Improving Study Outcomes for College Students With Executive Functioning Challenges

Abstract

Over the past decade, there has been an increase in postsecondary programs seeking to meet the needs of students with high incidence disabilities (e.g., learning disability, attention deficit hyperactivity disorder [ADHD]). Many of these students experience difficulties with executive functioning, that is, effectively applying problem-based strategies to set and obtain goals. There is limited research to enhance academic performance and retain this population throughout their college experience. This study investigated the use of a task analysis and goal setting intervention for improving the study skills and overall task completion by three college students with executive functioning challenges. Results demonstrated a functional relation between the intervention and dependent variables. Suggestions for future research and implications for practice are discussed.

Keywords

executive functioning single subject design task analysis goal setting learning disability

Some college students, especially those with disabilities (e.g., learning disability [LD], attention deficit hyperactivity disorder [ADHD]), lack executive functioning (EF) skills needed to solve problems and achieve goals. EF involves a range of skills such as inhibition, attention, working memory, emotional regulation, planning, time management, self-monitoring, and goal setting (Barkley, 2012). Misconceptions suggest that difficulties with EF result from low intelligence and low persistence, or affect only students with LDs and ADHD; however, many individuals use EF strategies (e.g., planning, attending to important information) to navigate their lives and may struggle with EF depending on task demands or the frequency with which these tasks occur (Meltzer, 2011). Deficits in EF skills can greatly affect students’ academic performance such as studying, taking exams, and writing term-papers (Grieve, Webne-Behrman, Couillou, & Sieben-Schneider, 2014; Petersen, Lavelle, & Guarino, 2006).

Most of the literature on EF has focused on school-aged students (“Addressing Executive Functioning at the Secondary Level,” 2011; Diamond & Lee, 2011) and has resulted in recommendations such as using computerized training to enhance working memory, physical activity, mindfulness training, and goal management training (GMT; Davis et al., 2011; Diamond & Lee, 2011; Duncan, 1986; Levine et al., 2000). Other research has highlighted the importance of EF skills with college students (Petersen et al., 2006). College students who struggle with EF skills often experience stress and higher levels of distraction due to the inability to self-regulate and engage in independent, purposeful, self-serving behaviors affecting their learning (“Addressing Executive Functioning at the Secondary Level,” 2011). Students who have challenges with EF may not know how to seek help, or even that they can seek help.

In addition to cognitive-based interventions (e.g., enhancing working memory), behavior-based interventions such as self-determination instruction (Wehmeyer & Palmer, 2000), self-management, self-monitoring (Cooper, Heron, & Heward, 2007), task analysis, and systematic instruction (Collins, 2012) have been used to improve academic outcomes for students with disabilities. Task analysis and systematic instruction, through prompting, are commonly used strategies in Applied Behavior Analysis (Cooper et al., 2007). In a task analysis, larger skills are broken down into smaller, manageable steps (e.g., identifying what to study, getting materials ready, and taking notes from a textbook).

Prompting tactics such as a system of least to most prompts (Collins, 2012) allow an instructor to provide a series of prompts from least to most intrusive (e.g., verbal prompt, modeling, physical prompt). Such prompting systems have been used successfully with students with disabilities and have an established evidence base (Spooner, Knight, Browder, & Smith, 2011). These prompting strategies have been largely used for students with more significant intellectual disability; however, the use of a task analysis has been used to teach self-determination skills to students with LD preparing to transition to postsecondary settings. For example, Durlak, Rose, and Bursuck (1994) used a checklist or task analysis, which included seven skills of self-determination. Throughout the intervention, students with LD learned skills such as stating their disability and identifying what accommodations they needed. Tasks were broken into manageable steps, and systematic instruction was used to successfully teach the new skills. In like manner, these skills may be taught using similar strategies for students already in postsecondary settings.

