The document discusses advancements in technology-based assessments in special education, emphasizing their efficiency in evaluating students and promoting individualized learning through innovative tools like video segments and algorithms. It highlights concerns about technological infrastructure and training for teachers, which impact the effectiveness of these assessments on students with learning differences. Additionally, it addresses the implications of current assessment practices, particularly regarding equity in educational outcomes for students with disabilities.

1. Technology-based assessments-special education
New technologies remain competitive in driving efforts to make
learning more efficient. Technology-based assessment in special
education has made quite some advancement (Goldsmith &
LeBlanc, 2004). First applications of computer technology
assessment were for the scoring student's test forms. Currently,
features incorporate self-administration, software control in
presentation, response evaluation based on algorithms,
prescription based on expert knowledge and direct links in
assessment and change in instructions. The technology-based
assessment uses electronic and software systems to evaluate
individual children in an educational setting. Traditional
assessments employ approaches of the computer.
Video-based computer assisted test enabled learning of
language for the student automatically increasing the validity of
measurements. Video segments incorporated movie elements of
moral dilemma in problem-solving tests. Students viewing the
video segments respond by simply touching the screen.
Innovative approaches have created relevance in testing
procedures. Misplaced students result into poor results and get
prompted to drop out. Teachers not well trained contribute to
the misplacement due to poor management of certain behaviors
and learning differences. For effect, teachers must be able to
analyze data produced by the assessment and develop a due
course of action.
In addressing students with physical limitations use of voice
recognition, handwriting interpreters, stylus tools, and
touchscreen enables communication without the use of keys
(Gierach, 2009). New software features allow students to
perform comfortable pace of video segments on preferred
language options. Computers are linked to videodisc enabling
students to learn according to individual needs and skills. Latest
technological features concern evaluation. Technological
advancements assess social competence among students. The

2. evaluator views students in a variety of context. Limitation in
technology infrastructure, seen as the key barrier in this sort of
assessment. Many district schools lack adequate high-speed
broadband access necessary for this evaluation. Moreover,
obsolesce in technology-based assessment erodes the capacity to
provide quality services technology-based systems have a
relatively short functional life.
Holistic assessments are the best in technology-based
assessments. They incorporate software control in presentation,
conceptual models or algorithms, decision-making based rules
and expert knowledge (Redecker, & Johannessen, 2013).
Proliferation technology helps students in the inclusion of
speech recognition, electronic communication, personal
computers, robotics and artificial intelligence. Trends in
technology-based assessments have impacted lives of students
with a disability. They achieve school improvement goals as
well as tracking student growth and progress. Current
assessment norms have embedded current standard-based
systems as the only way to administer the evaluation. Teachers
get separated from the evaluation especially in focus of high-
stake standardized tests. Access and integration are sensitive in
technology-based assessments. Discontinues in hardware,
software and present assessment practices and procedures have
implications. Regarding acquisition of technology informed
decisions get made.
References
Gierach, J. (2009). Assessing students’ needs for assistive
technology (ASNAT). Milton, WI: Wisconsin Assistive
Technology Initiative.
Goldsmith, T. R., & LeBlanc, L. A. (2004). Use of technology

