weber state university biennial report on assessment of ... science... · report due 11/15/2019...

1 Report due 11/15/2019

Weber State University Biennial Report on Assessment of Student Learning

Cover Page Department/Program: Masters of Science in Computer Engineering Academic Year of Report: 2018/19 (covering Summer 2017 through Spring 2019) Date Submitted: Report author: Fon Brown Contact Information: Phone: 626-7781 Email: [email protected]

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A. Brief Introductory Statement:

Please review the Introductory Statement and contact information for your department or academic program displayed on the assessment site:

http://www.weber.edu/portfolio/departments.html - if this information is current, please place an ‘X’ below. No further information is needed.

_X_ Information is current; no changes required.

Update if not current:

B. Mission Statement

Please review the Mission Statement for your department or academic program displayed on the assessment site:

http://www.weber.edu/portfolio/departments.html - if the mission statement is current, please place an ‘X’ below.; If the information is not

current, please provide an update:

_X_ Information is current; no changes required.


C. Student Learning Outcomes Please review the Student Learning Outcomes for your academic program displayed on the assessment site:

http://www.weber.edu/portfolio/departments.html. In particular, review in light of recent strategic reporting and indicate any needed updates. If

the outcomes are current, mark below. _X_ Information is current; no changes required.


http://www.weber.edu/portfolio/departments.html



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D-1. Curriculum

“A collection of courses is not a program. A curriculum has coherence, depth, and synthesis.”

(Linda Suskie; presentation at NWCCU Assessment Fellowship, June 19, 2019)

Please review the Curriculum Grid for your department or academic program displayed on the assessment site:

http://www.weber.edu/portfolio/departments.html.

Indicate in the curriculum grid where graduating student performance is assessed for each program outcome. In the ‘additional information’ section, please provide information about these assessments (e.g., portfolios, presentations, projects, etc.) This information will be summarized at the college and institutional level for inclusion in our NWCCU reporting on student achievement. Curriculum Map

Core Courses in Department/Program

Program Learning Outcomes

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CS 6100 - Distributed Operating Systems H L H L CS 6420 - Advanced Algorithms H L H H CS 6500 - Machine Learning H H H H CS 6600 - Machine Learning H H H H CS 6610 - Computer Architecture H L H H CS 6740 – Computer Systems Security L H H H CS 6820 - Compiler Design H H H H CS 6840 - Formal System Design H H H H CS 6850 - Parallel Programming and Architecture H H H L ECE 6010 - Design Project H H H H ECE 6020 - Thesis H H H H ECE 6110 - Digital VLSI Design H H H H ECE 6120 - Advanced VLSI Design H H H H ECE 6130 - Advanced Semiconductor Devices H H H H


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Core Courses in Department/Program

Program Learning Outcomes

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ECE 6210 - Digital Signal Processing H L H H ECE 6220 - Image Processing H L H H ECE 6410 - Communication Circuits and Systems H H L H ECE 6420 - Digital Communication L H H H ECE 6620 - Digital System Testing L L H H ECE 6710 - Real-Time Embedded Systems H H H L ECE 6900 – Special Topics L L L L

H – Indicates mastery, L – Introduces or elaborates

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Additional Information (details about graduating student assessment): Graduating Students are assessed at the time of their defense. The process is described later in this document.

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D-2. High Impact Educational Experiences in the Curriculum In response to the recent USHE requirement that all students have at least 1 HIEE in the first 30 credit hours and 1 HIEE in the major or minor we are asking programs to map HIEEs to curriculum using a traditional curriculum grid. This helps demonstrate how and where these goals are accomplished.

Courses

Dept. Use of HIEE

Pro

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ECE 6010 – Design Project X ECE 6020 - Thesis X

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Additional information (HIEE planning, assessment, or other information): In their final year, all students are require to take ECE 6010 – Design project (which entails a major project and substantial writing component) or ECE 6020 – Thesis (which entails a significant research effort and an intensive writing component).

