Persona: Academic Leadership

Overview:

IntelliBoard uses a persona-based approach to deliver customized data analytics solutions tailored to the unique needs of academic leadership. By focusing on the specific responsibilities and challenges that academic leadership face, IntelliBoard ensures that the most relevant data is available, helping academic leadership make informed decisions to enhance learner retention, engagement, and performance.

Academic Leadership refers to institutional leaders who guide and manage educational programs, faculty, and policies to promote academic excellence and innovation. Some commonly used titles include: Provost, Deans, Department Chairs, and School Directors.

Role Summary:

Academic leadership oversee academic programs, faculty performance, and learner outcomes at the institutional level. They are responsible for ensuring the quality of education, aligning academic programs with institutional goals, and managing resources effectively. Through data-driven insights and real-time reporting, academic leadership can make informed decisions that enhance program performance, allocate resources effectively, and promote a culture of continuous improvement.

Key Responsibilities:

• Monitor Overall Learner Performance and Retention:

  • Track overall learner engagement and performance throughout the semester.

  • Oversee risk identification strategies to support the retention of learners in academic programs.

• Evaluate Academic Programs and Courses:

  • Analyze course success rates, grade distributions, and participation trends across departments.

  • Determine which programs and courses are performing well and which need improvement.

• Assess Instructor Effectiveness:

  • Monitor instructor engagement, grading patterns, and course activities to ensure consistency and effectiveness.

  • Identify criteria that contribute to successful teaching and share best practices across departments.

• Implement and Refine Intervention Strategies:

  • Track the impact of risk intervention strategies over time.

  • Adjust and refine strategies based on real-time data to ensure they are effective in improving learner outcomes.

• Ensure Consistency Across Departments:

  • Deploy consistent notification and intervention strategies across the institution to maintain high standards.

  • Compare performance across departments to ensure equitable academic quality.

Challenges

• Lack of Timely Data:

  • Delayed access to learner performance data can hinder early interventions and timely support for at-risk learners.

  • Without real-time insights, decision-making is often based on outdated information, leading to reactive rather than proactive measures.

• Identifying and Addressing At-Risk Learners:

  • Tracking at-risk learners across multiple courses and departments can be time-consuming and complex, especially without centralized data.

  • It's challenging to identify patterns of disengagement early enough to implement effective interventions.

• Ensuring Consistent Instruction and Academic Quality:

  • Variations in teaching styles and course structures can make it difficult to maintain a standardized level of academic quality across different departments.

  • Inconsistent grading practices and instructor engagement levels can affect learner outcomes and overall program success.

• Evaluating the Effectiveness of Intervention Strategies:

  • It can be difficult to measure the long-term impact of intervention strategies on learner retention and success without consistent data tracking.

  • Understanding which interventions are working and which need adjustment requires ongoing analysis and a clear picture of learner progress.

• Balancing Multiple Responsibilities:

  • Overseeing academic programs, faculty performance, and learner outcomes demands a significant amount of time and resources, making it difficult to focus on strategic improvements.

  • Without efficient tools to consolidate and analyze data, leadership may struggle to prioritize tasks and make data-driven decisions quickly.

• Data Overload:

  • Difficulty in interpreting large volumes of data and translating it into actionable insights.

  • Multiple programs are required to access needed information, analyze the data, then take action on the data.

Common Questions

• Which courses are the most successful?

• How many learners have never accessed a course?

• What criteria define a successful instructor?

• Is our risk intervention strategy effectively improving learner outcomes?

• How can we ensure consistency in academic quality across departments?

Datasets:

These datasets are identified by IntelliBoard as providing critical information to Academic Leadership. The Academic Leadership Org Role Template includes these datasets. Organizational administrators may provide additional datasets or may not include all the datasets below. Contact your administrator for questions regarding dataset availability.

Course Grade Distribution Dashboard:

The Course Grade Distribution dashboard combines the Course Grade Distribution chart with the Learner Success and Progress report. Data in the report are filtered by the letter grade chosen in the chart.

Course Grade Distribution: The chart shows the number of learners who have achieved each letter grade as a histogram.

• Purpose: To visualize how learners are performing across different grade brackets.

• Usage: Academic Leadership can see the overall grade distribution within a course, helping them assess how well learners are doing. This information can be used to evaluate whether grading patterns are consistent with course expectations and to identify trends in learner performance.

Learner Success and Progress: Shows the progress accumulated in the course by the learner and can be filtered by grade. This report is used to identify at-risk learners.

• Purpose: To show the progress accumulated in the course by each learner and to identify at-risk learners.