Currently, few studies have examined how to improve skills that could alleviate EF difficulties for students with disabilities in universities and colleges. Stress and anxiety exacerbate EF difficulties and can affect college students’ academic performance (Petersen et al., 2006). In study sessions, for example, college students need to take strategic action to plan what they will accomplish during their study time, monitor themselves as they are working, and evaluate the study session when time has ended. Similar to GMT (Levine et al., 2000) and self-determination instruction (Wehmeyer & Palmer, 2000), task analysis and goal setting can help students set and attain goals. Teaching study skills to students with EF challenges who may also be identified with ADHD or LD is imperative considering the number of students with disabilities attending universities or colleges has increased over the past few decades (Parker & Boutelle, 2009). Students who lack these skills may experience lower retention and achievement compared with students without disabilities (Parker & Boutelle, 2009). As a result, strategies to improve college students’ academic performance are greatly needed. Therefore, the purpose of this study was to determine the effects of using a task analysis and goal setting intervention to improve three university students’ study skills and determine if improvement of these skills would lead to improved task completion. Furthermore, the study sought to investigate student perceptions of how the intervention impacted their study skills.

Method

Participants

Participants were enrolled in a 4-year university in the Southeastern United States, which had a total enrollment of above 28,000 students. The university offered a support program (Advancement; pseudonym) that served college students with documented high incidence disabilities (e.g., LD). In addition, this program was a part of a larger state network whose focus was on improving transition and retention of students with disabilities across state universities. The Advancement program offered services such as assistive technology (e.g., Livescribe pens), tutors/mentors, and participation in an online retention communication tool. The program accepted up to 10 new students graduating from high school each year, and at the time of the study, there were over 50 students enrolled. Enrollment into the Advancement program included completing an application, an interview process, submission of transcripts, a psychological evaluation, an individualized education program or 504 plan, and having an identification of one or more of the following learning challenges: dyslexia, dysgraphia, dyscalculia, and ADHD. Once formally accepted, students could select any major provided by the university as long as they obtained the proper grade point average (GPA) and met the requirements of their selected major. Average GPA for all students at the time of the study was a 2.95 (range = 1.91–3.73). Moreover, all students were registered with the university’s office of disability services.

Three young adults, one male and two females (pseudonyms used throughout), all of whom exhibited EF challenges as documented in recent psychological evaluations, participated in this study. Further selection criteria for participants included (a) participation in a postsecondary program designed for students with high incidence disabilities within the university, (b) difficulties with organizational skills and staying on task, (c) a history of being identified by professors as having difficulty in their courses (i.e., flagged through a university early alert system), and (d) experiencing academic difficulties within at least one course. Participants had identified LD in the areas of not otherwise specified (NOS) and dyscalculia. A diagnosis of NOS indicated deficits in auditory, visual, or processing speed, or any combination of these.

The first participant, Mary, was 20 years old, held a sophomore academic status at the time of the study, and was majoring in Child Care. Mary struggled academically with goal setting during her university study hall time. She exhibited characteristics associated with EF difficulties particularly in the areas of planning, time management, self-regulation, and prioritizing. Although Mary often had difficulties completing assignments, she had no issues asking for help when she felt overwhelmed or did not understand something.

Caitlyn, the second participant, was 22 years old at the time of the study and in her senior year at the university, majoring in Early Childhood Education. Caitlyn struggled academically with planning, time management, goal setting, self-regulation, and attention. She was often distracted during academic work, and frequently did not finish tasks during study hall and turn in assignments. Caitlyn exhibited characteristics associated with EF difficulties in the areas of working memory, inhibitory control (self-control), planning, and time management thus making studying and completing assignments difficult.

Finally, Arthur, the third participant, was 21 years old at the time of the study majoring in Recreation and Leisure Studies. He held a sophomore academic status. Arthur’s main challenges were with attention and task completion. He struggled during most study hall sessions to complete assignments and often felt that he had completed a lot of work, when in fact he had not. Arthur was very social and was often distracted during study hall.