3. in interventions for children with autism. Journal of Early and
Intensive Behavior Intervention, 1(2), 166.
Redecker, C., & Johannessen, Ø. (2013). Changing
assessment—Towards a new assessment paradigm using
ICT. European Journal of Education, 48(1), 79-96.
BUS308DiscussionsWeek 2 - Discussion 1
Part One – Hypothesis Testing
Read Lecture Four. Lecture Four starts out with the five-step
procedure for hypothesis testing. What is this? What does it do
for us? Why do we need to follow these steps in making a
judgement about the populations our samples came from? What
are the “tricky” parts of developing appropriate hypotheses to
test? What examples can you suggest where this process might
be appropriate in your personal or professional lives? (This
should be started on Day 1.)
Part Two – T-tests
Read Lecture Five. Lecture Five illustrates several t-tests on the
data set. What conclusions can you draw from these tests about
our research question on equal pay for equal work? What is
missing from these results to give us a complete answer to the
question? Why? (This should be started on Day 3.)
Part Three – F-test
Read Lecture Six. Lecture Six introduces you to the F-test for
variance equality. Last week, we discussed how adding a
variation measure to reports of means was a smart thing to do.
Why does variation make our analysis of the equal pay for equal
work question more complicated? What causes of variation
impact salary that we have not discussed yet? How can you
relate this issue to measures used in your personal or
professional lives? (This should be completed by Day 5.)
DataIDSalaryCompa-ratioMidpointAgePerformance
RatingServiceGenderRaiseDegreeGender1GradeCopy Employee
Data set to this page.The ongoing question that the weekly

4. assignments will focus on is: Are males and females paid the
same for equal work (under the Equal Pay Act)? Note: to
simplfy the analysis, we will assume that jobs within each grade
comprise equal work.The column labels in the table mean:ID –
Employee sample number Salary – Salary in thousands Age –
Age in yearsPerformance Rating – Appraisal rating (Employee
evaluation score)SERvice – Years of serviceGender: 0 = male, 1
= female Midpoint – salary grade midpoint Raise – percent
of last raiseGrade – job/pay gradeDegree (0= BSBA 1 =
MS)Gender1 (Male or Female)Compa-ratio - salary divided by
midpoint
Week 2This assignment covers the material presented in weeks
1 and 2.Six QuestionsBefore starting this assignment, make sure
the the assignment data from the Employee Salary Data Set file
is copied over to this Assignment file.You can do this either by
a copy and paste of all the columns or by opening the data file,
right clicking on the Data tab, selecting Move or Copy, and
copying the entire sheet to this file(Weekly Assignment Sheet
or whatever you are calling your master assignment file).It is
highly recommended that you copy the data columns (with
labels) and paste them to the right so that whatever you do will
not disrupt the original data values and relationships.To Ensure
full credit for each question, you need to show how you got
your results. For example, Question 1 asks for several data
values. If you obtain them using descriptive statistics,then the
cells should have an "=XX" formula in them, where XX is the
column and row number showing the value in the descriptive
statistics table. If you choose to generate each value using
fxfunctions, then each function should be located in the cell and
the location of the data values should be shown.So, Cell D31 -
as an example - shoud contain something like "=T6" or
"=average(T2:T26)". Having only a numerical value will not
earn full credit.The reason for this is to allow instructors to
provide feedback on Excel tools if the answers are not correct -
we need to see how the results were obtained.In starting the
analysis on a research question, we focus on overall descriptive

5. statistics and seeing if differences exist. Probing into reasons
and mitigating factors is a follow-up activity.1The first step in
analyzing data sets is to find some summary descriptive
statistics for key variables. Since the assignment problems
willfocus mostly on the compa-ratios, we need to find the mean,
standard deviations, and range for our groups: Males, Females,
and Overall.Sorting the compa-ratios into male and females will
require you copy and paste the Compa-ratio and Gender1
columns, and then sort on Gender1.The values for age,
performance rating, and service are provided for you for future
use, and - if desired - to test your approach to the compa-ratio
answers (see if you can replicate the values).You can use either
the Data Analysis Descriptive Statistics tool or the Fx =average
and =stdev functions. The range can be found using the
difference between the =max and =min functions with Fx
functions or from Descriptive Statistics.Suggestion: Copy and
paste the compa-ratio data to the right (Column T) and gender
data in column U. If you use Descriptive statistics, Place the
output table in row 1 of a column to the right.If you did not use
Descriptive Statistics, make sure your cells show the location of
the data (Example: =average(T2:T51)Compa-ratioAgePerf.
Rat.ServiceOverallMean35.785.99.0Standard
Deviation8.251311.41475.7177Note - remember the data is a
sample from the larger company
populationRange304521FemaleMean32.584.27.9Standard
Deviation6.913.64.9Range26.045.018.0MaleMean38.987.610.0S
tandard Deviation8.48.76.4Range28.030.021.0A key issue in
comparing data sets is to see if they are distributed/shaped the
same. At this point we can do this by looking at the
probabilities that males and females are distributed in the same
way for a grade levels.2Empirical Probability: What is the
probability for a:Probabilitya. Randomly selected person
being in grade E or above?b. Randomly selected person
being a male in grade E or above? c. Randomly selected
male being in grade E or above? d. Why are the results
different?3Normal Curve based probability: For each group