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E. Assessment Plan Please update the Assessment Plan for your department displayed on the assessment site: http://www.weber.edu/portfolio/departments.html. Keep in

mind that reporting will be done biennially instead of annually; that should be reflected in your assessment plan. Please ensure that Gen Ed courses

are assessed/reported at least twice during a standard program review cycle.

A complete plan will include a list of courses from which data will be gathered and the schedule, as well as an overview of the assessment strategy

the department is using (for example, portfolios, or a combination of Chi assessment data and student survey information, or industry certification

exams, etc.), and plans for continuous improvement.

Assessment plan: The assessment plan is unchanged from the portfolio website. No changes are needed to reflect the 2-year reporting cycle.


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F. Report of assessment results for the most previous academic year:

Evidence of Learning: Courses within the Major Course: CS 6420

Evidence of Learning: Courses within the Major Program Learning Outcome(s)

Measurable Learning Outcome

Method of Measurement

Threshold for Evidence of Student Learning

Findings Linked to Learning Outcomes

Interpretation of Findings

Action Plan/Use of Results

Demonstrate the ability to apply knowledge of math, science and engineering Demonstrate the ability to identify, formulate and solve engineering problems

Learning Outcome 1: Analyze running time and memory usage for algorithms

Measure 1: End of semester instructor assessment of each student’s ability to analyze algorithms in terms of run-time and memory usage.

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to apply empirical analysis and use plots to construct hypothesis and predictions, but unable to derive mathematical models for algorithm analysis.

Measure 1: At least 80% of students obtained a 3 (Satisfactory) rating.

Measure 1: Most students demonstrated mastery of this outcome.

No change is needed


Learning Outcome 2: Understand the sorting algorithm design paradigm and its applications

Measure 1: End of semester instructor assessment of each student’s under-standing of sorting algorithms.

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to write code to implement MergeSort and QuickSort, and able to analyze sorting algorithms and sufficiently describe their benefits and drawbacks.

Measure 1: At least 80% of students obtained a 4 (Excellent) rating.


No change is needed

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Learning Outcome 3: Understand the searching algorithm design paradigm and its applications

Measure 1: End of semester instructor assessment of each student’s under-standing of searching algorithms.

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to understand symbol tables and binary search trees and their applications; able to use symbol tables and binary search trees to implement ordered operations.

Measure 1: At least 80% of students obtained a 4 (Excellent) rating


No change is needed

Demonstrate the ability to apply knowledge of math, science and engineering Demonstrate the ability to identify, formulate and solve engineering problems Demonstrate the ability to apply master’s level knowledge to the specialized area of computer engineering

Learning Outcome 4: Understand the graph algorithm design paradigm and its applications

Measure 1: End of semester instructor assessment of each student’s under-standing of graph algorithms.

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to understand undirected graphs and directed graphs and their representations; Able to understand and apply depth-first search, breadth-first search, and connected components algorithms to solve simple problems.

Measure 1: At least 80% of students obtained a 4 (Excellent) rating


No change is needed

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Learning Outcome 5: Understand and operate practical data structures and their applications

Measure 1: End of semester instructor assessment of each student’s under-standing of data structures.

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to understand and write code to implement practical data structures such as stacks, queues, priority queues, heaps, hash tables, search trees, etc. Able to understand and analyze the performance and purpose of different data structures.

Measure 1: At least 80% of students obtained a 3 (Satisfactory) rating


No change is needed


Learning Outcome 6: Understand the dynamic programming algorithm design paradigm and its applications

Measure 1: End of semester instructor assessment of each student’s ability to apply the dynamic programming algorithm design paradigm

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to understand elements of the dynamic programming algorithm and apply it to solve simple problems.