Setting

The Advancement program was housed in a large space that included offices and a study hall specifically designated on the second floor of the university’s main library. This space could be used only by students in the program between 7:00 a.m. and 5:00 p.m. Afterward, other university students were also allowed to use the room. Study sessions for this project were conducted in the study hall. The study hall was large and included an open floor plan with rectangular desks around the edges of the walls and small couches and lounge chairs toward the center of the room. Seven offices and study rooms were also part of the study area. In addition, the space provided students a small kitchenette and a utility closet that included various work materials (e.g., notebooks, highlighters, smart pens). Participants came to study hall to work on course-related assignments and had the choice to sit anywhere they felt comfortable (e.g., rectangular desk along the wall, study room). Baseline and all intervention probes occurred within various locations of the study hall with data collectors sitting by participants, either beside or in front depending where participants decided to sit. Study sessions lasted for approximately 1 hr per session for 2 or 3 days a week depending on student schedules.

Materials

A study-planning sheet, task analysis, iPod touch™, data collection sheets, writing utensils, a MacBook Pro™, and other student study materials (e.g., course textbooks, worksheets) were used throughout sessions. Participants were also assigned a mentor, one of two graduate assistants who also served as data collectors, to assist students with daily study tasks. Mentors met with students at the beginning of each week to plan out study objectives for the remainder of the week. It is important to note that before the study began, as a part the Advancement program protocol, participants already had mentors assigned to them as supports to assist them throughout courses.

During baseline and intervention, all students were first given a study-planning sheet, which required for them to write their name, date, and assigned mentor. Participants were asked to write down their objectives for their study session on this document. Mentors served a facilitating role during study sessions and provided assistance as necessary to participants. An 11-step student task analysis was used during intervention to help students plan, monitor, and reflect on their study session. Moreover, an iPod touch™ was used to help students keep track of time during their study session. An alarm was set to sound off at the mid-point of the study session (see Step 6 of the task analysis). Depending on individual study sessions, students brought with them the necessary study materials for their session (e.g., laptop, textbook, notebooks, handouts, etc.). Finally, a MacBook Pro™ and an Excel file were used to create a graph to help monitor student performance, a step included within the task analysis. At the end of each intervention session, students would plot their performance within the premade excel document.

Data Collection

Dependent variables

The primary dependent variable was the percentage of steps correct on an 11-step task analysis for study skills (see Figure 1) developed during the previous year’s pilot study. Throughout all probes, a response was counted as correct if students independently completed a step in the task analysis. A step was counted as incorrect if students did not perform the step or required prompting. To prevent learning during baseline, a single opportunity format (Collins, 2012) presentation was used. During this process, once a student failed to complete a step in the task analysis, the session was terminated but the student was still allowed to continue the study session. During intervention, a total task presentation format (Collins, 2012) was used to teach the sequence of steps in the task analysis. If participants completed a step in the task analysis incorrectly or omitted a step, a mentor prompted the student to complete the step and proceeded to the next sequence in the task.

Figure 1.

Task analysis/student goal setting checklist.

The secondary dependent measure was the percentage of tasks completed during each study session. At the beginning of each study session, participants had to determine the number of tasks they would complete during each session. Tasks could consist of an array of items and were unique to each participant’s study session. Examples of tasks could include reading pages from a text, completing a quiz, writing a paper, and/or studying for an exam.

Interrater reliability

Mentors and the first author served as data collectors for the entire study. Because mentors served as primary data collectors, interrater reliability data were collected at least once a week across conditions. Reliability data for student responses (dependent measure) were taken for 33% of baseline and 20% of intervention sessions for Mary, and 25% of baseline and 20% of intervention sessions for Caitlyn and Arthur. Interrater reliability was determined by using an item-by-item score. The following formula was used to calculate interrater reliability: number of agreements divided by the number of agreements plus the number of disagreements multiplied by 100 (Kennedy, 2005). Interrater reliability was 100% across baseline and intervention sessions for all participants.

