & Messaging Application)

2019 ◽  
Vol 9 (2) ◽  
pp. 2112-2116
Keyword(s):  
Social Media ◽  
Student Performance ◽  
Learning Process ◽  
Positive Impact ◽  
Research Result ◽  
Research Unit ◽  
Learning Effectiveness ◽  
Improve Student Performance ◽  
Media Messaging ◽  
The Impact

Indonesian internet users reached 143,26 Million in 2017, most of them used internet for accessing messaging and social media application. We argue that usage of messaging and social media can give positive impact to the learning process. Our research method using questionnaire to collect data, research conduct in Private University in Jakarta, and student as our research unit analysis. The second year’s research shows that optimization of social media application and messenger services to improve student performance can be done by knowing the most common social media application and messenger services that used by student, socialize the process to increase number of participation, utilization of features of the application, continuous improvement, and communication about method’s success story that can attracts lecturer and students to apply and keep improve the more effective method and learning process. This research result can be use by the lecturer or educator to improve education through social media application and messenger.

scholarly journals First Year Student Perception and Experience of Online Topographical Anatomy Laboratory Classes using Zoom Technology during the COVID-19 Pandemic

2021 ◽  
Vol 29 (3) ◽  
Author(s):  
Rudi Klein ◽  
Chiara Tomassoni ◽  
Gayathri Rajaaman ◽  
Maxwell Winchester ◽  
Norman Eizenberg ◽  
...  
Keyword(s):  
Student Performance ◽  
Learning Experience ◽  
Student Perception ◽  
Visual Learning ◽  
Functional Anatomy ◽  
Gross Anatomy ◽  
First Year ◽  
Topographical Anatomy ◽  
Anatomy Laboratory ◽  
Student Grades

During semester one of 2020, the units ‘Functional Anatomy of the Trunk’ and ‘Functional Anatomy of the Limbs’ which focus on human topographical anatomy were re-designed into an online delivery format and taught remotely in response to the COVID-19 lockdown. It was expected that the move to remote teaching would negatively impact student perception and learning experience, in particular that of the cadaver-based laboratory work. The aim of this study was to investigate whether the replacement of traditional face-to-face cadaver-based anatomy laboratories with an online version using digital anatomy resources and Zoom technology as the communication platform would achieve comparable student learning experience and outcomes. First Year Students (n=69) enrolled in these units were invited to participate in this study and were asked at the conclusion of each unit to complete an anonymous opinion-based survey via Qualtrics. The Qualtrics data, student grades and Learning Management System (LMS) statistics were analysed. Results indicate that student perception of the online gross anatomy laboratory learning was positive and that it had complemented their learning. Most students agreed that as a visual learning resource, it provided an improved understanding of anatomy and helped with the application of anatomical knowledge. Interestingly, student performance showed a similar range of marks compared with previous years. However, students strongly agreed that the online 2D learning experience had significant limitations when compared to live use of cadavers in laboratories.

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