• Usage: Academic Leadership can filter this report by grade to pinpoint learners who may need additional support or intervention. It helps in monitoring learner progress and success, ensuring timely support and adjustments to improve overall course outcomes.

Time Spent on Course Tools Dashboard:

The Time Spent on Course Tools Dashboard combines the Time Spent on Course Tools vertical bar chart with the Course Content utilization report. Clicking on an activity type (tool) in the chart filters the report to activities of that type.

Time Spent on Course Tools: Shows the amount of time spent per Activity Type.

• Purpose: To monitor the amount of time learners spend on different types of course activities.

• Usage: Academic Leadership can use this chart to identify which tools or activities are most engaging or challenging for learners. This can inform decisions on which resources to emphasize or revise to improve learner engagement and learning outcomes across multiple courses.

Course Content Utilization: The Course Content Utilization report provides a detailed log of users' overall access to content/events/activities. Critical data includes users' activity-level time spent, first access and last access.

• Purpose: To provide a detailed log of users' overall access to course content, including time spent, first access, and last access.

• Usage: Academic Leadership can use this report to assess how frequently and effectively learners interact with course materials, helping to identify patterns in content engagement and areas where learners may need additional support or where course content may need to be updated.

Needs Grading Dashboard:

The Needs Grading Dashboard combines the Needs Grading bar chart with the Needs Grading report. Courses selected using the Needs Grading chart automatically filter the table of the report.

Needs Grading Chart: This bar chart displays the number of ungraded submissions in each displayed course.

• Purpose: To visually display the number of ungraded submissions in each course, allowing academic leadership to quickly see where grading attention is most needed.

• Use: Academic Leadership can use this chart to identify courses with the highest number of ungraded activities, helping them identify instructors that may need help with their workload.

Needs Grading: The Needs Grading report displays activities that learners have submitted that still need grading in the LMS system.

• Purpose: To provide a detailed list of all activities that learners have submitted but still need grading in the LMS.

• Use: Academic Leadership can access this report to directly view the specific activities that require grading. By selecting courses from the Needs Grading chart, the report automatically filters to show only the relevant submissions, streamlining the grading process.

Instructor Behavior Dashboard:

Intended for Academic Leadership, this Dashboard helps monitor Instructor behavior in courses.

Needs Grading Chart: This bar chart displays the number of ungraded submissions in each displayed course.

• Purpose: To visually display the number of ungraded submissions in each course, allowing academic leadership to quickly see where grading attention is most needed.

• Use: Academic Leadership can use this chart to identify courses with the highest number of ungraded activities, helping them address potential grading delays and support instructors with heavy workloads.

Instructor Daily Engagement: This line graph displays the instructors daily page views and defaults to a two week period.

• Purpose: To track the daily page views of instructors over a two-week period, showing their engagement levels with the course platform.

• Use: Academic Leadership can use this line graph to monitor and assess the consistency of instructor engagement, helping to identify potential issues with instructor activity or areas where additional support might be needed.

Instructor Course Engagement Summary: Displays detailed information about instructor behavior within courses and learners, including instructor email and a link to the course, and count of submissions broken out by grade status. Can be filtered by category/course, course start date, and course end date.

• Purpose: To provide detailed information about instructor behavior within courses, including their engagement level and submission counts by grade status.

• Use: Academic Leadership can use this summary to evaluate instructor performance, assess how actively they engage with their courses, and determine if additional resources or support are needed.

Instructor Course Activity Engagement Detail: The Instructor Course Activity Engagement Detail report details instructor time spent for all activities used within a course.

• Purpose: To detail the time instructors spend on various activities within a course, providing insights into their level of involvement.

• Use: Academic Leadership can use this report to analyze how instructors allocate their time across different course activities, helping to identify patterns in engagement and areas where instructors may need additional support or resources.

Projected DFW Risk Dashboard:

Intended for Registrars and Academic Leadership to monitor course progress, this Dashboard seeks to quickly identify courses with high D/F rates. This Dashboard includes a bar chart which visualizes courses with learner course grades separated by 3 values: grades 0-59 (red), 60-69 (yellow), 70-100 (green). Two table charts are also included; 1 showing the same courses, with a count of course grades in the corresponding thresholds. The last table displays individual learner information for a deeper review.

DFW Rates by Category: This bar chart displays count of active learners by course category or Sub-Account with current course grades of D and F. This bar chart is intended to quickly identify course categories or Sub-Account that may have high D/F/W rates.

• Purpose: Shows the count of active learners with D and F grades, organized by course category or Sub-Account.

• Use: To quickly identify which course categories or departments may be contributing to higher D/F/W rates, enabling targeted support or interventions.

DFW by Courses: This bar chart displays count of active learners by course with current course grades of D and F. This bar chart is intended to quickly identify courses that may have a high D/F/W rate.