Measure 1: At least 80% of students obtained a 2 (Improving) rating


Provide more hands-on examples of greedy algorithms and dynamic programming

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Course: CS 6600 Evidence of Learning: Courses within the Major

Program Learning Outcome(s)








Learning Outcome 1: Understand and be able to use logistic regression

Measure 1: Instructor assessment of each student’s under-standing and use of logistic regression

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to understand decision boundary, cost function, gradient descent for logistic regression; Able to apply logistic regression to solve classification problems; Able to understand and apply advanced optimization to solve multiclass classification problems.



No change is needed

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Learning Outcome 2: Understand and be able to use neural networks

Measure 1: Instructor assessment of each student’s under-standing and use of neural networks

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to understand neural network representations and learning algorithms, including forward propagation, backpropagation, network architectures, gradient checking, and random



No change is needed

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Learning Outcome 3: Understand and be able to use support vector machines

Measure 1: Instructor assessment of each student’s under-standing and use of vector machines.

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to understand support vector machines, including large margin classifier, kernels, and situations when it may be used as an alternative to logistic regression and neural networks; Able to apply support vector machines to solve simple problems.



No change is needed

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Learning Outcome 4: Design and develop unsupervised learning systems using clustering algorithm

Measure 1: Instructor assessment of each student’s machine-learning system that uses the clustering-algorithm.

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to understand unsupervised learning and its applications; Able to understand K-means algorithm; Able to apply K-means algorithm to solve clustering problems .



No change is needed

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Demonstrate the ability to apply knowledge of math, science and engineering Demonstrate the ability to design a system, component or process. Demonstrate the ability to identify, formulate and solve engineering problems Demonstrate the ability to apply master’s level knowledge to the specialized area of computer engineering

Learning Outcome 5: Develop, debug, and evaluate learning algorithms and intelligent systems

Measure 1: Instructor assessment of each student’s learning algorithms and intelligent systems

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to diagnose machine learning algorithm using a training set, a test set, and a cross validation set; Able to use the learning curve to analyze algorithms; Able to apply precision and recall to analyze errors of an intelligent system.



No change is needed

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Demonstrate the ability to apply knowledge of math, science and engineering

Learning Outcome 1: Understand different types of language translators and translation processes

Measure 1: Instructor assessment of each student’s explanation of language translator types and processes

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to demonstrate sufficient knowledge of the various translation schemes that involve compilation and/or interpretation, and the diversity of source and target programs.



No change is needed

Demonstrate the ability to apply knowledge of math, science and engineering Demonstrate the ability to design a system, component or process. Demonstrate the ability to identify, formulate and solve engineering problems Demonstrate the ability to apply master’s level knowledge to the specialized area of computer

Learning Outcome 2: Analyze and implement compilers with limited instruction sets

Measure 1: Instructor assessment of each student’s analysis and implementation of a compiler

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to understand and create working code that successfully implements all phases of the compilation process.



No change is needed

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Learning Outcome 3: Recognize and analyze data flow and format within and between compiler phases

Measure 1: Instructor assessment of each student’s analysis of data flow between compiler phases

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to demonstrate sufficient knowledge of the transformation and conversion of source code data and semantics between different compiler phases.



No change is needed


Learning Outcome 4: Understand and implement the data structures and algorithms necessary to implement a working compiler.

Measure 1: Instructor assessment of the algorithms and data structures implemented in each student’s compiler

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to understand and create working code that successfully implements symbol tables, parse trees, abstract syntax trees and associated algorithms that support compiler operation.



No change is needed

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Learning Outcome 5: Analyze and apply thorough and methodical testing to a modular software engineering project.

Measure 1: Instructor assessment of the testing regimen for each student’s compiler project

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to apply a sufficient and acceptable testing regimen to compiler project software.



No change is needed

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Demonstrate the ability to identify, formulate and solve engineering problems

Learning Outcome 1: Describe the models of parallel programming

Measure 1: Instructor assessment of each student’s description of the various parallel programming models

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to demonstrate sufficient knowledge of all of the covered parallel programming models including shared memory, message passing, task parallelism, and data parallelism and can correctly apply Flynn’s taxonomy.