Social validity

At the end of the study, students were given a questionnaire to determine the social importance of the intervention. The questionnaire consisted of six questions utilizing a Likert-type scale (i.e., 5 = strongly agree, 4 = agree, 3 = neutral, 2 = disagree, and 1 = strongly disagree) to determine student satisfaction with the intervention. The questionnaire took students less than 5 min to complete.

Experimental Design

A multiple probe across participants design (Kennedy, 2005) was used to evaluate the effects of a task analysis and goal setting intervention to teach study skills to young adults with EF challenges. Baseline data were first collected across all students for a minimum of three data points or until baseline data were stable. Intervention then began with Mary as her performance demonstrated a stable baseline and had the most difficulty completing tasks during this phase. Once Mary met mastery criteria (80% across three consecutive sessions), then all students were provided another probe to demonstrate continued baseline stability. Once baseline data were stable, a second student entered intervention and the procedure was repeated for the final student. This process allowed for a demonstration of experimental control on the acquisition of steps learned and for the ability to demonstrate a functional relation between the independent and dependent variables.

Procedures

General procedures

Prior to the study, students participated in a mentoring program designed to assist them in organizing weekly tasks from their university courses. Students met with a mentor at the beginning of each week and used a daily planner to prioritize these tasks. Mentors made themselves regularly available to students for assistance and tutoring sessions. Students continued in this mentoring program during the course of this study. Mentors served as primary interventionists and assisted with observations and data collection. Before the study began, mentors were trained on how to use the 11-step task analysis (see Figure 1) and collect procedural fidelity and interrater reliability for both baseline and intervention phases. Mentors participated in an hour-long training session that included modeling and role-playing activities led by the first author. During these activities, mentors collected data, calculated reliability and fidelity scores, and compared notes. Data collected during activities were reviewed and training ceased once mentors were able to score 100% on procedural fidelity and on interrater reliability.

Baseline

During baseline, students were asked to sit in an area of their choosing within the study hall. Mentors then gave students a study-planning sheet and read a script to them:

I’d like to start off by asking you to review this planning sheet and list out some tasks you want to complete by the end of the study session. Think about a place you want to start, where you want to be mid-way through your session, and where you want to end. You can use this sheet to organize your planning process. Feel free to write as much as you want and then proceed with your study session.

During this process, no additional supports were provided to students in assisting them to plan for their study session. Mentors used the 11-step task analysis to monitor student study progress and used the planning sheet to track the number of tasks students set out to complete for the specified study session. No assistance or prompts were provided during baseline and a single opportunity format was used for data collection. If a step in the task analysis was not completed, data collection ceased and the student continued with the remainder of the study session. At the conclusion of study sessions, mentors collected the study-planning sheet and reviewed it with participants to determine how many tasks were completed.

Task analysis and goal setting intervention

Before beginning intervention, the first author trained each student to use the task analysis by modeling each step during a student’s 1-hr study session. For instance, he modeled a step and then had the student complete the step with him. During intervention, students were given their study-planning sheet, an iPod touch™, the task analysis, and a MacBook Pro™ with a premade graph using an Excel document. After this initial training session, subsequent sessions began with students first meeting with their mentors to plan out their tasks (Step 1 of the task analysis). Mentors only acted as facilitators during this process and did not tell students what tasks to complete for the session. Mentors followed the same script as well during baseline and provided guidance or asked questions to assist students through the planning stage (e.g., What can you accomplish within this study hour? What do you think you need to focus on today? What do you mean by this particular task?). Next, students identified a starting point, mid-point, and ending point for their study session (Steps 2–4). Once these areas were determined, a mentor set an alarm that would signal the mid-point of the study session. For instance, if the study session was an hour long, the alarm was set to signal at 30 min into the session. Throughout this period, students self-monitored their study skills and checked off steps within the task analysis (Steps 5–8). For example, when the alarm signaled, students determined what task they were working on during their session and marked whether they had made it to their mid-point within the task analysis. At the end of the session, students completed reflective questions concerning if they felt they did well during the study session, if they completed the tasks they set out to complete, and what they could have done better during the session (Step 9). Next, students graphed the number of steps and tasks completed using the Excel document on a MacBook Pro™ to help them monitor their performance over time (Step 10). Finally, students met with their mentors to address and discuss any questions about tasks completed or items that could be worked on for the next study session. Mastery was considered at 80% across three consecutive sessions at which point the intervention was supposed to end. If students and mentors were interested, they could continue using the intervention past the mastery criterion.