6. (overall, females, males), what are the values for each question
below?:Make sure your answer cells show the Excel function
and cell location of the data used.AThe probability of being in
the top 1/3 of the compa-ratio distribution.Note, we can find the
cutoff value for the top 1/3 using the fx Large function:
=large(range, value).Value is the number that identifies the x-
largest value. For the top 1/3 value would be the value that
starts the top 1/3 of the range,For the overall group, this would
be the 50/3 or 17th (rounded), for the gender groups, it would
be the 25/3 = 8th (rounded) value.OverallFemaleMaleAll of the
functions below are in the fx statistical list.i.How nany salaries
are in the top 1/3 (rounded to nearest whole number) for each
group? Use the "=ROUND" function (found in Math or All
list)iiWhat Compa-ratio value starts the top 1/3 of the range for
each group?Use the "=LARGE" functioniiiWhat is the z-score
for this value?Use Excel's STANDARDIZE function iv.What is
the normal curve probability of exceeding this score?Use "=1-
NORM.S.DIST" functionBHow do you interpret the relationship
between the data sets? What does this suggest about our equal
pay for equal work question?4Based on our sample data set, can
the male and female compa-ratios in the population be equal to
each other?AFirst, we need to determine if these two groups
have equal variances, in order to decide which t-test to
use.What is the data input ranged used for this question:Step
1:Ho:Ha:Step 2:Decision Rule:Step 3:Statistical test:Why?Step
4:Conduct the test - place cell B77 in the output location
box.Step 5:Conclusion and InterpretationWhat is the p-value:Is
the P-value < 0.05 (for a one tail test) or 0.025 (for a two tail
test)?What is your decision: REJ or NOT reject the null?What
does this result say about our question of variance
equality?BAre male and female average compa-ratios
equal?(Regardless of the outcome of the above F-test, assume
equal variances for this test.)What is the data input ranged used
for this question:Step 1:Ho:Ha:Step 2:Decision Rule:Step
3:Statistical test:Why?Step 4:Conduct the test - place cell B109
in the output location box.Step 5:Conclusion and

7. InterpretationWhat is the p-value:Is the P-value < 0.05 (for a
one tail test) or 0.025 (for a two tail test)?What is your
decision: REJ or NOT reject the null?What does your decision
on rejecting the null hypothesis mean?If the null hypothesis
was rejected, calculate the effect size value:If the effect size
was calculated, what doe the result mean in terms of why the
null hypothesis was rejected?What does the result of this test
tell us about our question on salary equality?5Is the Female
average compa-ratio equal to or less than the midpoint value of
1.00?This question is the same as: Does the company, pay its
females - on average - at or below the grade midpoint (which is
considered the market rate)?Suggestion: Use the data column T
to the right for your null hypothesis value.What is the data input
ranged used for this question:Step 1:Ho:Ha:Step 2:Decision
Rule:Step 3:Statistical test:Why?Step 4:Conduct the test - place
cell B162 in the output location box.Step 5:Conclusion and
InterpretationWhat is the p-value:Is the P-value < 0.05 (for a
one tail test) or 0.025 (for a two tail test)?What, besides the p-
value, needs to be considered with a one tail test?Decision:
Reject or do not reject Ho?What does your decision on rejecting
the null hypothesis mean?If the null hypothesis was rejected,
calculate the effect size value:If the effect size was calculated,
what doe the result mean in terms of why the null hypothesis
was rejected?What does the result of this test tell us about our
question on salary equality?6Considering both the salary
information in the lectures and your compa-ratio information,
what conclusions can you reach about equal pay for equal
work?Why - what statistical results support this conclusion?
Week 3Week 3ANOVAThree QuestionsRemember to show how
you got your results in the appropriate cells. For questions
using functions, show the input range when asked.Group
name:G1G2G3G4G5G61One interesting question is are the
average compa-ratios equal across salary ranges of 10K
each.Salary Intervals: 22-2930-3940-4950-5960-6970-79While
compa-ratios remove the impact of grade on salaries, are they
different for different pay levels,Compa-ratio values: that is are