No change is needed

Demonstrate the ability to apply knowledge of math, science and engineering

Learning Outcome 2: Identify tradeoffs between different parallel programming approaches.

Measure 1: Instructor assessment of each student’s trade-off analysis for various parallel programming approaches

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to Enumerates all of the different trade-offs between parallel programming approaches and usually selects the best approach for the given task.

Measure 1: At least 80% of students obtained a 2 (Improving) rating.


Dr Peterson does not believe the criteria are approproiate. A committee has been formed to reevaluate the assessment criteria

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Demonstrate the ability to apply knowledge of math, science and engineering Demonstrate the ability to design a system, component or process. Demonstrate the ability to identify, formulate and solve engineering problems

Learning Outcome 3: Use a variety of parallel programming approaches and techniques to solve interesting programming problems

Measure 1: Instructor assessment of each student’s solution to various parallel programming problems

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to create an efficient parallel implementation that correctly solves the problem. The solution is close to optimal and covers all obvious corner cases.



Dr Peterson does not believe the criteria are approproiate. A committee has been formed to reevaluate the assessment criteria

22 Report due 11/15/2019

Course: ECE 6110 Evidence of Learning: Courses within the Major








Demonstrate the ability to design a system, component or process. Demonstrate the ability to apply master’s level knowledge to the specialized area of computer engineering

Learning Outcome 1: Demonstrate the ability to design a standard cell library from CMOS

Measure 1: Instructor assessment of each student’s cell library.

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to design standard digital logic utilizing a schematic capture and a layout tool.



No change is needed.

Demonstrate the ability to apply knowledge of math, science and engineering Demonstrate the ability to design a system, component or process. Demonstrate the ability to apply master’s level knowledge to the specialized area of computer engineering

Learning Outcome 2: Demonstrate the ability to design large scale integrated digital systems

Measure 1: Instructor assessment of the integration level in each student’s term project

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to design large-scale digital systems utilizing schematic capture tool, hand layout techniques, and auto routing tools.

Measure 1: At least 80% of students obtained a 3 (Satisfactory) rating



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Learning Outcome 3: Demonstrate the ability to design custom digital systems to speed, power, and size constraints

Measure 1: Instructor assessment of the speed, power consumption and physical size of each student’s term project.

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to design custom digital systems to size and power constraints.




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Learning Outcome 1: Demonstrate knowledge of discrete-time signals and systems.

Measure 1: Instructor assessment of each student’s analysis of several discrete-time signals and systems

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to implement structures for realization of discrete-time, linear shift-invariant systems Linear difference equation.



No change is needed


Learning Outcome 2: Use linear transforms in discrete time systems (DFT, FFT, and Z-transforms).

Measure 1: Instructor assessment of each student’s use of linear transforms in several discrete-time systems

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to determine the transient and steady state responses for discrete-time LTI systems.



No change is needed

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Learning Outcome 3: Analyze discrete-time signals in the Time and Frequency domains.

Measure 1: Instructor assessment of each student’s time and frequency analysis of several discrete-time signals.

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student has acquired a knowledge of ADC and DAC and Quantization effects.



No change is needed


Learning Outcome 4: Synthesize Discrete time systems, FIR, IIR.

Measure 1: Instructor assessment of each student’s implementation of (a) an FIR discrete-time system and (b) an IIR discrete-time system.

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to implement cascade structures for FIR and IIR systems.



No change is needed

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Learning Outcome 5: Demonstrate knowledge of Linear Prediction and Optimum Linear filters.

Measure 1: Instructor assessment of each student’s ability to analyze linear prediction and optimum linear filters

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to demonstrate a knowledge of Linear Prediction error filter properties.


Measure 1: Less than 80% of students demonstrated mastery of this outcome.

Schedule less review of Continuous-Time systems at beginning of semester

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Learning Outcome 1: Demonstrate understanding of image formation and digitization effects.

