ABSTRACT
Students have different learning styles and preferences to help them learn and understand course material. Current course structures in education create restrictions on how a class session can be designed, which makes it difficult, if not impossible, to create a course that encompasses all of these students’ needs because of the complexity of accounting for each of these differences. To help overcome this intricacy, this research attempts to understand how competency-based courses related to application development and programming can utilize a more personalized approach. This was explored by using an application called eAsel. To understand competency and personalization, interviews were conducted with university instructors and students to understand how students learn, how teaching is handled in the classroom, and how programming course within the college could transition to implement a personalized course flow in eAsel. During these interviews, the participants were able to interact with eAsel through a guided walkthrough. Observations of students were also conducted in a two-hundred level programming course as well as during common tutoring hours hosted by the college to test eAsel’s student activity record page which helps to score student understandings of course competencies based on a five-point scale.
Overall, at the conclusion of this research study, there were positive comments toward implementing the application into the classroom for programming courses in the College of IST. Through eAsel, both professors and students are able to more easily understand the structure of the course, identify resources that they can use to learn the material they are still having a difficult time understanding, and demonstrate how well students understand course material through a rating system. By being able to assess students in real-time, both the students and professors can have faster feedback of how students can improve to master a certain competency. However, there are also challenges identified within eAsel. For instance, class size can affect the efficiency of recording data and the ability to individually communicate with each student to build a personal connection. The effectiveness of the application also varies based on how it is implemented in the classroom, including how many and which resources are available to students, how much time is devoted to the application in and out of the classroom, and how professors are able to motivate students to reach mastery of each course competency. Although eAsel can be implemented into these classes, there are still some looming questions surrounding what this would look like within the classroom and how it would affect the current course structure. These questions must be answered before implementation can take place.
Chapter 1
INTRODUCTION
In the past several decades, researchers have gained a better understanding of different learning styles for students which help them to better succeed within the classroom (Hummel, Manderveld, Tattersall, & Koper, 2003; Keefe, 1987). These learning styles can be based on motivation, prior learning, previous experiences, and learning processes (Keefe, 1987; Vickers, Field, & Thayne, 2016). With so many different preferences and, on top of this, the combination of preferences, it becomes extremely difficult, if not impossible, to create a tailored teaching pedagogy that enables all students to succeed in the classroom. Although there are various styles in which students learn, the current educational system does not account for many of these, but research has shown how the adaption of teaching methods can help these students to better learn in the classroom (Franzoni, Assar, Defude, & Rojas, 2008). Instead, the current educational model teaches for the population mean of learning styles and negates other learning styles that do not fit into this mold and has a set structure to teaching, learning, and evaluating courses (Haynes, 2017). Because of this, many students find it difficult to succeed within the classroom. To fix this, there are several steps needed. One of which is to understand the student learning experience by working to identify what mechanisms for learning are present and which are missing by changing the focus from teacher-centered to student-centered structures.
Within the educational system, there is also a lack in understanding of how students learn, what tools are commonly used, and how the course material is reviewed to understand the necessary information. At The Pennsylvania State University (PSU), there have been some small strides that allow faculty members to better gauge student interaction with the course material through the adoption of
Canvas LMS, which allows professors to track progress through different features in the gradebook tool
(Instructure, 2018). Although this enables staff to have improved understanding of student learning, there are still holes in this system inhibiting professors’ abilities to fully understand the learning process. Canvas also has a feature, Outcomes, which allows professors to track student mastery of the course and monitor student proficiency of course concepts based on a four-point scale (Instructure, 2019; University of Michigan’s Information and Technology Services, 2018). However, professors within the University rarely use this feature. On top of this, there is a disconnect in professors’ understanding what materials students used in combination with the scores they received within the units of course material over the course of the semester.
MOTIVATIONS
During my undergraduate and graduate experience, I was able to work as a College of Information Sciences and Technology (IST) tutor as well as becoming an Instructional Assistant for several programming and mathematics courses.
After several semesters of working with students during office hours and tutoring hours, I worked
with different representatives within the College of IST to write a report that discussed areas within the programming courses where students have the most difficulty in understanding specific course topics and competencies (Servich & Mahon, 2016). These findings were shared with the faculty and staff of these courses. When discussing these learning difficulties, many of the professors were aware of several of the highlighted issues and have tried to account for them by spending more time and energy on these topics with little to no improvement in student grades. After these conversations, I began to wonder if there were other external factors that could cause this disconnect between teaching and understanding.
