ELearning/Course design/Learner characteristics

There are a number of characteristics that vary widely among our learners. We can say that:

• Activity within the brain's learning networks vary significantly between individuals during learning.
• Students come to the course with widely varying backgrounds, life experiences, world views and attitudes toward learning.
• We all have our strengths and weaknesses, aptitudes, preferences and cognitive strategies.
• We have differing emotional makeup and needs for social support.
• We come to online learning with a wide variety of computer and Internet skills.
• We come to learning with differing levels of subject-matter mastery, from novice to expert.

Know your audience

A fundamental principle of designing instruction is to know your audience. Does this mean you, the course designer, have to account for every possible individual variance for every student? That would be impossible. The best we can do is, first, consider the general makeup of our target learner population and, second, include sufficient variety in our content presentation, engagement with the content and methods of assessment that allow for differences. To narrow learner analysis, concentrate on those characteristics most critical to the achievement of the specific training objectives (Morrison et al., 2011).

Knowing your student population will necessarily begin with a sketchy lack of detail, you begin with general characteristics like age group, family income level, ethnic background, aptitude, educational level, geographical location, grade point average, work position, etc. Note how these characteristics are based in fact and not perception. One of the traps instructional designers, educators, marketers, and retailers succumb to is believing the "hype" of self-described gurus about particular demographic categories.

A perfect example of this is the so-called "net generation" also referred to as "digital natives" - those born after 1980. Bennett et al. (2008) summarize the situation:

"The idea that a new generation of students is entering the education system has excited recent attention among educators and education commentators. These young people are said to have been immersed in technology all their lives, imbuing them with sophisticated technical skills and learning preferences for which traditional education is unprepared. Grand claims are being made about the nature of this generational change and about the urgent necessity for educational reform in response. A sense of impending crisis pervades this debate. However, the actual situation is far from clear."

They go to cite numerous studies in countries around the globe showing that, while ownership of all things digital is very high among this group, the large majority limit their activities to word processing, texting, keeping in touch with "friends," surfing the net, playing games, and watching videos. Only 20% create content of their own through blogs, YouTube, etc.

Watson (2012) further cautions us about depending too heavily on demographic categories: "Although it would be quite efficient to consider our students as homogeneous members of a generation, experience and cultural imperatives dictate that we must see our students as individuals, heterogeneous in their experiences and their abilities. Instead of pursuing the more arduous path of "creating learning environments that develop talent rather than merely select it", which would require that we examine underlying cognitive processes that influence our individual abilities to learn, we opt for the silver bullet."

When all is said and done, many, if not most, subject areas include audiences with significantly varying characteristics. Nonetheless, it is useful to narrow the categories as much as possible. This may come only with experience over the long-term.

There are some learner characteristics we know impact learning and should be included in teaching and instructional design.

Existing knowledge and skills: Learner expertise

Research has proven that learners' prior knowledge of course content exerts the most influence on learning. Learners with little prior knowledge benefit from different instructional methods than learners who are relatively experienced (Clark & Mayer, 2011). This is known as the "expertise reversal effect" (Clark, Nguyen & Sweller, 2006; Kalyuga & Renkl, 2010). Many instructional methods that work for novices either have no effect or actually depress learning for more advanced learners. This is so because of the limits of working memory, and the fact that learners develop mental schema as they advance. Schema are handled as single information bits within working memory, in essence providing instant access to the learner's entire mental representation of the subject (Kalyuga, Chandler & Sweller, 1998).

We know that course level does not necessarily imply proficiency within the particular subject area, so it's a good idea to classify the "average" student you will target.

Levels of Expertise - While expertise is a universal concept, there is no recognized classification system to describe it beyond the trades. Page-Jones (1998) articulated seven stages of expertise in software engineering:

Table 1. Seven stages of expertise for software engineers
Stage	Terms	Definition
1	Naïve	Complete lack of exposure to relevant concepts, etc.
2	Aware, untrained	General conceptual awareness, but no experience.
3	Novice, Student, Trainee	Systematic exposure to concepts and the ability to demonstrate basic mastery.
4	Apprentice	Concept mastery and ability to perform under supervision.
5	Journeyman	Completion of apprenticeship and ability to perform without supervision; source of advice; not qualified to supervise others.
6	Master	Possesses a profound methodological foundation; able to break surface rules in service to fundamental principles; handles the most difficult questions; can train and supervise others.
7	Researcher	Concerned with discovery and delivering the latest developments to a wider audience; identifies failings in current knowledge and practice, searching for improvements and breakthroughs.