Measure 1: Instructor assessment of each student’s analysis of various images and digitization effects.

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to understand basic image format and digitization effects.



No change is needed


Learning Outcome 2: Use the discrete Fourier, cosine, Haar transforms.

Measure 1: Instructor assessment of each student’s imple-mentation of the Fourier, cosine and Haar transforms

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able apply 2D Fourier transform for image filtering and 2D cosine transform for image compression.



No change is needed

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Learning Outcome 3: Use image filtering, segmentation and enhancement.

Measure 1: Instructor assessment of each student’s use of segmentation and enhancement to filter images

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to improve image quality by performing filtering and segmentation.



No change is needed


Learning Outcome 4: Use the methods of morphological image processing.

Measure 1: Instructor assessment of each student’s use of morphological methods to process images.

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to use morphological methods to improve image quality.



No change is needed

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Demonstrate the ability to apply master’s level knowledge to the specialized area of computer engineering

Learning Outcome 1: Use baseband modulation/demod-ulation techniques to transmit digital data over a noisy, bandwidth-limited channel (wired or wireless).

Measure 1: Instructor assessment of each student’s usage of various modulation and demodulation techniques

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to simulate Matlab/Simulink models for binary PAM, BPSK, QPSK, 16-QAM modulation/demodulation.



No change is needed

Demonstrate the ability to identify, formulate and solve engineering problems

Learning Outcome 2: Analyze the performance of the digital data.

Measure 1: Instructor assessment of each student’s analysis of digital data sets

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to understand Signal Noise Ratio (Eb/No) and generate BER plots in Matlab/Simulink.



No change is needed

Demonstrate the ability to design a system, component or process. Demonstrate the ability to identify, formulate and solve engineering problems

Learning Outcome 3: Design various components of the receiver in a digital communication system

Measure 1: Instructor assessment of each student’s design of various receiver components

Measure 1: At least 80% of students should obtain a 3 (Satisfactory) rating, which indicates that the student is able to understand and apply key concepts such as up-sampling, down-sampling, low-pass-filtering, carrier phase synchronization, and symbol timing synchronization for various mod/demod techniques described above.



No change is needed

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A. Evidence of Learning: Program Project Defense Reports (direct assessment) Summary of Project Defense Assessments (Graduating Cohort Fall 2018)

SEMESTER YEAR GRADUATES Fall 2018 5

Program Outcome Score Ability to apply knowledge of math, science and engineering 3.54 Ability to design a system, component or process 3.78 Ability to identify, formulate and solve engineering problems 3.56 Ability to apply master’s level knowledge to the specialized area of computer engineering

3.26

Summary of Project Defense Assessments (Graduating Cohort Spring 2019)

SEMESTER YEAR GRADUATES Spring 2019 0

Program Outcome Score Ability to apply knowledge of math, science and engineering N/A Ability to design a system, component or process N/A Ability to identify, formulate and solve engineering problems N/A Ability to apply master’s level knowledge to the specialized area of computer engineering

N/A

Program Action Plan (if one or more mean Likert Scores fall below 2.67): Not Necessary

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Appendix A Most departments or programs receive a number of recommendations from their Five/Seven-Year Program Review processes. This page provides a means of updating progress towards the recommendations the department/program is acting upon. Additional narrative: This program is in its fourth year, so no review has yet been performed

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Appendix B Please provide the following information about the full-time and adjunct faculty contracted by your department during the last academic year (summer through spring). Gathering this information each year will help with the headcount reporting that must be done for the final Five Year Program Review document that is shared with the State Board of Regents.