8. people at different levels paid differently relative to the
midpoint? (Put data values at right.)What is the data input
ranged used for this question:Step 1:Ho:Ha:Step 2:Decision
Rule:Step 3:Statistical test:Why?Step 4:Conduct the test - place
cell b16 in the output location box.Step 5:Conclusions and
InterpretationWhat is the p-value?Is P-value < 0.05?What is
your decision: REJ or NOT reject the null?If the null
hypothesis was rejected, what is the effect size value (eta
squared)?If calculated, what does the effect size value tell us
about why the null hypothesis was rejected?What does that
decision mean in terms of our equal pay question?2If the null
hypothesis in question 1 was rejected, which pairs of means
differ?Why?Groups ComparedDiffT+/- TermLowto
HighDifference Significant?Why?G1 G2G1 G3G1 G4G1 G5G1
G6G2 G3G2 G4G2 G5G2 G6G3 G4G3 G5G3 G6G4 G5G4 G6G5
G63Since compa is already a measure of pay for equal work, do
these results impact your conclusion on equal pay for equal
work? Why or why not?
Week 4Regression and CorellationFive QuestionsCompa-
ratioMidpointAgePerformance
RatingServiceRaiseDegreeGenderRemember to show how you
got your results in the appropriate cells. For questions using
functions, show the input range when asked.1Create a
correlation table using Compa-ratio and the other interval level
variables, except for Salary.Suggestion, place data in columns T
- Y.What range was placed in the Correlation input range
box:Place C9 in output box.bWhat are the statistically
significant correlations related to Compa-ratio?T =Significant r
=cAre there any surprises - correlations you though would be
significant and are not, or non significant correlations you
thought would be?dWhy does or does not this information help
answer our equal pay question?2Perform a regression analysis
using compa as the dependent variable and the variables used in
Q1 along withincluding the dummy variables. Show the result,
and interpret your findings by answering the following
questions.Suggestion: Place the dummy variables values to the

9. right of column Y.What range was placed in the Regression
input range box:Note: be sure to include the appropriate
hypothesis statements.Regression hypothesesHo:Ha:Coefficient
hyhpotheses (one to stand for all the separate
variables)Ho:Ha:Place B36 in output box.Interpretation:For the
Regression as a whole:What is the value of the F statistic: What
is the p-value associated with this value: Is the p-value <
0.05?What is your decision: REJ or NOT reject the null?What
does this decision mean? For each of the coefficients:
MidpointAgePerf. Rat.ServiceGenderDegreeWhat is the
coefficient's p-value for each of the variables: Is the p-value <
0.05?Do you reject or not reject each null hypothesis: What are
the coefficients for the significant variables?Using the intercept
coefficient and only the significant variables, what is the
equation?Compa-ratio = Is gender a significant factor in compa-
ratio?Regardless of statistical significance, who gets paid more
with all other things being equal?How do we know? 3What does
regression analysis show us about analyzing complex
measures?4Between the lecture results and your results, what
else would you like to knowbefore answering our question on
equal pay? Why?5Between the lecture results and your results,
what is your answer to the questionof equal pay for equal work
for males and females? Why?