After giving this report, and for several semesters after, I looked back at the past four and a half years that I have worked as an Instructional Assistant and tutor, and the interactions that I had with students. Over time, I have realized that these students not only have varying levels of understanding for programming and mathematics, but they also prefer and employ different learning processes when studying. Some students learn from walking through previously worked programs, others need to examine the different course readings, and others prefer to have different analogies associated with their interests, such as football. While helping these students, I found that I have to constantly change my own teaching styles to best support these students’ individual learning needs.
From this, I realized that, because each student has a preferred learning method, it can be difficult for a professor to adequately address all of these preferences in a classroom of seventy-five, which ultimately hampers some of these students’ ability to have an effective learning experience. Because of this, some students may become isolated and have lower performance than students whose learning preferences are met within the classroom.
Many of the students that I helped through my tutoring and Instructional Assistant positions value the ability to have learning methods that are not PowerPoint lectures or confusing book readings that list fully completed programs that the student is unable to decipher. Exercises suggested by students to help their learning experiences are stepping through example problems, diagramming code or conceptual ideas, breaking down the problem set into smaller chunks that are more manageable to focus on, understanding the background information of why a certain piece of code or process is important, and having one-on-one or small group discussions about course material.
Kharb, Samanta, Jindal, and Singh (2013) found that not only did medical students have preferences between the various types of learning experiences (i.e., visual, auditory, read-write, and kinesthetic) that can be taught through different avenues—for instance, practical/dissection, self-study, lecture, or tutorial—but that there are different combinations of these preferences that benefit different students, which can range from one to four methods for each. However, since there are so many moving parts with individual learning styles, course structure, the restraints of the course subject area, and more, understanding how to make a better classroom environment for all students is an arduous task, especially because the answer is not concrete.
CONTRIBUTIONS
The ultimate goal of this research is to be able to implement a tool, called eAsel (eAsel, 2019), into Information Technology courses within the College of Information of Sciences and Technology (IST) at The Pennsylvania State University. This tool will be able to better track and manage how students learn course material and complete overall course objectives. Professional staff will be able to better track what processes are used by students within this application so that they can better understand how students learn, what material students are struggling with, and how to overcome these struggles. On the student end of the application, learning will be more personalized. This is done by allowing students to choose as many or as little of the activities necessary to complete the current concept that they must master. This process happens by breaking down course objectives into smaller course competencies, then completing as many or as little of the learning objectives as needed before being assessed and certified in the competency. This process continues until the completion of the course (see Figure 1-1). This process is not meant to take the place of current course processes, but instead aid the students and professors as another resource for the course. By focusing on these areas, students will hopefully gain greater comprehension and retention of course material and take this knowledge for the future in course assignments, courses, and internships. This may in turn create greater success rates within the classroom as more students effectively learn and apply course material.
Figure 1-1: Personalized Learning Approach for Programming and Application Development Courses (Haynes, 2017).
Ultimately, the courses that this will be implemented in are the competency-based learning courses within the technology field where the material from a current lesson builds on the knowledge that was learned in previous ones. Many of the courses that follow this style are programming-based courses, mathematics courses, and networking courses.
At a high level, this research will hopefully help to better understand the processes that students use to learn course material and what courses could work to implement this framework to help students succeed within the classroom.
STRUCTURE OF THESIS
This thesis examines how eAsel, a personalized learning environment, can help students of different learning preferences succeed within programming courses and analyze what tools students need to succeed in the classroom while professors determine what data they need to better help students in these classes. To do this, this thesis is broken up into nine chapters. The first focuses on the interests and motivations to study this subject area as well as potential contributions to the field. The second chapter examines previous research that has been conducted in personalized learning and competency-based courses. The third chapter discusses the eAsel tool and its capabilities. The fourth chapter describes the study design used to examine this topic. The fifth through seventh chapters explore the findings from this research. The eighth chapter discusses and analyzes the collected data. The ninth chapter concludes the thesis and presents possible future work for this field of study.
EXPLORING COMPETENCY-BASED EDUCATION IN AN APPLICATION DEVELOPMENT CURRICULUM