Faculty Headcount 2017-18 2018-19 With Doctoral Degrees (Including MFA and other terminal degrees, as specified by the institution)

13 13

Full-time Tenured 5 6 Full-time Non-Tenured (includes tenure-track) 8 7 Part-time and adjunct With Master’s Degrees Full-time Tenured Full-time Non-Tenured Part-time and adjunct With Bachelor’s Degrees Full-time Tenured Full-time Non-tenured Part-time and adjunct Other Full-time Tenured Full-time Non-tenured Part-time Total Headcount Faculty 13 13 Full-time Tenured 5 6 Full-time Non-tenured 8 7 Part-time

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Appendix C – alternative format for Evidence of Learning Reporting

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Please respond to the following questions.

1) First year student success is critical to WSU’s retention and graduation efforts. We are interested in finding out how departments

support their first-year students. Do you have mechanisms and processes in place to identify, meet with, and support first-year students? Please provide a brief narrative focusing on your program’s support of new students:

a. Any first-year students taking courses in your program(s). All new students meet with the program director for an orientation.

b. Students declared in your program(s), whether or not they are taking courses in your program(s) Students who are not registered receive a phone call or email to check on their status (first absent semester only).

2) A key component of sound assessment practice is the process of ‘closing the loop’ – that is, following up on changes implemented as a response to your assessment findings, to determine the impact of those changes/innovations. It is also an aspect of assessment on which we need to improve, as suggested in our NWCCU mid-cycle report. Please describe the processes your program has in place to ‘close the loop’. If any of the assessment triggers are activated, the faculty or a subcommittee meet to address the trigger. For example, in Fall 2017 the defense reports triggered a faculty response. The faculty met on 4/10/18 (minutes available on request) to address the problem. The decision was made to enforce a policy that limited the number of graduate students any one faculty member could mentor to one (unless all the faculty had a student). That policy seems to have been successful inasmuch as defense assessments have steadily improved since then.

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Glossary Student Learning Outcomes/Measurable Learning Outcomes

The terms ‘learning outcome’, ‘learning objective’, ‘learning competency’, and ‘learning goal’ are often used interchangeably. Broadly, these terms

reference what we want students to be able to do AFTER they pass a course or graduate from a program. For this document, we will use the word

‘outcomes’. Good learning outcomes are specific (but not too specific), are observable, and are clear. Good learning outcomes focus on skills:

knowledge and understanding; transferrable skills; habits of mind; career skills; attitudes and values.

- Should be developed using action words (if you can see it, you can assess it).

- Use compound statements judiciously.

- Use complex statements judiciously.

Curriculum Grid

A chart identifying the key learning outcomes addressed in each of the curriculum’s key elements or learning experiences (Suskie, 2019). A good

curriculum:

- Gives students ample, diverse opportunities to achieve core learning outcomes.

- Has appropriate, progressive rigor.

- Concludes with an integrative, synthesizing capstone experience.

- Is focused and simple.

- Uses research-informed strategies to help students learn and succeed.

- Is consistent across venues and modalities.

- Is greater than the sum of its parts.

Target Performance (previously referred to as ‘Threshold’) The level of performance at which students are doing well enough to succeed in later studies (e.g., next course in sequence or next level of course) or career. Actual Performance How students performed on the specific assessment. An average score is less meaningful than a distribution of scores (for example, 72% of students met or exceeded the target performance, 5% of students failed the assessment). Closing the Loop The process of following up on changes made to curriculum, pedagogy, materials, etc., to determine if the changes had the desired impact.

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Continuous Improvement An idea with roots in manufacturing, that promotes the ongoing effort to improve. Continuous improvement uses data and evidence to improve student learning and drive student success. Direct evidence Evidence based upon actual student work; performance on a test, a presentation, or a research paper, for example. Direct evidence is tangible, visible, and measurable. Indirect evidence Evidence that serves as a proxy for student learning. May include student opinion/perception of learning, course grades, measures of satisfaction, participation. Works well as a complement to direct evidence. HIEE – High Impact Educational Experiences Promote student learning through curricular and co-curricular activities that are intentionally designed to foster active and integrative student engagement by utilizing multiple impact strategies.

weber state university biennial report on assessment of ... science... · report due 11/15/2019...

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