A system of least to most prompts (Collins, 2012) was used to help students complete steps of the task analysis. A prompting hierarchy of verbal, verbal + gesture, and verbal + model was created. Prompts were only used if a student did not complete a step within the task analysis. If a step was omitted or not checked off by a student, a mentor provided the least intrusive prompt and marked the step as incorrect. Students never needed more than a verbal prompt and, in most cases, only needed a verbal prompt for Steps 9, 10, or 11 of the task analysis.

Procedural reliability

Like interrater reliability, fidelity on the use of the task analysis protocol was collected for 33% of baseline and 20% of intervention sessions for Mary, and 25% of baseline and 20% of intervention sessions for the remaining participants. Procedural fidelity scores were 100% across baseline and intervention sessions for all participants.

Results

Figure 2 shows data for Mary, Caitlyn, and Arthur across conditions of the study. Each student increased his or her percentage of completed steps of the task analysis and percentage of goals/tasks completed during the intervention. Despite some overlapping data, particularly for Session 7 for Mary, 12 for Caitlyn, and 11 for Arthur, all students were able to meet mastery criteria.

Figure 2.

Student data.

Mary

During baseline, Mary completed 45% of steps within the task analysis across three sessions. The mean of goals completed for study sessions during this period was 33% with a range of 0% to 66%. During intervention, Mary completed a mean of 95% of steps within the task analysis (range = 75%–100%) and increased the percentage of tasks completed with a mean of 86.6% (range = 33%–100%). During Session 7, Mary reported that she was “not having a good day” and attributed this to her lower performance.

Caitlyn

During baseline, Caitlyn completed, on average, 38.4% of steps within the task analysis (range = 18%–45%). The mean of goals completed for study sessions during this period was 49.8% (range = 33%–66%). During intervention, Caitlyn completed a mean of 96.4% of steps within the task analysis (range = 82%–100%) and increased the percentage of goals completed with a mean of 86.6% (range = 33%–100%). Like Mary, Caitlyn reported personal reasons for her lower performance on Session 12.

Arthur

During baseline, Arthur completed a mean of 29.3% of steps within the task analysis (range = 0%–54%) and completed a mean of 16.5% (range = 0%–66%) of goals across study sessions. During intervention, Arthur completed a mean of 87.2% of steps within the task analysis (range = 64%–100%) and completed a mean 86.6% of his goals (range = 33%–100%).

Social Validity

Students agreed that mentor consultations were helpful (M = 5); setting goals before they studied helped them be more productive (M = 4); the task analysis was helpful in guiding them to complete their goals (M = 4.7); the intervention helped them stay on task (M = 4); and they would be interested in using this intervention for help in future courses (M = 4.7). One student commented that being “asked what I needed to get done helped me think. I had a tangible list on what I had to get done. It helped me a lot to make a to-do list.”

