Reforming Undergraduate Statistics Education in the Arab World in the Era of Information

Figures & data

Table 1 Distribution of the undergraduate statistics programs in Arab universities.

Country	Universities	Programs	Respondents	Country	Universities	Programs	Respondents
Egypt	13	18	8	Bahrain${}^{\mathrm{a}}$	1	1	1
Iraq	10	10	6	Jordan	1	1	1
Sudan	6	7	4	Qatar${}^{\mathrm{a}}$	1	1	1
Libya	5	5	1	Tunisia	1	1	1
KSA${}^{\mathrm{a}}$	5	5	5	UAE${}^{\mathrm{a}}$	1	1	1
Algeria	4	4	4	Yemen	1	1	0
Lebanon	3	3	3	Syria	3	3	0
Morocco	2	2	1	Somalia	3	3	0
Palestine	2	2	2	Mauritania	–	–	–
Oman${}^{\mathrm{a}}$	1	3	3	Djibouti	–	–	–
Kuwait${}^{\mathrm{a}}$	1	2	2	Comoro Islands	–	–	–

${}^{\mathrm{a}}$

GCC countries.

Table 2 Characteristics of the undergraduate statistics programs in Arab universities.

Characteristic	Number	Percentage
University type
Public	57	93%
Private	4	7%
Location
Science	41	59%
Business	29	41%
Region
GCC	12	17%
Non-GCC	58	83%
Program title
Statistics/applied statistics	45	64%
Statistics and computer science	12	17%
Statistics and business	9	13%
Mathematics and statistics	4	6%

Table 3 Statistics graduates in five Arab states during 2013–2016.

Country	2013	2014	2015	2016
Egypt	582	256	491	469
Tunisia	78	58	66	53
UAE	24	28	30	18
Oman	38	35	72	56
Qatar	16	15	12	25

Source: Compiled from statistics centers and universities’ websites.

Table 4 Characteristics of the surveyed statistics programs.

Characteristic	Number (%)	Characteristic	Number (%)
Region		Annual number of graduates${}^{\mathrm{a}}$
Non-GCC	32 (73)	Fewer than 10	9 (21)
GCC	12 (27)	10–29	15 (35)
Location		30–49	5 (12)
Business	18 (41)	50+	14 (32)
Science	26 (59)
Language of instruction		Main destination of statistics graduates${}^{\mathrm{a}}$
Arabic	11 (25)	Government	24 (58)
English	27 (61)	Statistics agencies	13 (32)
French	6 (14)	Business/industry	4 (10)

${}^{\mathrm{a}}$

Slight variations in the reported sample size are due to nonresponses by chairs.

Table 5 Topics covered by the surveyed statistics programs.

		Coverage				Coverage
Topic	Region	No/implicit	Partial	Full	College	No/implicit	Partial	Full
Statistical methods and theory	Non-GCC	25%	13%	62%	Business	17%	17%	66%
	GCC	0%	25%	75%	Science	19%	15%	66%
Data manipulation and computation	Non-GCC	25%	28%	47%	Business	22%	28%	50%
	GCC	8%	33%	59%	Science	19%	31%	50%
Mathematical foundations	Non-GCC	31%	16%	53%	Business	33%	22%	45%
	GCC	17%	8%	75%	Science	23%	8%	69%
Practice and communication of statistical findings	Non-GCC	38%	38%	24%	Business	39%	39%	22%
	GCC	0%	33%	67%	Science	19%	35%	46%
Disciplines other than statistics	Non-GCC	38%	24%	38%	Business	44%	6%	50%
	GCC	42%	8%	50%	Science	35%	30%	35%
Big data and data science	Non-GCC	41%	41%	18%	Business	44%	39%	17%
	GCC	42%	50%	8%	Science	39%	46%	15%

Implicit coverage: Informal coverage (in-class discussion, assignment).

Partial coverage: Covered partially in a course (as a chapter or more).

Full coverage: Covered as a separate course.