Types of learner characteristics

• Demographic profile. Enough information about the typical member of a group to create a mental picture of the hypothetical aggregate. For example, a marketer might speak of the single, female, middle-class, age 18 to 24, college educated demographic.

• Specific entry characteristics. Prerequisite knowledge, skills, attitudes, aptitudes, etc.

• Academic information. Grade or training level completed, grade point average, standardized achievement test scores, specialized and advance courses taken.

• Personal and social characteristics
  • age and maturity level
  • motivation and attitude toward the subject; interest and self-efficacy
  • expectations and vocational aspirations
  • work experience and recency
  • special talents
  • mechanical dexterity
  • ability to work under various environmental conditions such as inclement weather and heights
  • physical characteristics such as body type, physical and learning disabilities, physical fitness, weight, health status

• Cultural characteristics
  • ethnicity
  • diversity
  • avoiding stereotyping
  • language

Tiger Culture "Like other Chinese kids, my son has excelled: black belt in kung fu, award-winning pianist, math whiz, and so on. I'd like to take credit for it, but my main role (as a white father) is just enforcing the intense regimen that his mother lays out for us. Many Americans are shocked by the way Asians excel at math and engineering, and secretly wonder about a math gene or some engineering enzyme in the blood. But like most everything else in ethnic groups, the answer is not biological. The parents just force math, science, and engineering on their kids. The Asian child is not consulted by the tiger parents what the child wants to study, nor is there an attempt to discern what the child might be naturally good at. That inquiry would be akin to asking the leg of a chair if it wanted to pursue a different function. The happiness of the leg is not the goal. The function of the chair (the family, in my little analogy) is the real goal of life." Stephen Asma (2014)

• Child, adolescent, or adult learners

• Technology use and virtual distance

Virtual Distance: Technology is rewriting the rulebook for human interaction A mother walks into a diner joining friends for lunch, carrying her 2-year-old. She sets him down at the table, hands him a tablet device, takes out her smartphone, searches messages, and half listens for only occasional moments of adult conversation squeezed in between swooshes across their collective screens. Virtual distance is a psychological and emotional sense of detachment that accumulates little by little, at the sub-conscious or unconscious level, as people trade-off time interacting with each other for time spent "screen skating" (swiping, swishing, pinching, tapping, and so on). It is also a measurable phenomenon and can cause some surprising effects. For example, when virtual distance is relatively high, people become distrustful of one another. One result: they keep their ideas to themselves instead of sharing them with others in the workplace – a critical exchange that's necessary for taking risks needed for innovation, collaboration and learning. Another unintended consequence: people disengage from helping behaviors – leaving others to fend for themselves causing them to feel isolated, often leading to low job satisfaction and organizational commitment. Virtual distance research underscores that the rules of interaction have changed. It changes the way people feel – about each other, about themselves, and about how they fit into the world around them. But the demonstrated impacts measured among adults seem comparatively benign when considered against what it might be doing to children. Read more in this Phys.org article. Refer to metacognitive activities for an approach to helping young people manage their virtual distance. Karen Sobel Lojeski and Martin Westwell (2015)

Guiding questions

Here are some questions that can help identify characteristics specific to the subject area:

What are the learners' subject competencies?

1. At what levels are the learners' current knowledge and skill in the subject?
2. What background experiences do the learners have in the subject area?
3. Are learners likely to have any major misconceptions in the subject area?

What are the learners' attitudes?

1. What are the general attitudes of the learners toward the instructional content? Are there any subtopics within the content toward which they are likely to feel very positive or very negative?
2. What preferences for instructional format and media do learners have beyond or instead of the differences we have discussed?

At what levels are the learners' language skills?

1. What is the language level of the learners? How much of the specialized terminology is in their vocabularies? Unfamiliar language and complex sentence structure contribute to cognitive overload.
2. What preferences for style of language (e.g., conversational or formal) do the students have? If you are not sure, always use a conversational tone.

What tool skills do they possess?

1. What tools, physical or electronic, must students be able to manipulate, and to what degree (beginner, intermediate, advanced)?
2. Are learners able to handle the online environment without special instruction?
3. Do you have a special learner population that requires special attention?

College students

Time of study

Research has demonstrated that late starts are optimal for most high school students, and a new study extends that analysis to freshmen and sophomores in college (Evans et al., 2017). The study showed that much later starting times of 11 a.m. or noon, result in the best learning. It also revealed that those who saw themselves as "evening" people outnumbered the "morning" people by 2:1, and it concluded that every start time disadvantages one or more of the chronotypes (propensity for the individual to be alert and cognitively active at a particular time during a 24-hour period). The authors concluded that the best practical model may involve three alternative starting times with one afternoon shared session.