Discussion

The purpose of this study was to evaluate the effects of a task analysis and goal setting intervention on the study skills of college students with EF challenges. Results of this study indicated a functional relation and marked improvement in goals and steps completed throughout intervention. Furthermore, students reported the intervention was helpful in managing their study time and helping them ensure completion of self-selected goals. This study extends the literature in several ways. First, it provides evidence that systematic instructional strategies can be used to facilitate goal training for college students with EF challenges. Similar to Levine et al. (2000), which solely used errorless learning in combination with GMT, this study used task analytic instruction and incorporated many of the same principles in the GMT procedures. For instance, in GMT, students first identify a problem, define goals, break down the steps to meet those goals, work toward set goals, and monitor one’s behavior. Similar procedures were used in this study. Students had mentors who facilitated the development of goals for study sessions. Students then defined goals to be completed and used the task analysis and self-monitoring to help them stay on task and aid them in completing their goals. Through this process, students monitored their own behaviors and also graphed their progress for further analysis and reflection. If students failed to complete a specific task, a system of least to most prompts was used with prompts naturally faded over time. Research has long supported the use of systematic instructional strategies for students with intellectual disabilities (Spooner et al., 2011); however, the explicit and systemic nature of such instruction can also benefit students with high incidence disabilities as demonstrated in this study. Although it was not the intent of this study to use the exact procedure in GMT, this study demonstrates that adapted versions of this procedure may also be beneficial with similar populations. Finally, this study exemplifies the value of students monitoring their own performance through self-graphing their behaviors. In a review of literature, Briesch and Chafouleas (2009) found that many self-management interventions often rely on a form of self-graphing or charting, which has positive benefits for students with and without disabilities, across various grade levels and behaviors. Participants in this study found graphing their performance advantageous, particularly Arthur. At one point in the study, after charting and reviewing his graph, Arthur noted his performance was not as good as he initially thought and verbally stated, “I can do better next time.” These moments of self-evaluation may further contribute to personal goal achievement.

Limitations and Directions for Future Research

Although all students showed marked improvements in this study, there are limitations that warrant suggestions for future research. For instance, the use of mentors could be considered a limitation because most college students do not have access to mentors who have explicit training and are readily available to them as provided within the Advancement program. In this study, mentors were always in the designated study hall and prepared to assist students without those students seeking out or advocating for a mentor. Although the program included mentors as a support system, it may have been beneficial to fade mentor assistance over time to determine if students could develop goals independently and complete the task analysis without assistance. Future research should evaluate similar trainings and determine if students can complete similar tasks independently. A second limitation to this study was the lack of generalization and maintenance data. Due to time restraints, the study was conducted in the latter part of the semester providing a limited window for data collection. Future research should examine if students could successfully use similar interventions in other settings and over time. For instance, Caitlyn demonstrated a decrease in performance in Sessions 12 and 13. Additional data collection would have been beneficial to determine maintenance of the skill and whether or not she would have benefited from additional instruction. Another limitation is that there are no data to determine the efficacy of the intervention related to improvements in academic performance (e.g., exam grades, final grades). Although this was not within the scope of the proposed research questions, understanding the relationship among goal setting, study skills, and overall academic performance is important. Future researchers should consider examining the extent to which task analysis and goal setting affect particular course assignments or final grades. Finally, researchers should consider conducting a component analysis. The intervention used multiple components (task analysis, a prompting procedure, self/progress monitoring through graphing data, student reflections) and as such should be examined to determine which components are most needed to ensure student success.

Implications for Practice

With the increase in programs, similar to, designed to assist students with LD, it is important to examine the evidence base of interventions that can be used to ensure success when transitioning from high school to higher education. As teacher support diminishes and academic competition increases, it can be difficult for students with learning differences and EF challenges to establish sure footing in college settings. Students in this study had mentors to help them navigate their study sessions; however, data showed that students’ performance did not improve until the task analysis and goal setting were added to their study sessions. Suggesting that mentors utilizing a task analysis to teach specific study skills, allowing students to organize their own study sessions, and having them reflect on their performance is more effective. Practitioners and researchers should seek ways to train university tutors and mentors to use research-based strategies that seek to increase students’ goal setting and self-determination. Teaching students to set goals, systematically follow steps to improve study skills, and monitor their progress can improve their academic success in higher education and perhaps positively increase retention rates.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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