Evaluate 3.1.1 Differentiation Quest

Provide sample data from student results for a course within your field. The data can be entirely theoretical and written out in text form.

Upon completion of the data, discuss the method used to group students based on abilities, strategies utilized to examine student performance, and areas requiring modification of instruction.

Next, focus on the individual level. How could the data be referenced to identify the needs of each student? How could the settings of the LMS be used to create personalized learning paths? Note that a personalized learning path does not necessarily mean one student sees a resource no one else does, but that the overall order and selection of work one student does may be entirely unique from the order and assignments others did. Once the product is complete, share the results in your blog.

For this quest, we are focusing on one assignment from this unit of study, HW4.  This was my mad scientist experiment with my students that involved grouping students by lexile scores and having them read articles related to macroeconomics.  Following the readings, students were put into groups of four to complete the assignment.  Students were given the task of creating a "Wall Street Journal" story board.  While some of the assignments impressed, it was clear that group 7 did not achieve the standards for the assignment.  Sue, Jim, and Tim were given another article with a lower lexile level to see if they would attain an acceptable level of accomplishment.  Based on their preliminary scores, looks like the original article was a bit too difficult for them and has left me rethinking my reliance on our district's lexile evaluation tool.

		Assignments
		HW 1	HW 2	HW 3	HW 4	Exam 1	HW 5	HW 6	HW 7	HW 8	Exam 2	HW 9	HW 10	HW 11	Extra Credit	Final
	Points:	50	60	50	50	150	50	50	50	50	150	50	50	50	0	200	Curve:	0.00%
	Student																Total	%	Grade
1.	Bob	40	50	45	36	140											311	86.4%	B+
2.	Sally	40	41	E	39	125											245	79.0%	C+
3.	Sue	42	42	50	29	75											238	66.1%	D+
4.	Jill	41	46	40	49	65											241	66.9%	D+
5.	Jon	43	48	31	37	125											284	78.9%	C+
6.	Ted	44	49	31	38	148											310	86.1%	B+
7.	Mag	47	45	36	40	132											300	83.3%	B
8.	Jim	45	42	41	26	145											299	83.1%	B
9.	Jan	49	44	32	37	63											225	62.5%	D-
10.	Todd	42	43	37	48	63											233	64.7%	D
11.	Tim	47	48	50	29	120											294	81.7%	B-
12.	Jake	40	41	34	45	130											290	80.6%	B-
13.	Sam	42	43	47	46	127											305	84.7%	B
14.	Betty	41	46	44	35	135											301	83.6%	B
15.	Maria	47	50	25	42	145											309	85.8%	B
16.	Max	46	41	40	40	62											229	63.6%	D
17.	Kate	45	47	46	48	76											262	72.8%	C-
18.	Jake	40	43	34	46	85											248	68.9%	D+
19.	Sam	41	40	27	36	97											241	66.9%	D+
20.	Betty	43	47	43	35	102											270	75.0%	C
21.	Maria	42	46	32	47	115											282	78.3%	C+
22.	Max	44	44	35	28	57											208	57.8%	F
23.	Kate	50	46	35	40	125											296	82.2%	B-
24.	Sam	46	50	E	32	137											265	85.5%	B
25.	Maria	50	50	50	50	147											347	96.4%	A+
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[42]	To add rows, copy an existing row and then insert it above this line.
	Class Avg:	43.9	45.3	38.5	39.1	109.6	-	-	-	-	-	-	-	-	-	-	Mean:	76.8%	C+
	Class Avg %:	87.8%	75.5%	77.0%	78.2%	73.1%
	Median:	86.0%	76.7%	74.0%	78.0%	83.3%											Median:	79.0%
	StDev:	6.4%	5.4%	14.9%	14.1%	21.2%											StDev:	9.7%	[42]