"The crux of the matter in the temporal misalignment problem is that biological changes beginning in puberty shift wake and sleep times 2–3 hours later in the day. This shift is at its greatest at age 19 (Roenneberg et al., 2004) before reverting to an earlier pattern in the mid-20s. Oblivious to these changes, secondary schools and universities continue to start classes early in the morning."

Location: Proximity makes a difference

Eighty percent of online students live within 100 miles of the institution they attend (Aslanian & Clinefelter, 2012). For colleges and universities, the vast majority of students are from within the state, with the highest out-of-state coming from neighboring states.

College major

Business was the most popular undergraduate major, while the highest number of master's degrees was awarded in education and the highest number of doctoral degrees in the health professions (Almanac of Higher Education, 2012). All degrees combined:

1. College majors in the U.S.

Major declaration

The Advisory Committee on Student Financial Assistance (2012) reports that students who enroll in a specific program of study early in their college career are more likely to complete their degree and will take less time to graduate, regardless of age.

Student study and work patterns

The National Survey of Student Engagement (2012) queries students from 1,500 colleges and universities each year. Two of the most interesting findings for 2011 are work and study time, shown here for various majors. Green bars represent average weekly work hours and blue bars represent study time. Note the opposing slopes.

2. Average weekly work (•) and study (•) time by college major

Course access patterns

The Indicators Project of Central Queensland University analyzes usage data from learning management systems (LMS) throughout Australia. As such, their sample sizes are large, ranging from 20-90,000+.

• The most significant trend demonstrated in the following graphs is that student hits within the LMS are significantly correlated to student grades. A hit equals one access to one LMS element.

3. Accessing the LMS more often correlates with higher student grades

• Female students access the LMS more frequently than males regardless of grades.

• Older students access the LMS more than younger age groups regardless of grades.

Technology as distraction

College students spend one-fifth of their time in class using digital devices—such as smartphones—for non-educational purposes, new research (McCoy, 2016) reveals.

The main culprit is texting. Almost nine out of 10 reported that texting was their main digital diversion while in class. About three-quarters said they emailed or checked the time on their devices. Seventy percent reported checking in on social media (e.g., Facebook), while 40 percent surfed the web. One in 10 spent time in class playing games on their devices. About one in 10 said they wouldn't be able to stop even if they wanted to, the survey found.

Compared with a 2013 survey, the new poll shows a slight uptick in the regularity with which students are using their devices in class. For example, while just 30 percent said they checked their device a minimum of 10 times a day back in 2013, that figure rose to 33 percent by 2015, the study authors said. In 2013, about 8 percent of students said they never used their devices for non-educational purposes in class. By 2015, that figure dropped to just 3 percent, according to the researchers.

"Young people turn to digital media as an immediate way to relieve boredom and, sadly, the classroom is one of the environments in which they most commonly experience boredom."

Learning styles?

While intuitively appealing, years of solid research tells us that student "learning styles" have no practical application to teaching (Pasher, et. al., 2009; Watson, 2012). Regardless of the popularity of the approach, learning style preferences serve little of value for students and instructors alike. We all learn in a multitude of ways, and it is the combination of styles that is of value. Each approach is useful, but none is exclusively useful. Styles are facets of the whole and they work best in tandem. A potentially valuable use of such inventories is in learning-to-learn instruction, addressed in the Cognitive support article.

Pashler, et. al. conducted an extensive literature search and examined the available evidence on learning styles. Some of their conclusions:

• The definition of learning styles is inchoate, with the nature of dimensions varying widely between approaches.

• There is a paucity of well-designed studies on the matter, and those that were well-designed failed to find any relationship between style preference and learning.

• When asked about their preferences, adults and children do express preferences, but there is no demonstrated correlation between preference and ability to learn using other styles.

• There is no relationship between individually expressed style preference and objectively measured aptitude profiles.

• There is growing evidence that people who manage their own learning based on style preferences do themselves a disservice by failing to take advantage of other learning domains.

"The contrast between the enormous popularity of the learning-styles approach within education and the lack of credible evidence for its utility is, in our opinion, striking and disturbing" (p. 117).

Conclusion

Learners come to courses with a wide range of background characteristics that impact their ability and motivation to learn. While it is impossible to discern those characteristics for every student, instructors and designers do well by identifying the general and most salient features of their learner population, and by using flexible, adaptable designs. This is so because teaching and course management methods can and should be adapted to meet the needs of learners as much as possible.

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