Table 6 The use of pedagogical approaches in teaching statistics.

		Usage level${}^{\mathrm{a}}$				Usage level
Approach	Region${}^{\mathrm{b}}$	Low	Medium	High	College${}^{\mathrm{c}}$	Low	Medium	High
Use of authentic real-world data	Non-GCC	13%	32%	55%	Business	17%	28%	55%
	GCC	8%	54%	38%	Science	8%	46%	46%
Develop flexible problem-solving skills	Non-GCC	10%	39%	51%	Business	11%	39%	50%
	GCC	8%	15%	77%	Science	8%	27%	65%
Include experience with statistical computing and data-related skills	Non-GCC	7%	19%	74%	Business	5%	17%	78%
	GCC	0%	46%	54%	Science	4%	35%	61%
Encourage synthesis of theory, methods, computation, and applications	Non-GCC	7%	19%	74%	Business	5%	28%	67%
	GCC	0%	31%	69%	Science	4%	19%	77%
Provide opportunities to work in teams	Non-GCC	17%	17%	66%	Business	17%	22%	61%
	GCC	23%	15%	62%	Science	20%	12%	68%
Integrate training in professional conduct and ethics	Non-GCC	16%	42%	42%	Business	11%	61%	28%
	GCC	8%	46%	46%	Science	15%	31%	54%
Incorporate regular assessment to provide authentic feedback	Non-GCC	19%	42%	39%	Business	22%	45%	33%
	GCC	15%	8%	77%	Science	15%	23%	62%

${}^{\mathrm{a}}$

Low (1–2), medium (3), and high (4–5).

${}^{\mathrm{b}}$

Non-GCC (n = 32), GCC (n = 12).

${}^{\mathrm{c}}$

Business (n = 18), Science (n = 26).

Table 7 The perceptions of chairs and employers on the preparation of statistics graduates.

		Quality rating${}^{\mathrm{a}}$				Quality rating
	Region${}^{\mathrm{b}}$	Low	Medium	High	College${}^{\mathrm{c}}$	Low	Medium	High
Faculty perceptions
Effective technical writing	Non-GCC	13%	45%	42%	Business	11%	56%	33%
	GCC	8%	54%	38%	Science	12%	42%	46%
Presentation skills	Non-GCC	10%	42%	48%	Business	17%	28%	55%
	GCC	8%	31%	61%	Science	4%	46%	50%
Data visualization	Non-GCC	6%	29%	65%	Business	11%	28%	61%
	GCC	0%	31%	69%	Science	0%	31%	69%
Teamwork and collaboration	Non-GCC	13%	27%	60%	Business	22%	33%	45%
	GCC	8%	31%	61%	Science	4%	24%	72%
Interaction and communications with Others	Non-GCC	10%	43%	47%	Business	22%	39%	39%
	GCC	15%	39%	46%	Science	4%	44%	52%
Problem solving	Non-GCC	10%	48%	42%	Business	17%	55%	28%
	GCC	23%	46%	31%	Science	12%	42%	46%
Critical thinking	Non-GCC	13%	45%	42%	Business	17%	50%	33%
	GCC	23%	39%	38%	Science	15%	39%	46%
Employers perceptions
Statistical knowledge	Non-GCC	17%	21%	62%	Business	23%	24%	53%
	GCC	0%	9%	91%	Science	4%	13%	83%
Analytical and statistical skills	Non-GCC	10%	17%	73%	Business	12%	23%	65%
	GCC	0%	0%	100%	Science	4%	4%	92%
Overall career preparation	Non-GCC	32%	29%	39%	Business	29%	18%	53%
	GCC	27%	18%	55%	Science	32%	32%	36%

${}^{\mathrm{a}}$

Low (1–2), medium (3), and high (4–5).

${}^{\mathrm{b}}$

Non-GCC (n = 32), GCC (n = 12).

${}^{\mathrm{c}}$

Business (n = 18), Science (n = 26).