• Purpose: This bar chart displays the count of active learners with D and F grades, organized by specific courses.

• Use: To pinpoint courses with high D/F/W rates, allowing them to investigate underlying issues and implement corrective actions at the course level.

Projected DFW Risk: This table chart displays a count of learners course grades separated by 3 values: grades 0-59 (red), 60-69 (yellow), 70-100 (green). This intends to be paired with the corresponding DFW by Course bar chart, allowing you to quickly identify courses that may have a high D/F/W rate. Additional information about course, including category, term, course codes, total enrollments, is included for additional detail.

• Purpose: Categorizes learners' grades into three thresholds (0-59, 60-69, 70-100) and pairs with the DFW by Course chart for detailed risk assessment.

• Use: Identify courses with high DFW rates that can indicate issues with course content, teaching effectiveness, or learner support, and help leadership identify areas needing improvement.

User Level Course Access Details:

The User Level Course Access Details report summarizes an individual user's access to a course. Critical data includes time spent, participations, first and last access. This dataset can be paired with Course Level Access Trends to identify individual users within a course or to understand positive access trends.

• Purpose: Provides a detailed summary of an individual learner's access and participation within a specific course.

• Use: Academic Leadership use this dataset to identify individual learners' engagement levels, allowing for personalized interventions or support strategies when paired with course-wide trends. It also identifies users that do not have course access or have dropped a course but remain listed in the enrollment.

Course Level Access Trends:

This dataset displays a course-level aggregate to illuminate course trends related time spent and participation. Used by course administrators (managers, deans, etc.) to determine low or high activity/access courses.

• Purpose: Provides an aggregate view of time spent and participation trends across a course.

• Use: Academic Leadership use this dataset to identify courses with low or high levels of learner activity and engagement, enabling targeted support or adjustments to course content.

Inactive Users in Courses:

The Inactive Users in Courses dataset identifies users who have not accessed a course in a particular time period. Last Access, Last Participation and Last Login Dates also are included to provide additional context. Used by a wide auditory to evaluate users with at-risk access behavior.

• Purpose: Identify learners who are disengaged from courses or have issues with course access.

• Use: Academic Leadership utilize this dataset to pinpoint at-risk learners with low engagement, enabling timely interventions to improve retention and course participation.

Learners Average Course Grade Distribution:

Represents number of Learners with average grade across learner's courses distributed across grade threshold: 0-59 %, 60-69 %, 70+ %

• Purpose: Visualizes the distribution of learners' average grades across courses, segmented into grade thresholds (0-59%, 60-69%, 70+%).

• Use: Academic Leadership use this dataset to assess overall learner performance trends, identifying groups of learners who may need academic support or intervention based on their average grades.

Learners Average Course Grade:

This chart displays the average of the learner's current course grades. For example, if a learner is taking 5 courses, the bar represents the average grade of those 5 courses.

• Purpose: Analyze learner performance across all courses.

• Use: Academic Leadership can use the chart to track the average grade distribution to identify trends in learner achievement to inform recommendation in course loads in varying fields of study.

Page Views by Tools:

The chart displays the number of views of pages in the selected course by Activity Type. It can be filtered by Course and Category and by Activity Type.

• Purpose: Tracks the number of page views within a course, categorized by different Activity Types.

• Use: Academic Leadership use this dataset to analyze engagement with specific course tools, identifying which tools are most or least utilized, aiding in course design and resource allocation decisions.

Activity Participation and Grade Correlation:

This scatterplot shows the correlation between participations and the LMS gradebook grade on those activities per Learner. The user can filter by Course, Activity, and Graded At date.

• Purpose: To understand the relationship between learner participation and grades.

• Use: Academic Leadership can use the scatterplot to identify whether participation in particular activity types correlates with higher grades to inform course design and instruction.

Course Verification:

Leverage the Course List dataset to review course level information, including course location, course codes, and a summary of course activity. Grey columns represent system level information; blue columns represent course level information.

• Purpose: To ensure accuracy and consistency in course-level information, including course location, codes, and activity summaries.

• Use: Academic Leadership use this dataset to verify course details, ensuring proper categorization, and monitoring course activity to maintain academic standards and support program oversight.

Enrolled Participants per Category:

This pie chart shows the number of Participants per Course Category, including Learners, Instructors, and other roles. It can be filtered by Course and by Category and by Course Start Date.

• Purpose: Provides a visual breakdown of the number of participants, including learners, instructors, and other roles, across various course categories.

• Use: Academic Leadership use this data to assess enrollment distribution, evaluate instructor-to-learner ratios, and make informed decisions about resource allocation and program support.