Table 8 Curriculum development in the undergraduate statistics programs.

	Non-GCC	GCC
	countries	countries	Business	Science
	(n = 32)	(n = 12)	(n = 18)	(n = 26)
Major changes in the program in the past five years	75%	33%	72%	58%
Drivers of curriculum changes
Labor market needs	66%	75%	78%	62%
Following guidelines from statistical associations	25%	50%	28%	35%
Updating the program structure	63%	75%	78%	58%
Updating the program content	78%	83%	89%	73%
Following university policies	44%	75%	50%	54%
Responding to accreditation requirements	41%	58%	39%	50%
Future plans for curriculum changes	58%	75%	67%	60%

NOTE: Percentages in the table represent the proportions of respondents in each category who nominated each specific driver of curriculum change.

Table 9 The perceptions of the challenges facing the delivery of the statistics programs.

		Level${}^{\mathrm{a}}$				Level
Obstacles	Region${}^{\mathrm{b}}$	Low	Medium	High	College${}^{\mathrm{c}}$	Low	Medium	High
Shortage of qualified faculty	Non-GCC	33%	17%	50%	Business	22%	28%	50%
	GCC	15%	31%	54%	Science	32%	16%	52%
Availability of textbooks	Non-GCC	33%	23%	44%	Business	39%	17%	44%
	GCC	62%	23%	15%	Science	44%	28%	28%
Availability of authentic real data	Non-GCC	40%	13%	47%	Business	33%	11%	56%
	GCC	15%	39%	46%	Science	32%	28%	40%
Availability of statistical packages	Non-GCC	37%	23%	40%	Business	33%	28%	39%
	GCC	46%	15%	39%	Science	44%	16%	40%
Attracting good students	Non-GCC	17%	13%	70%	Business	11%	11%	78%
	GCC	8%	15%	77%	Science	16%	16%	68%
Employability of graduates	Non-GCC	21%	14%	65%	Business	12%	12%	76%
	GCC	25%	17%	58%	Science	29%	17%	54%
Business/industry collaboration	Non-GCC	23%	13%	64%	Business	11%	6%	83%
	GCC	31%	23%	46%	Science	36%	24%	40%
Availability of internship opportunities for students	Non-GCC	33%	7%	60%	Business	22%	11%	67%
	GCC	15%	31%	54%	Science	32%	16%	52%

${}^{\mathrm{a}}$

Low (1–2), medium (3), and high (4–5).

${}^{\mathrm{b}}$

Non-GCC (n = 32), GCC (n = 12).

${}^{\mathrm{c}}$

Business (n = 18), science (n = 26).

Table

Topics	Not covered at all	Implicitly covered	Covered partially in a course (as a chapter or more)	Covered as a separate course
Statistical methods and theory (design and modeling)
Data manipulation and computation
Mathematical foundations
Statistical practice (ability to understand and communicate statistical findings)
Disciplinespecific knowledge (a subject besides statistics)
Big data and data science

Table

Activities	1	2	3	4	5
Emphasize authentic real-world data (nontextbook data)
Develop flexible problem solving skills
Include experience with statistical computing and data-related skills
Encourage synthesis of theory, methods, computation, and applications
Provide opportunities to work in teams
Integrate training in professional conduct and ethics
Incorporate regular assessment to provide authentic feedback

Table

Skills and competencies	1	2	3	4	5
Effective technical writing
Presentation skills
Data visualization
Teamwork and collaboration
Ability to interact with the and communicate with a variety of clients and collaborators
Problem solving
Critical thinking

Table

Skills and competencies	1	2	3	4	5
Statistical knowledge
Analytical and statistical skills
Overall career preparation

Table

Challenges	1	2	3	4	5
Shortage of qualified faculty
Availability of textbooks
Availability of authentic (nontextbook) real data
Availability of statistical packages
Attracting good students
Employability of graduates
Business/industry collaboration
Availability of training/internship opportunities for students